LATERAL RESERVOIR HETEROGENEITIES AND THEIR ...

LATERAL RESERVOIR HETEROGENEITIES AND THEIR IMPACTS ON STRESS

SHADOWING IN THE EAGLE FORD RESERVOIR

by

Ahmed Ali Alrashed

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A thesis submitted to the Faculty and the Board of Trustees of the Colorado School of

Mines in partial fulfillment of the requirements for the degree of Master of Science (Petroleum

Engineering).

Golden, Colorado

Date

Signed:

Ahmed Ali Alrashed

Signed:

Dr. Jennifer Miskimins

Thesis Advisor

Golden, Colorado

Date

Signed:

Dr. Erdal Ozkan

Professor and Head

Department of Petroleum Engineering

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ABSTRACT

Optimizing hydraulic fracture spacing in horizontal wells of unconventional reservoirs

requires investigating the extent of stress shadowing and the influence of rock quality lateral

variations. For that purpose, a base hydraulic fracture model was created for a well in the Eagle

Ford reservoir. Fiber optic distributed acoustic sensing (DAS) data analysis was utilized to find

the individual perforation cluster contribution based on the total proppant placed in each cluster.

The modeled well cluster contribution and production data were matched with actual data.

Reservoir and geomechanical properties for certain fracturing stages of the horizontal

wellbore were altered from the base model to address the effect of rock quality lateral variations.

The sensitized properties include matrix permeability, Poisson’s ratio, Young’s modulus, and

Biot’s coefficient. In response to these changes, the new flowing fracture lengths of the four

simulated stages were calculated and compared to the base model values. It was found that

fracturing stages with a higher matrix permeability of 0.0023 mD, compared to a base case value

of 0.00023 mD, were able to create fractures with larger flowing fracture length by 69%, 68%, and

48% in the heel, middle, and toe clusters, respectively. Increasing Poisson’s ratio from 0.28 to 0.33

caused changes in the flowing fracture lengths by 32%, 41%, and -1.4% in the heel, middle, and

toe clusters, respectively. Compared to a Young’s modulus base case value of 5.5 MMpsi, a 6.5

MMpsi value resulted in decreasing the flowing fracture lengths at rates of -8%, -3%, and -24% in

the heel, middle, and toe clusters, respectively. Decreasing Biot’s coefficient from 0.9 to 0.1

reduced the flowing fracture lengths in the heel, middle, and toe clusters at rates of -44%, -32%,

and -39%, respectively. Overall, the rate of increase in flowing fracture length at the performed

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sensitivity analyses was more pronounced in the heel and middle clusters and less evident in the

toe clusters.

Four scenarios of 57’ (Scenario 1), 76’ (Scenario 2), 100’ (Scenario 3), and 142’ (Scenario

4) spacing between perforation clusters were run to address the effects of stress shadowing.

Simulations showed that the tightest spacing scenario (Scenario 1) yielded the largest fracture

network volume due to the higher number of clusters. However, these created fractures were less

conductive than the ones created with wider spacing scenarios. Scenario 1 average cluster

contributions based on fracture conductivity were 56%, 29%, and 15% for the heel, middle, and

toe clusters, respectively, compared to more uniform contributions of 36%, 28%, and 36% for the

heel, middle, and toe clusters, respectively, in Scenario 4. In terms of production, Scenario 1

forecasted the highest cumulative oil production of 355,000 STB in 30 years compared to 256,000

STB production of Scenario 4. Therefore, the created fracture network volume, which is an

indication of reservoir contact, was more influential on production than fracture conductivity for

the studied case.

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TABLE OF CONTENTS

ABSTRACT ................................................................................................................................... iii

LIST OF FIGURES ..................................................................................................................... viii

LIST OF TABLES ....................................................................................................................... xix

NOMENCLATURE .................................................................................................................... xxi

ACKNOWLEDGMENTS ......................................................................................................... xxiv

DEDICATION ........................................................................................................................... xxvi

CHAPTER 1 INTRODUCTION .................................................................................................... 1

1.1 Motivation of the Study......................................................................................................... 2

1.2 Problem Statement ................................................................................................................ 2

1.3 Research Objectives .............................................................................................................. 2

1.4 Eagle Ford Play Overview .................................................................................................... 3

1.4.1 Geology .......................................................................................................................... 3

1.4.2 Project Focus Area.......................................................................................................... 6

1.4.3 Available Data ................................................................................................................ 6

CHAPTER 2 LITERATURE REVIEW ....................................................................................... 10

2.1 Rock Mechanics Fundamentals ........................................................................................... 10

2.1.1 Stress and Strain ........................................................................................................... 10

2.1.2 Young’s Modulus and Poisson’s Ratio ........................................................................ 11

2.1.3 In-Situ Stresses ............................................................................................................. 13

2.2 Unconventional Reservoirs ................................................................................................. 16

2.3.1 Types of Hydraulic Fractures in Horizontal Wells ....................................................... 17

2.3.2 Hydraulic Fracture Modes ............................................................................................ 18

2.3.3 Hydraulic Fracture Modeling ....................................................................................... 18

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2.3.4 Natural Fractures .......................................................................................................... 21

2.3.5 Design and Treatment Schedule ................................................................................... 22

2.3.6 Hydraulic Fracture Monitoring ..................................................................................... 25

2.3.7 Stress Shadow Phenomenon ......................................................................................... 29

2.3.8 Pressures Associated with Hydraulic Fracturing .......................................................... 33

2.3.9 Engineered Completion ................................................................................................ 36

2.3.10 Spacing Optimization ................................................................................................. 37

CHAPTER 3 METHODOLOGY ................................................................................................. 40

3.1 Base Case Model Development .......................................................................................... 40

3.1.1 Log Processing ............................................................................................................. 41

3.1.2 DFIT and Log Calibration ............................................................................................ 46

3.1.3 Treatments .................................................................................................................... 55

3.1.4 Production History Matching ....................................................................................... 67

3.2 Sensitivity Analyses Creation ............................................................................................. 68

CHAPTER 4 MODEL RESULTS AND DISCUSSION.............................................................. 73

4.1 Parameter Sensitivity Analyses ........................................................................................... 73

4.1.1 Matrix Permeability Sensitivity .................................................................................... 74

4.1.2 Poisson’s Ratio Sensitivity ........................................................................................... 79

4.1.3 Young’s Modulus Sensitivity ....................................................................................... 80

4.1.4 Biot’s Coefficient Sensitivity ....................................................................................... 81

4.1.5 Cluster Spacing Sensitivity ........................................................................................... 82

4.1.6 Fluid and Proppant Type Sensitivity .......................................................................... 112

4.2 Natural Fracture Density ................................................................................................... 114

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS ............................................... 129

5.1 Conclusions ....................................................................................................................... 129

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5.2 Recommendations ............................................................................................................. 133

REFERENCES ........................................................................................................................... 134

APPENDIX A MATCHED TREATING PRESSURE PLOTS ................................................. 142

APPENDIX B PREDICT-KTM INPUT DATA .......................................................................... 156

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LIST OF FIGURES

Figure 1.1 Eagle Ford play location and boundary map (from U.S. Energy Information

Administration 2014). ............................................................................................... 4

Figure 1.2 Eagle Ford stratigraphic column (from Ratcliffe et al. 2012). .................................. 5

Figure 1.3 Relative locations of Wells A, B, and C (only Wells A and C are used in this

research). The cross signs represent the surface locations of the wells. The

horizontal section of the target well (Well A) was drilled in the northwest

direction. ................................................................................................................... 7

Figure 1.4 Eagle Ford lithology (from Breyer et al. 2013). Upper Eagle Ford lithology is

more to the right side of the upper bar in the graph (more calcite than clay),

whereas the lower Eagle Ford lithology is more to the left side (more clay than

calcite). Well A primarily targets the Eagle Ford Marl (between shale and

limestone in the lithology bar). ................................................................................. 8

Figure 1.5 Eagle Ford shale play (Western Gulf Basin) petroleum windows (from EIA

2010). Formation is dipping down from NW to SE direction. ................................. 9

Figure 2.1 Stress and strain relationship showing both elastic and plastic regions (from

Cyberphysics Website 2018). The relationship is linear until the limit of

proportionality point. Once the stress exceeds the yield point, the material

deforms plastically where the strain is permanent (point A returns to point B not

to the original curve if stress is released). The fracture point represents the

maximum strain reached before the material ruptures. ........................................... 12

Figure 2.2 Left: material before applying stress; Right: material after applying stress (from

Fjar et al. 2008). Stress caused the material to shrink parallel to stress direction

and stretch perpendicular to it. Poisson’s ratio represents the negative ratio of transverse strain to longitudinal strain. ................................................................... 13

Figure 2.3 Unconventional resources versus conventional resources (from Cander 2012).

Unconventional reservoirs are characterized with low permeability/viscosity

ratio where either permeability or viscosity need to be altered for them to be

produced commercially. .......................................................................................... 17

Figure 2.4 US lower 48 states shale oil and gas plays (from EIA 2016). ................................ 19

Figure 2.5 Hydraulic fracture types in horizontal wells based on their growth direction

(from EPT International 2015). .............................................................................. 20

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Figure 2.6 Hydraulic fracture modes: (a) Mode I, opening or tensile mode; (b) Mode II,

sliding or in-plane shearing mode; (c) Mode III, tearing or anti-plane shearing

mode (from Kanninen and Popelar 1985). .............................................................. 21

Figure 2.7 PKN versus KGD 2D hydraulic fracture models (from Montgomery and Smith

2010). The PKN model (left) assumes elliptical fracture in wellbore and through

formation. The KGD model (right) assumes rectangular fracture in wellbore and

through formation. .................................................................................................. 22

Figure 2.8 Fracture complexity levels (from Warpinski et al. 2008). Top left: simple planar

fracture; Top right: planar fracture with added complexity such as roughness

and waviness; Bottom left: complex fracture connected with natural fractures;

Bottom right; primary and secondary fractures connected to create a complex

fracture network. ..................................................................................................... 23

Figure 2.9 Proppant conductivity pyramid for different types of proppant (from Gallagher

2011). Top of pyramid represents the highest conductivity (ceramic). Bottom of

pyramid represents the lowest conductivity (sand). ................................................ 26

Figure 2.10 Generic DFIT procedure (from Barree et al. 2015). The black curve represents

the fluid rate that starts low then increases before it ends with a step down until

shut-in. The red curve represents the surface pressure. .......................................... 28

Figure 2.11 DAS/DTS data (from Wheaton et al. 2016). Top: DTS data changes with time

(warmer colors denote higher temperatures); Middle: DAS data changes with

time (warmer colors denote higher acoustic activity); Bottom: treatment plot

showing different curves versus time (black curve represents rate, dark blue

curve represents surface pressure, light blue curve represents bottomhole

pressure, and green curves represents surface and bottomhole proppant

concentrations). ....................................................................................................... 30

Figure 2.12 Effect of stress shadowing in multiple transverse fractures (from Fisher et al.

2004). Top part shows a top view of a hydraulic fracture where fracture length

propagation is limited by stress shadowing. Bottom part shows a side view of a

hydraulic fracture where fracture height is limited by stress shadowing. .............. 32

Figure 2.13 Stress shadow effect minimized by lateral heterogeneity between propagating

fractures (from Manchanda et al. 2016). Left two windows show Young’s modulus effect. Right two windows show Poisson’s ratio effect. .......................... 32

Figure 2.14 Effect of net pressure on fracture spacing (from Morrill and Miskimins 2012).

As fluid net pressure increases, the induced stress shadow increases and so

should the hydraulic fracture spacing required to eliminate stress shadow

effects. ..................................................................................................................... 33

Figure 2.15 Stress shadow effect diminishes in later stages of fracturing (from Daneshy

2017). The reduction in the curve slope implies lower additional induced stress

in the later stages of fracturing. Minimum horizontal stress, maximum

x

horizontal stress, and initial closing fracture width coefficient values shown in

the plot. ................................................................................................................... 34

Figure 2.16 Effect of cluster spacing on fracture propagation due to stress shadow (left: 10m

spacing vs. right: 50m spacing) (from Lu 2016). SDEG means scalar stiffness

degradation variable (SDEG), which is a measure of how damaged the element

is. Outer fractures are dominant in the 10 m spacing case, whereas all fractures

are equal in terms of dominance in the 50 m spacing case. .................................... 38

Figure 2.17 Engineered completion (Track 1 from the top) vs. geometric completion (Track

2 from the top), and their respective quality evaluations and logs (from Ajisafe

et al. 2014). Engineered completion spacing planned based on the below

respective quality logs (e.g. porosity, resistivity, Young’s modulus, etc.). Geometric completion has the fracturing stages equally spaced with no

considerations of any horizontal reservoir quality logs. ......................................... 39

Figure 3.1 Left: dynamic Young’s modulus log showing different curves (YMERESIST: calculated based on resistivity; YMEPHIA: based on average porosity;

YMEGR: based on GR; YMEACT: based on DTC & DTS logs; YMEDTC:

based on DTC log). Right: dynamic Poisson’s ratio log showing different curves (same YME abbreviation meanings apply to PR). ................................................. 44

Figure 3.2 Well C processed logs. Tracks from left to right (Track 1: density, resistivity,

effective porosity, and GR; Track 2: static Young’s modulus, process zone stress, Poisson’s ratio, and permeability; Track 3: total stress, pore pressure, and caliper; Track 4: lithology volumes). Top Eagle Ford (primary target and zone

where Well A is placed) and bottom Eagle Ford (secondary target) zones are

highlighted. ............................................................................................................. 47

Figure 3.3 Geosteering to convert logs from Well C to Well A. GR signatures of Well C

vertical and Well A horizontal logs overlap as indicated in the top left window

of the figure. ............................................................................................................ 48

Figure 3.4 DFIT rate and pressure data plot showing the fracture extension and falloff

periods. .................................................................................................................... 49

Figure 3.5 ISIP pick from DFIT data. The regression line was extrapolated to the shut-in

time to pick ISIP at 4545 psi and eliminate the toe tortuosity effects. ................... 49

Figure 3.6 Well A DFIT G-function plot showing the bottomhole pressure, pressure first

derivative (dP/dG), and pressure semilog derivative (GdP/dG) curves versus G

time. The closure pressure was picked to be 9563 psi at G=35.726. ...................... 51

Figure 3.7 Well A DFIT square-root of time plot showing the bottomhole pressure,

pressure first derivative (dP/d(dt)2), and pressure semilog derivative ((dt)2

dP/d(dt)2) curves versus (dt)2. The closure pressure was picked to be 9563 psi at

(dt)2=44.282 min2.................................................................................................... 52

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Figure 3.8 Well A DFIT log-log plot showing dP, its first derivative (d(dP)/d(dt)), and its

pressure semilog derivative (dt d(dP)/d(dt)) curves versus (dt). The closure

pressure was picked to be 9563 psi at (dt)=1960.905 min...................................... 53

Figure 3.9 After-closure linear analysis plot of bottomhole pressure versus time showing

pore pressure determination at 9434 psi ................................................................. 54

Figure 3.10 Fissure leakoff analysis plot of leakoff ratio versus bottomhole pressure

showing leakoff coefficient determination at 0.0039 1/psi. .................................... 55

Figure 3.11 Well A with actual perforation locations shown as green dots. A total of 14

fracturing stages (64 perforation clusters) were treated. ......................................... 57

Figure 3.12 Stage 11 treatment data with a matched pressure between model and actual

values. Dotted pressure curve represents the actual surface treating pressure

(surface pressure in the legend), whereas the connected pressure curve

represents the model surface treating pressure (well pressure in the legend). ........ 58

Figure 3.13 Total proppant displaced at each perforation cluster of the 14 stages calculated

from DAS data (from OptaSense 2015). Each color denotes a perforation cluster

(e.g. red color represents Cluster 2 in all stages). Cluster 1 in any stage is the

deepest at that stage (i.e. closest to toe) and is the far left in the plot. .................... 59

Figure 3.14 Modeled individual cluster contribution to production based on modeled

proppant concentration. Error difference between the model and actual values

in each cluster is represented by the black curve. ................................................... 60

Figure 3.15 Transverse view of the proppant concentration grid for Cluster 5 (heel cluster)

in Stage 11. Formation tops and lithology are shown on the left whereas, the

grid scale is shown on the right. ............................................................................. 61

Figure 3.16 Transverse view of the proppant concentration grid for Cluster 4 (middle

cluster) in Stage 11. Formation tops and lithology are shown on the left, whereas

the grid scale is shown on the right. ........................................................................ 62







Figure 3.19 Transverse view of the proppant concentration grid for Cluster 1 (toe cluster) in

Stage 11. Formation tops and lithology are shown on the left, whereas the grid

scale is shown on the right. ..................................................................................... 65

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Figure 3.20 A top view of the entire wellbore of Well A showing the created fracture planes

in all 14 stages. The shown property grid is proppant concentration in lb/ ft2

(scale on the right). ................................................................................................. 66

Figure 3.21 A side view of the entire wellbore of Well A showing the created fracture planes

in all 14 stages. The shown property grid is proppant concentration in lb/ft2

(scale on the right). ................................................................................................. 67

Figure 3.22 Type curve plot showing the model dimensionless pressure and its derivative

establish a good fit with actual data. ....................................................................... 69

Figure 3.23 A fitted pseudo pressure plot of dp/q versus time showing the representative

estimated ultimate recovery (EUR), drainage area, and aspect ratio. ..................... 70

Figure 3.24 A fitted semi-log plot of dp/q versus time showing the representative

permeability, transmissivity, skin, and fracture half-length. .................................. 71

Figure 3.25 Model production history matched with actual production. The matched curves

include bottomhole pressure, oil rate, water rate, cumulative oil production, and

cumulative water production. Matching was established for the available

production data of around 470 days. ....................................................................... 72

Figure 4.1 Matrix Permeability grid showing how Stages 3, 7, 9, and 13 permeability

values are different from the rest of the wellbore stages. Case 5 (0.0023 mD

permeability) is used here as an example. .............................................................. 75

Figure 4.2 Stage 3 matrix permeability sensitivity plot showing the change in flowing

fracture length as permeability changes. Each colored curve represents a

perforation cluster in Stage 3 with Cluster 3.1 being the closest to the toe and

Cluster 3.5 being the closest to the heel. Black ellipse represents the base case

matrix permeability (0.00023 mD). ........................................................................ 76








perforation Cluster in Stage 9 with Cluster 9.1 being the closest to the toe and






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Figure 4.6 Poisson’s ratio grid showing how Stages 3, 7, 9, and 13 PR values are different from the rest of the wellbore stages. Case 2 (0.2 Poisson’s ratio) is used here as an example. ............................................................................................................. 83

Figure 4.7 Stage 3 Poisson’s ratio sensitivity plot showing the change in flowing fracture length as Poisson’s ratio changes. Each colored curve represents a perforation cluster in Stage 3 with Cluster 3.1 being the closest to the toe and Cluster 3.5

being the closest to the heel. Black ellipse represents the base case Poisson’s ratio (0.28). ............................................................................................................. 84

Figure 4.8 Stage 7 Poisson’s ratio sensitivity plot showing the change in flowing fracture

length as Poisson’s ratio changes. Each colored curve represents a perforation cluster in Stage 7 with Cluster 7.1 being the closest to the toe and Cluster 7.5


Figure 4.9 Stage 9 Poisson’s ratio sensitivity plot showing the change in flowing fracture length as Poisson’s ratio changes. Each colored curve represents a perforation

cluster in Stage 9 with Cluster 9.1 being the closest to the toe and Cluster 9.4


Figure 4.10 Stage 13 Poisson’s ratio sensitivity plot showing the change in flowing fracture length as Poisson’s ratio changes. Each colored curve represents a perforation cluster in Stage 13 with Cluster 13.1 being the closest to the toe and Cluster 13.5


Figure 4.11 Young’s modulus grid showing how Stages 3, 7, 9, and 13 YM values are different from the rest of the wellbore stages. Case 1 (2.5 MMpsi Young’s modulus) is used here as an example. ..................................................................... 88

Figure 4.12 Stage 3 Young’s modulus sensitivity plot showing the change in flowing fracture length as Young’s modulus changes. Each colored curve represents a perforation cluster in Stage 3 with Cluster 3.1 being the closest to the toe and


Young’s modulus (5.5 MMpsi). ............................................................................. 89

Figure 4.13 Stage 7 Young’s modulus sensitivity plot showing the change in flowing fracture length as Young’s modulus changes. Each colored curve represents a perforation cluster in Stage 7 with Cluster 7.1 being the closest to the toe and



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Figure 4.14 Stage 9 Young’s modulus sensitivity plot showing the change in flowing

fracture length as Young’s modulus changes. Each colored curve represents a perforation cluster in Stage 9 with Cluster 9.1 being the closest to the toe and



Figure 4.15 Stage 13 Young’s modulus sensitivity plot showing the change in flowing fracture length as Young’s modulus changes. Each colored curve represents a




Figure 4.16 Biot’s coefficient grid showing how Stages 3, 7, 9, and 13 Biot’s coefficient values are different from the rest of the wellbore stages. Case 4 (0.7 Biot’s coefficient) is used here as an example. ................................................................. 93

Figure 4.17 Stage 3 Biot’s coefficient sensitivity plot showing the change in flowing fracture length as Biot’s coefficient changes. Each colored curve represents a perforation cluster in Stage 3 with Cluster 3.1 being the closest to the toe and Cluster 3.5

being the closest to the heel. Black ellipse represents the base case Biot’s coefficient (0.9). ...................................................................................................... 94

Figure 4.18 Stage 7 Biot’s coefficient sensitivity plot showing the change in flowing fracture

length as Biot’s coefficient changes. Each colored curve represents a perforation cluster in Stage 7 with Cluster 7.1 being the closest to the toe and Cluster 7.5


Figure 4.19 Stage 9 Biot’s coefficient sensitivity plot showing the change in flowing fracture length as Biot’s coefficient changes. Each colored curve represents a perforation

cluster in Stage 9 with Cluster 9.1 being the closest to the toe and Cluster 9.4


Figure 4.20 Stage 13 Biot’s coefficient sensitivity plot showing the change in flowing fracture length as Biot’s coefficient changes. Each colored curve represents a perforation cluster in Stage 13 with Cluster 13.1 being the closest to the toe and


Biot’s coefficient (0.9). ........................................................................................... 97

Figure 4.21 Well A trajectory plot showing 84 perforation clusters (green dots) with a cluster

spacing of 57 ft. This plot represents Scenario 1 from Table 4.5. .......................... 98

Figure 4.22 Well A trajectory plot showing 64 perforation clusters (green dots) with an

average cluster spacing of 76 ft. This plot represents Scenario 2 from Table 4.5

(actual treatment scenario). ..................................................................................... 99

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spacing of 100 ft. This plot represents Scenario 3 from Table 4.5. ...................... 100


spacing of 142 ft. This plot represents Scenario 4 from Table 4.5. ...................... 101

Figure 4.25 Change in fracture conductivity (left axis) and fracture network volume (right

axis) as a function of changing cluster spacing. The base scenario of 76 ft cluster

spacing is highlighted by the black ellipse. Fracture network volume, in the

figure, is defined as the flowing fracture length multiplied by fracture height and

average fracture width. ......................................................................................... 104

Figure 4.26 Change in total proppant placed (left axis) and fracture network volume (right

axis) as a function of changing cluster spacing. The base scenario of 76 ft cluster

spacing is highlighted by the black ellipse. Fracture network volume, in the

figure, is defined as the flowing fracture length multiplied by fracture height and

average fracture width. ......................................................................................... 105

Figure 4.27 Average fracture conductivity distribution between the heel, middle, and toe

clusters of all 14 fracturing stages in all simulated spacing scenarios. ................. 106

Figure 4.28 Maximum fracture conductivity distribution between the heel, middle, and toe


Figure 4.29 Minimum fracture conductivity distribution between the heel, middle, and toe


Figure 4.30 Standard deviation fracture conductivity distribution between the heel, middle,

and toe clusters of all 14 fracturing stages in all simulated spacing scenarios. .... 109

Figure 4.31 Forecasted oil production rate for the different cluster spacing scenarios for 30

years. The actual production rate is represented by the green curve and shown

for the available data period of 470 days. ............................................................. 110

Figure 4.32 A zoomed-in plot of the forecasted oil production rate for the different cluster

spacing scenarios for 30 years. The actual production rate is represented by the

green curve and shown for the available data period of 470 days. ....................... 111

Figure 4.33 Forecasted cumulative oil production for the different cluster spacing scenarios

for 30 years. The actual cumulative production is represented by the green curve

and shown for the available data period of 470 days overlapping the 32

contributing fractures (base scenario) curve. ........................................................ 112

Figure 4.34 Proppant concentration grid for Cluster 5 of Stage 3. The simulated fracturing

fluid type is 2% KCl (base case). .......................................................................... 116


fluid type is 50# CMHPG-Zr. ............................................................................... 117

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fluid type is 45# Guar-Borate 2. ........................................................................... 118

Figure 4.37 Forecasted cumulative oil production for different fracturing fluid types for 30

years. The current production was matched as seen in the 2% KCl curve during

470 days. ............................................................................................................... 119

Figure 4.38 Proppant concentration grid for Cluster 5 of Stage 3. The simulated proppant

type is Sand A 30/50 (base case). ......................................................................... 120


type is Sand B 30/50. ............................................................................................ 121


type is Ceramic A 30/50. ...................................................................................... 122


type is Ceramic B 30/50. ....................................................................................... 123

Figure 4.42 Dynamic proppant conductivity for the simulated proppant types plotted against

formation stress. Eagle Ford stress is pointed in the figure. ................................. 124

Figure 4.43 Forecasted cumulative oil production for different proppant types for 30 years.

The current production was matched as seen in Sand A curve during 470 days. . 125

Figure 4.44 Natural fracture density plotted versus Well A measured depth. The shaded

yellow boxes refer to the location of the 14 stages whereas the red dots represent

the depths of the individual perforation clusters. .................................................. 126

Figure 4.45 DTS data recorded during hydraulic fracturing for Stages 1-7 indicating

communication between some stages (modified from OptaSense 2015). The x-

axis represents time, whereas the y-axis represents the measured depth. The

inter-stage communication is represented by the red squares. Clusters 3 of Stage

4 and Cluster 4 of Stage 6 are highlighted. ........................................................... 127

Figure 4.46 DTS data recorded during hydraulic fracturing for Stages 8-14 indicating

communication between some stages (modified from OptaSense 2015). The x-

axis represents time, whereas the y-axis represents the measured depth. The

inter-stage communication is represented by the red squares. .............................. 128

Figure A.1 Base case Stage 1 treatment data with a matched pressure between model and

actual values. Dotted pressure curve represents the actual surface treating

pressure (surface pressure in the plot legend), whereas the connected pressure

curve represents the model surface treating pressure (well pressure in the plot

legend). ................................................................................................................. 142



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legend). ................................................................................................................. 143





legend). ................................................................................................................. 144





legend). ................................................................................................................. 145





legend). ................................................................................................................. 146





legend). ................................................................................................................. 147





legend). ................................................................................................................. 148





legend). ................................................................................................................. 149





legend). ................................................................................................................. 150



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legend). ................................................................................................................. 151





legend). ................................................................................................................. 152





legend). ................................................................................................................. 153





legend). ................................................................................................................. 154





legend). ................................................................................................................. 155

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LIST OF TABLES

Table 1.1 Project Main Data ..................................................................................................... 9

Table 2.1 Faulting Regimes Based on Stress State (modified from Golombek 1985) ........... 15

Table 2.2 Permeability-based Options for Fracturing Gas Wells (modified from

Economides and Martin 2007) ................................................................................ 18

Table 2.3 Water and Oil Based Fracturing Fluids (modified from Miskimins 2017) ............ 26

Table 2.4 Fracturing Fluid Additives (modified from Abass 2016) ....................................... 27

Table 2.5 Pressure terms associated with hydraulic fracturing (modified from Economides

and Martin 2007; Miskimins 2017) ........................................................................ 34

Table 3.1 DFIT Results Showing the Main Reservoir Parameters ......................................... 50

Table 3.2 Representative Treatment Schedule of Performed Treatments in Well A .............. 56

Table 3.3 Created Sensitivity Runs ......................................................................................... 68

Table 4.1 Matrix Permeability Sensitivity Cases .................................................................... 76

Table 4.2 Poisson’s Ratio Sensitivity Cases ........................................................................... 80

Table 4.3 Young’s Modulus Sensitivity Cases ....................................................................... 81

Table 4.4 Biot’s coefficient Sensitivity Cases ........................................................................ 82

Table 4.5 Cluster Spacing (Number of Clusters) Sensitivity Scenarios ................................. 82

Table 4.6 Cluster Spacing (Number of Contributing Clusters) Sensitivity Scenarios .......... 105

Table 4.7 Fracturing Fluid Type Sensitivity Cases ............................................................... 114

Table 4.8 Proppant Type Sensitivity Cases .......................................................................... 114

Table B.1 Reservoir Properties Input into Predict-KTM Production Model .......................... 156

Table B.2 Well Properties Input into Predict-KTM Production Model .................................. 156

Table B.3 Fluid Properties Input into Predict-KTM Production Model .................................. 157

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Table B.4 Fracture Properties and Model Parameters Input into Predict-KTM Production

Model .................................................................................................................... 157

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NOMENCLATURE

= area, in2, [L]

= acceleration due to gravity, in/s2, [L][�]−

= fracture width, ft, [L] = bottomhole treating pressure, psi, [M][L]− [�]−

= dimensionless fracture conductivity, dimensionless

= critical fissure opening pressure, psi, [M][L]− [�]−

= coefficient of discharge, dimensionless

= perforation diameter, in, [L] = compressional travel time, µsec/ft, [�][L]−

= shear travel time, µsec/ft, [�][L]−

= Young’s modulus, psi, [M][L]− [�]−

= dynamic Young’s modulus, MMpsi, [M][L]− [�]−

= force, lbs, [M][L][�]−

= G-time function, dimensionless � = dimensionless time function, dimensionless � = � at shut in time, dimensionless

= instantaneous shut in pressure, psi, [M][L]− [�]−

= matrix permeability, mD, [L]

= permeability exponent, dimensionless

= formation permeability, mD, [L]

= fracture permeability, mD, [L]

= permeability multiplier, mD, [L] ∆ = deformation (new length – original length), ft, [L] = fracture half-length, ft, [L]

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= original length, ft, [L] = number of perforations, dimensionless

= fracture closure pressure (min horizontal stress), psi, [M][L]− [�]−

ℎ = hydrostatic pressure, psi, [M][L]− [�]− ∆ = near wellbore pressure loss, psi, [M][L]− [�]−

= reservoir pore pressure, psi, [M][L]− [�]− ∆ = friction loss across the perforations, psi, [M][L]− [�]−

= fluid friction pressure inside the pipe, psi, [M][L]− [�]− ∆ = pressure loss due to tortuosity, psi, [M][L]− [�]−

= process zone stress, psi, [M][L]− [�]−

= total flow rate, bbl/min, [L] [�]−

= square of shear to compressional travel time ratio, dimensionless � = elapsed time, minutes, [�] ∆� = dimensionless pumping time, dimensionless � = total pumping time, minutes, [�] = true vertical depth, ft, [L]

ℎ = shale volume fraction, dimensionless

= wellhead pressure, psi, [M][L]− [�]−

= depth, in, [L] �= Biot’s coefficient, dimensionless �ℎ= horizontal Biot’s coefficient, dimensionless � = vertical Biot’s coefficient, dimensionless � =strain, dimensionless � =longitudinal strain, dimensionless � =transverse strain, dimensionless � = strain at the x direction or regional horizontal strain, dimensionless � = Poisson’s ratio, dimensionless � = dynamic Poisson’s ratio, dimensionless

xxiii

�= overlying rock density, lbm/in3, [M][L]− � =log measurement of formation bulk density (RHOB), g/cm3, [M][L]− � = fluid density, lb/gal or gm/ cm3, [M][L]− � = matrix grain density, g/cm3, [M][L]− � = stress, psi, [M][L]− [�]− � = effective stress, psi, [M][L]− [�]− ��= max horizontal stress, psi, [M][L]− [�]− �ℎ= min horizontal stress, psi, [M][L]− [�]− � = horizontal tectonic stress, psi, [M][L]− [�]− � = overburden stress (vertical stress), psi, [M][L]− [�]− � = stress at the x direction, psi, [M][L]− [�]− � = stress at the y direction perpendicular to x and z directions, psi, [M][L]− [�]− � = stress at the z direction perpendicular to x and y directions, psi, [M][L]− [�]− ∅ = total porosity, dimensionless ∅ = density-derived porosity, dimensionless ∅ = effective porosity, dimensionless

xxiv

ACKNOWLEDGMENTS

I would like to thank God for granting me the health, the right conditions, and the right

time to pursue my Masters of Science degree at the right place, Colorado School of Mines.

Foremost, I would like to thank my advisor Dr. Jennifer Miskimins for her tremendous support

throughout the past year. She accepted to advise me despite the many students she was advising at

the time I asked her. She was always offering support by all means through daily office hours and

emails since the first day I started working with her. She also was responsive to emails immediately

even during the weekends. Dr. Miskimins was very kind that I did not hesitate to ask her any

question even the easy one. This research would not have been completed without her guidance

and continuous support until the end. Because of her support and motivating words, I believed

more in myself and gained more confidence to complete this project. I cannot thank Dr. Miskimins

enough and I was truly lucky to be mentored by her.

I would like to thank my committee members Dr. Azra Tutuncu and Dr. Mansur Ermila

who always offered the support and guidance whenever needed. They were helpful and

cooperative during the whole journey starting from the research proposal to recently scheduling

my thesis defense presentation. I also would like to thank Dr. Ali Tura, the director of the RCP

Consortium, who gave me the opportunity to work with the project data and always provided the

needed support and feedback during RCP weekly meetings. Thanks are also extended to my ex-

advisor Dr. Hazim Abass who supported me during my first year at Colorado School of Mines and

made that year an easier one.

I would like to thank my sponsor employer Saudi Aramco for their support in all matters

during my overseas journey. I also would like to thank the Petroleum Engineering Department

xxv

faculty and staff here at Colorado School of Mines for their continuous help. Special thanks go to

Denise Winn-Bower for her support in all administrative and logistical matters. Thanks are

extended to my colleagues in both consortia, FAST and RCP, for offering the help whenever

needed.

Last but not least, I want to thank my parents, my wife, the whole family, and friends for

their endless support at all times. I could not have made it to this point without them.

xxvi

To my beloved family

1

CHAPTER 1

INTRODUCTION

Horizontal wells with multistage hydraulic fracturing represent the most common and

effective completion technique to produce unconventional shale reservoirs. However, generating

a connective fracture network is a challenging task, especially in shale formations. One of the

recurring challenges is the unbalanced contribution of multiple hydraulic fracturing stages to

production. This is applicable to different hydraulic fracturing stages not contributing evenly to

production or different perforation clusters within a single stage not contributing equally to the

stage production. Significant literature attributes this unequal contribution mainly to the stress

shadow phenomenon.

Stress shadow, the additional stress induced by creating a hydraulic fracture, affects

adjacent hydraulic fractures in way that hinders fracture propagation. The stress shadow is

concentrated on the fracture and its magnitude diminishes further from it. Therefore, minimizing

the stress shadow effect requires further spacing between hydraulic fractures (Fisher et al. 2004;

Ingram et al. 2014). Nonetheless, excessively increasing the spacing may result in keeping portions

of the reservoir unstimulated. Another consideration of fracture stage placement is the rock quality

variations along the lateral (Manchanda et al. 2016). Unconventional shales are characterized with

lateral heterogeneity in reservoir properties, which affects stimulation and production results.

Therefore, finding the optimum hydraulic fracture spacing that aims to eliminate the stress shadow

effect and ensure placing hydraulic fractures in the best quality reservoir rock is of interest to the

industry.

2

1.1 Motivation of the Study

The uneven contribution of multiple transverse hydraulic fractures to production is

discussed broadly in the literature. As most of the research attributes this behavior to the stress

shadow phenomenon and attempts to unravel it by optimizing hydraulic fracture spacing, few

articles examine the effect that reservoir quality lateral variations may have. This paper aims to

undertake both factors of stress shadowing and reservoir lateral variations and incorporate them to

optimize hydraulic fracture spacing in unconventional reservoirs.

1.2 Problem Statement

To address non-uniform cluster/stage contribution to production, two considerations are

studied which are stress shadow effects and reservoir lateral heterogeneity effects. The stress

shadow problem is approached by running different cluster spacing scenarios. Fracture network

volume and fracture conductivity are used to evaluate the performance of each scenario. On the

other hand, the reservoir lateral heterogeneity problem is addressed by running sensitivity analyses

on different reservoir and geomechanical parameters. Consequently, the change in flowing fracture

length and other parameters are documented.

1.3 Research Objectives

This research aims to provide a better understanding of the causes behind the uneven

contribution of multiple transverse hydraulic fractures to production through:

Optimizing hydraulic fracture spacing by considering stress shadowing and reservoir

lateral heterogeneity effects;

Studying how different parameters can play a role in altering the reservoir quality along

the horizontal wellbore;

3

Determining the effects of running engineered completions (horizontal logs that

characterize the reservoir lateral variations); and,

Correlating the perforation cluster spacing to created optimum fracture network volume

and fracture conductivity.

1.4 Eagle Ford Play Overview

The Eagle Ford is one of the top active oil and gas shale plays in the United States and is

the focus area of this study. The name is derived from the town of Eagle Ford in Texas, the home

where this shale outcrop formed (Eagle Ford Shale Overview 2017). Located in Southern Texas,

the play covers an area that is 50 miles wide and 400 miles long and extends from the Texas-

Mexico border southwest to above the San Marcos Arch northeast, as demonstrated in Figure 1.1.

The first discovery well in the Eagle Ford, drilled by Petrohawk Energy, dates back to 2008 in La

Salle County, Texas (Universal Royalty Company 2013). Three years later, the Eagle Ford was

considered one of the most active shale plays in the world with promptly increasing drilling

activities (Institute for Energy Research 2012). January 2018 reports showed that Eagle Ford shale

was producing at an average of more than 800,000 bbls per day during the year of 2017 (Texas

Railroad Commission 2018).

Eagle Ford depths vary from 1500 ft to 14000 ft TVD with formation thicknesses between

50 ft and 330 ft (Martin et al. 2011). The Eagle Ford formation has its highest thickness in the

Maverick Basin (southwest) and thins towards the San Marcos arch (northeast) (Hentz and Ruppel

2010).

1.4.1 Geology

The Eagle Ford formation is a sedimentary rock formation formed around 90 million years

ago in the late Cretaceous (Cenomanian-Turonian) age. The formation was deposited in a marine

4

Figure 1.1 Eagle Ford play location and boundary map (from U.S. Energy Information

Administration 2014).

continental shelf environment. Figure 1.2 shows the stratigraphic column of the Eagle Ford

formation. The Eagle Ford formation lies unconformably beneath the Austin Chalk formation and

is underlain by the Buda Limestone formation (Robison 1997). The Eagle Ford acts as the source

rock for the Austin Chalk reservoir in East Texas, whereas it is considered as an unconventional

reservoir target in South Texas. The Eagle Ford formation consists primarily of two units. The

lower Eagle Ford is a dark gray mudstone, whereas the upper section is a mixture of light and dark

gray mudstone with limestones, shales, and carbonaceous siltstones. The upper Eagle Ford was

deposited in a regressive sequence (low sea level and well-oxygenated environment), unlike the

5

lower Eagle Ford that was deposited in a transgressive sequence (high sea level and low-

oxygenated environment) (Condon and Dyman 2006) (U.S. EIA 2014).

The mineralogical composition of the Eagle Ford formation includes mainly carbonate (40-

90%), clay (15-30%), and quartz (15-20%) (U.S. EIA 2014). TOC values range from 2-9% with

an average value of 3.43%. Produced Eagle Ford oil chemistry indicates that the organic matter

type is predominantly Type II oil prone kerogen (Rahman et al. 2017).

Figure 1.2 Eagle Ford stratigraphic column (from Ratcliffe et al. 2012).

6

1.4.2 Project Focus Area

Two wells, Well A and Well C, are used in this project. These wells were drilled in Lavaca

County, Texas. Well A targets the upper Eagle Ford/lower Austin Chalk formation, whereas the

horizontal section of Well C targets the lower Eagle Ford formation. The pilot hole of Well C

deepens down to the Del Rio formation. It is worth noting that the target for Well A can be

considered either the Lower Austin Chalk or the Upper Eagle Ford Marl, as it is hard to

differentiate between the two formations in this area due to the unconformities. Figure 1.3 indicates

the relative locations of the wells used in this study. In the subject area, the upper Eagle Ford is a

thin section that is dominated with marls interbedded with limestone. On the other hand, the lower

Eagle Ford is more shale-rich than the upper section (Tian et al. 2013). Figure 1.4 demonstrates

the Eagle Ford lithology system.

The difference in elevation across the Eagle Ford play from northwest to southeast causes

a change in the hydrocarbon fluid type. Deeper wells in the southeast tend to have higher GOR

ratios than the wells in the northwest. As illustrated in Figure 1.5, the project area is located in the

oil to wet gas/condensate window. The GOR in the subject area is around 4000 scf/bbl.

1.4.3 Available Data

A full set of data was provided for this project by the Eagle Ford Team of the Reservoir

Characterization Project (RCP) Consortium. The data set includes microseismic, well logs,

completion, stimulation, production, and fiber-optic data. Table 1.1 shows the main data used in

the project for the two wells. Well A was used as the treatment well for which all sensitivities have

been performed. On the other hand, Well C pilot hole logs were used as a reference to generate the

geologic and geomechanic model for Well A. More details of the log calibration between the two

wells are provided in Chapter 3.

7

Figure 1.3 Relative locations of Wells A, B, and C (only Wells A and C are used in this research).

The cross signs represent the surface locations of the wells. The horizontal section of the target

well (Well A) was drilled in the northwest direction.

8

Figure 1.4 Eagle Ford lithology (from Breyer et al. 2013). Upper Eagle Ford lithology is more to

the right side of the upper bar in the graph (more calcite than clay), whereas the lower Eagle Ford

lithology is more to the left side (more clay than calcite). Well A primarily targets the Eagle Ford

Marl (between shale and limestone in the lithology bar).

9

Figure 1.5 Eagle Ford shale play (Western Gulf Basin) petroleum windows (from EIA 2010).

Formation is dipping down from NW to SE direction.

Table 1.1 Project Main Data

Well A data Well C pilot hole data

MWD logs MWD logs

Wireline logs Wireline logs

Mud logs Mud logs

Image logs Formation tops

Wellbore survey Wellbore survey

Treatment details of all 14 stages

Daily production

Fiber-optic DAS/DTS data (hydraulic

fracturing and production)

10

CHAPTER 2

LITERATURE REVIEW

This chapter focuses on the main concepts related to this research including rock

mechanics, unconventional reservoirs, and hydraulic fracturing.

2.1 Rock Mechanics Fundamentals

Rock mechanics or geomechanics is not a single field that is studied separately from other

fields of the petroleum engineering. It is rather associated with the whole process of petroleum

field development including exploration, drilling and completion, stimulation, EOR, and

production. Tutuncu (2016) defined geomechanics as “studying the response of rocks and fluids

to different factors through applying physics, solid mechanics, and mathematics”. This section

discusses the basic principles of geomechanics with a focus on the aspects related to hydraulic

fracturing.

2.1.1 Stress and Strain

Stress is defined as the force (F) applied to a surface of a cross sectional area (A), and

calculated as per Equation 2.1 (Aadnoy and Looyeh 2011). The stress component acting

perpendicular to the surface is known as the normal stress, whereas the shear stress is the

component acting parallel to the surface. If stress was the action, the reaction from the rock to that

applied stress is known as strain. Strain is defined as the resulting deformation (change in length)

divided by the original length before applying the stress and can be calculated using Equation 2.2

(Aadnoy and Looyeh 2011). The applied stress can be in the form of tension or compression where

each form results in a different strain response. Elongation of the rock is caused by tensile stress

11

whereas compressive stress causes rock shortening. The relationship between stress and strain

showing both elastic and plastic regions is demonstrated in Figure 2.1 (Cyberphysics Website

2018).

� = � (2.1)

� = ∆0 (2.2)

Where, � = stress, psi, [M][L]− [�]−

= force, lbs, [M][L][�]−

= area, in2, [L] � =strain, dimensionless ∆ = deformation (new length – original length), ft, [L] = original length, ft, [L]

2.1.2 Young’s Modulus and Poisson’s Ratio

Young’s modulus and Poisson’s ratio are two important elastic parameters that are used in

hydraulic fracture design. Young’s modulus or modulus of elasticity, E, is a material property that

indicates its stiffness. The relationship between Young’s modulus, stress, and strain is governed

by Hooke’s law as presented in Equation 2.3 (Fjar et al. 2008). This relationship represents the

slope in the stress/strain plot (refer to Figure 2.1). Under the same amount of stress, materials with

higher E will undergo a smaller deformation than materials with lower E. Economides and Martin

(2007) stated that high Young’s modulus materials are more brittle than low Young’s modulus

materials.

12

Figure 2.1 Stress and strain relationship showing both elastic and plastic regions (from

Cyberphysics Website 2018). The relationship is linear until the limit of proportionality point.

Once the stress exceeds the yield point, the material deforms plastically where the strain is

permanent (point A returns to point B not to the original curve if stress is released). The fracture

point represents the maximum strain reached before the material ruptures.

Poisson’s ratio, �, is an indication of the resulting strain in the direction perpendicular to

the applied stress compared to the strain parallel to the applied stress. Equation 2.4 is used to

calculate Poisson’s ratio, and Figure 2.2 illustrates its definition (Fjar et al. 2008).

Applying the stress will cause the material to shrink longitudinally and stretch transversely. Hence,

the strain ratio will be negative and Poisson’s ratio will be positive. Theoretically, the values of

Poisson’s ratio range from 0 to 0.5. However, negative values of Poisson’s ratio have been

observed in single crystals where compression occurs along the applied stress axis and all other

directions (Svetlov et al. 1988; Christensen 1996). Measurements of Young’s modulus and

Poisson’s ratio can be static (lab measurements) or dynamic (log measurements).

13

= �� (2.3) � = − �� (2.4)

Where,

= Young’s modulus, psi, [M][L]− [�]− � = Poisson’s ratio, dimensionless � =transverse strain, dimensionless � =longitudinal strain, dimensionless

Figure 2.2 Left: material before applying stress; Right: material after applying stress (from Fjar et

al. 2008). Stress caused the material to shrink parallel to stress direction and stretch perpendicular

to it. Poisson’s ratio represents the negative ratio of transverse strain to longitudinal strain.

2.1.3 In-Situ Stresses

This section presents the main stresses and pressures that affect the formation and should

be accounted for in hydraulic fracture designs.

14

2.1.3.1 Overburden Stress

Overburden stress or vertical stress, � , accounts for the weight of the overlying rock and

fluid. It can be calculated using Equation 2.5 (Varela-Pineda et al. 2015). The weight increases

with depth and so does the overburden stress. Rock density is generally obtained from a density

log.

� = ∫ � � (2.5)

Where,

� = overburden stress, psi, [M][L]− [�]− �= overlying rock density, lbm/in3, [M][L]−

= acceleration due to gravity, in/s2, [L][�]−

= depth, in, [L] 2.1.3.2 Horizontal Stresses

Maximum horizontal stress, ��, and minimum horizontal stress, �ℎ, are the stresses acting

on the formation horizontally in the x-y plane perpendicular to the overburden stress. The three

mutually-perpendicular stresses are related through Hooke’s law as presented in Equation 2.6

(Economides and Martin 2007). The relationship between these stresses also defines the in-situ

stress state. Table 2.1 indicates the different faulting regimes according to the relationship between

the three normal stresses (Golombek 1985). Knowing the stress state is essential for the placement

of horizontal wells that are planned to be hydraulically fractured.

� = [� − � � + � ] (2.6)

Where,

� = strain at the x direction, dimensionless � = stress at the x direction, psi, [M][L]− [�]−

15

� = stress at the y direction perpendicular to x and z directions, psi, [M][L]− [�]− � = stress at the z direction perpendicular to x and y directions, psi, [M][L]− [�]−

Table 2.1 Faulting Regimes Based on Stress State (modified from Golombek 1985)

Stress state Faulting regime � > �� > �ℎ Normal faulting �� > �ℎ > � Reverse faulting �� > � > �ℎ Strike-slip faulting

2.1.3.3 Pore Pressure

Pore pressure, , accounts for the fluid pressure existent in the porous rock. Knowing the

pore pressure is important in calculating the effective stress using Terzaghi’s law as per Equation

2.7 (Terzaghi 1925). Biot’s coefficient (Biot 1941) or poroelastic constant, �, determines how

much pore pressure influences the effective stress. It ranges between 0 and 1. Knowledge of the

in-situ stresses, pressures, and elastic parameters are critical in determining fracture closure

pressure as per Equation 2.8 (Barree et al. 2009). Fracture closure pressure, , below which the

hydraulic fracture closes, is equivalent to the minimum horizontal stress, �ℎ, as the fracture opens

against �ℎ direction.

� = � − � (2.7) = − [� − � ] + �ℎ + � + � (2.8)

Where,

= fracture closure pressure, psi, [M][L]− [�]−

� = effective stress, psi, [M][L]− [�]− �= Biot’s coefficient, dimensionless

= reservoir pore pressure, psi, [M][L]− [�]− � = vertical Biot’s coefficient, dimensionless

16

�ℎ= horizontal Biot’s coefficient, dimensionless � = regional horizontal strain, dimensionless � = horizontal tectonic stress, psi, [M][L]− [�]−

2.2 Unconventional Reservoirs

Unconventional reservoirs are commonly defined as those that require unconventional

technology to be developed. Others may define unconventional reservoirs by setting a permeability

threshold value (e.g. 0.1 md) (Meckel and Thomasson 2008). Cander (2012) defined

unconventional reservoirs based on permeability and viscosity. Figure 2.3 illustrates a graphical

presentation of this definition. The figure suggests that technology has to be used to alter either

permeability or viscosity in unconventional reservoirs to produce them commercially.

Shale hydrocarbons are a major unconventional resource worldwide and in the United

States. The decline in conventional oil and gas production plus technology advancements helped

shale hydrocarbon production to boom in the US since the beginning of this century. Figure 2.4

shows the shale plays in the lower 48 states of the US. Shale characteristics vary from one play to

another and heterogeneities can be observed within the same basin. Therefore, formation damage

mechanisms and stimulation treatments that apply to one shale may not be applicable to another

(Davis 2011). Generally, shale reservoirs are sedimentary rocks interbedded with carbonaceous

and siliceous minerals. Shale gas in these reservoirs comes in three forms: free gas, adsorbed gas,

and gas from natural fracture systems (Economides and Martin 2007). Passey et al. (2012) stated

that most producing shale reservoirs contain Type I or Type II kerogen.

2.3 Hydraulic Fracturing in Horizontal Wells

Drilling horizontal wells induces damage to the reservoir. Bypassing the formation damage

and enhancing low permeability require hydraulic fracturing, which is the completion approach in

17

nearly all horizontal wells drilled in shale reservoirs.

Figure 2.3 Unconventional resources versus conventional resources (from Cander 2012).

Unconventional reservoirs are characterized with low permeability/viscosity ratio where either

permeability or viscosity need to be altered for them to be produced commercially.

2.3.1 Types of Hydraulic Fractures in Horizontal Wells

As described earlier in Section 2.1.3, the three in-situ stresses are overburden, minimum

horizontal, and maximum horizontal stresses. Hydraulic fractures in horizontal wells can be

divided into three types accordingly, transverse, longitudinal, and oblique fractures. Fractures,

generally, tend to open against minimum horizontal stress as it is the path of least resistance. When

the well is drilled in the minimum horizontal stress direction, transverse fractures will be created

as they propagate perpendicular to the wellbore direction. On the other hand, when the well is

drilled perpendicular to the minimum horizontal stress direction, a longitudinal fracture will grow

18

along the direction of the horizontal lateral. Oblique fractures occur when the well is drilled

orthogonal to the minimum and maximum horizontal stress directions. Figure 2.5 demonstrates

the three types of hydraulic fractures in horizontal wells based on their direction of growth. The

most common completion technique in horizontal shale wells is the creation of multiple transverse

fractures. However, Economides and Martin (2007) presented a criterion shown in Table 2.2 for

selecting the type of fracture based on gas reservoir permeability. Both transverse and longitudinal

fractures are single planar fractures. Most shale reservoirs, however, contain complex fracture

networks that involve natural fractures.

Table 2.2 Permeability-based Options for Fracturing Gas Wells (modified from Economides and

Martin 2007)

Permeability range, md Best technical solution > Horizontal wellbore, longitudinal fractures . �� Horizontal wellbore, longitudinal fractures or

vertical wellbore with fracture . �� . Horizontal wellbore, transverse fractures < . Horizontal wellbore, transverse fractures or

vertical wellbore with fracture

2.3.2 Hydraulic Fracture Modes

There are three fracture modes that are characterized based on the force that caused the

crack to open. These modes are Mode I (opening or tensile), Mode II (sliding or in-plane shearing),

and Mode III (tearing or anti-plane shearing). Figure 2.6 shows the three fracture modes. In Mode

I, fractures open against minimum horizontal stress. Modes II and III include shear fractures and

faults (Kanninen and Popelar 1985).

2.3.3 Hydraulic Fracture Modeling

Hydraulic fracture models are constructed to help design fracture stimulation treatments in

a way that they can predict fracture geometry, fracture cleanup, or long-term fractured well

19

performance (Nghiem et al. 1984). Hydraulic fracture models can be broadly categorized into two-

dimensional (2D) and three-dimensional (3D) models (Rahman and Rahman 2010).

Figure 2.4 US lower 48 states shale oil and gas plays (from EIA 2016).

2.3.3.1 2D Fracture Models

2D fracture models assume that fracture is contained, thus only fracture width and length

are the varying dimensions as fracture height is kept constant. Two of the early main 2D fracture

models are the Perkins-Kern-Nordgren (PKN) (Perkins and Kern 1961) and the Khristianovich-

Geertsma-DeKlerk (KGD) (Khristianovich and Zheltov 1955) models. Figure 2.7 illustrates the

difference between the two models. In the PKN model, strain is in the vertical plane. Meaning,

hydraulic fracture width changes with fracture height. The PKN model assumes an elliptical

20

fracture shape in the wellbore and through the formation. On the other hand, strain plane in the

KGD model is on the horizontal plane with a cusp-shaped fracture tip that represents the area of

largest flow resistance. Furthermore, the KGD model assumes a rectangular fracture in the

wellbore and through the formation, which makes fracture width unchanged along the vertical

plane (Yousefzadeh et al. 2017). These models are simplistic and should not be used for complex

fractures as they do not account for factors such as rock fabric and pore pressure (Barree 2015).

Figure 2.5 Hydraulic fracture types in horizontal wells based on their growth direction (from EPT

International 2015).

2.3.3.2 3D Fracture Models

In 3D fracture models, the added complexity is that fracture height is not constant as

assumed in 2D models. Plus, fracture length, width, and height are calculated independently.

21

Therefore, blocks can be assigned unlike pseudo 3D models where only layers can be assigned

and no lateral changes are accounted for. Some 3D fracture models allow for de-coupling to

account for shear slippage (Mode II and III fractures). 3D fracture models use finite difference

(FD) solution or finite element (FE) solutions (Miskimins 2017).

Figure 2.6 Hydraulic fracture modes: (a) Mode I, opening or tensile mode; (b) Mode II, sliding or

in-plane shearing mode; (c) Mode III, tearing or anti-plane shearing mode (from Kanninen and

Popelar 1985).

2.3.4 Natural Fractures

Natural fractures refer to pre-existing fractures that were created over geologic time

(millions of years). The presence of natural fractures increases porosity and can enhance

permeability if these fractures are interconnected. In unconventional reservoirs, multi-stage

hydraulic fracturing of horizontal wells is sought to rejuvenate natural fractures, thus creating a

larger fracture network (Kazemi 2017). However, connecting to natural fractures in low-

permeability reservoirs can cause a high fluid leakoff (Yew and Wing 2014). Simulating natural

fractures requires utilizing discrete fracture network (DFN) models. Natural fractures are very

22

common in shale reservoirs, which makes the fracture network more complex due to their

interaction with hydraulic fractures, as presented in Figure 2.8.

Figure 2.7 PKN versus KGD 2D hydraulic fracture models (from Montgomery and Smith 2010).

The PKN model (left) assumes elliptical fracture in wellbore and through formation. The KGD

model (right) assumes rectangular fracture in wellbore and through formation.

2.3.5 Design and Treatment Schedule

Hydraulic fracture design is a critical step in the stimulation process. It involves many

variables such as fluid selection, proppant selection, types of additives, fluid and proppant rates,

volumes, and concentrations. These variables differ from field to field and even within the same

field based on the target reservoir. Hydraulic fracturing aims at removing the skin, initiating or

enhancing productivity, and ultimately increasing net present value (NPV). One of the important

parameters that reflects the success of a fracturing treatment is the dimensionless fracture

23

conductivity, as presented in Equation 2.9 (Pearson 2001). The upper part of the equation

represents the fracture conductivity, whereas the lower part represents the reservoir deliverability.

Figure 2.8 Fracture complexity levels (from Warpinski et al. 2008). Top left: simple planar

fracture; Top right: planar fracture with added complexity such as roughness and waviness; Bottom

left: complex fracture connected with natural fractures; Bottom right; primary and secondary

fractures connected to create a complex fracture network.

= � (2.9)

Where,

= dimensionless fracture conductivity, dimensionless

= fracture permeability, mD, [L]

= fracture width, ft, [L]

24

= formation permeability, mD, [L]

= fracture half-length, ft, [L]

Proppant fracturing is usually associated with sandstones whereas acid fracturing is

associated with carbonates. A typical proppant fracturing treatment consists of four primary stages.

The first one is the pre-pad stage which aims at cooling the wellbore and conditioning the

formation. The second stage is the pad stage where the main fracturing fluid is injected along with

the required additives. The third one is the proppant laden where the proppant get transported and

placed into the fracture. The last one is the flush stage where any remaining slurry in the wellbore

gets displaced into the formation (Abass 2016).

2.3.5.1 Fracturing Fluid Selection

Fracturing fluids can be water-based, oil-based, or a mixture of both. The properties that

qualify a fluid to be selected for fracturing are formation compatibility, rheology, fluid loss,

breaking capability (viscosity reduction), proppant-carrying capacity (sufficient viscosity), residue

in the proppant pack, and filter-cake residue (Economides et al. 1998). As the main function of the

fluid besides creating the fracture is transporting the proppant, there are two proppant

transportation mechanisms. The first one is through utilizing highly viscous fluids for transporting

the proppant. This method is usually used in low to moderate permeability reservoirs. The other

method, which is used in low permeability reservoirs, is utilizing velocity instead of viscosity

where the fracturing fluid (usually water) is pumped at a high velocity (Al-Muntasheri 2014). In

this case, the most common fracturing fluid is fresh water or slickwater. Additives for viscous

systems include gelling agents, friction reducers, clay control agents, crosslinkers, breakers, iron

control agents, corrosion and scale inhibitors, surfactants, buffers, and biocides/bactericides

(Montgomery 2013). Table 2.3 presents the most common water-based and oil-based fracturing

25

fluids and their characteristics. Table 2.4 shows different types of fracturing fluid additives, their

functions, and examples of each.

2.3.5.2 Proppant Selection

After generating the hydraulic fracture and the fracture pressure is released, the purpose of

the pumped proppant is to provide conductivity in the created fracture. Proppants can be of a basic

type such as sand and ceramic or a modified type such as resin coated proppant (RCP) and

lightweight proppant (Liang et al. 2016). The proppant selection criterion is based on different

factors such as cost, associated fluid system, and conductivity requirements in the formation

(Palisch 2012). The proppant quality can measured in the lab by conducting different proppant

pack conductivity tests such as API RP61 (API 1989) and API RP19D (API 2008) (Duenckel et

al. 2016). Figure 2.9 illustrates the variations in conductivity among different types of proppants.

Usually, the proppant pack conductivity tests yield optimistic results compared to the later field

performance. This difference is attributed to different damage mechanisms. These damage

mechanisms include non-Darcy flow, multiphase flow, reduced proppant concentration, gravity

and viscous segregation, filter-cake and gel residue, embedment, and spalling (Barree et al. 2003;

Palisch et al. 2007).

2.3.6 Hydraulic Fracture Monitoring

There are various techniques to monitor and map hydraulic fracture jobs and the reservoir

response to them. These techniques include diagnostic fracture injection tests (DFITs), tracers

(radioactive and non-radioactive), surface and downhole tiltmeters, fiber optics (DTS and DAS),

and microseismic monitoring (Bhatnagar 2016). This section reviews DFITs and the distributed

acoustic sensing (DAS)/distributed temperature sensing (DTS), which utilize fiber optics, due to

their relevance to the thesis topic.

26

Table 2.3 Water and Oil Based Fracturing Fluids (modified from Miskimins 2017)

Water-based fluids

Fluid Characteristics

Guar gum High molecular weight and residue

Hydroxypropyl guar (HPG) Derived from guar but provides lower residue

Hydroxyethylcellulose (HEC) Cleaner than guar and HPG

Carboxymethyl-hydroxypropyl guar

(CMHPG)

Used in high temperatures and can be

crosslinked with Zr

Carboxymethyl guar, CMG Breaks fast by adding water

CO2 water-based foams Energized fluids, less water usage, more

cleanup, safety concerns N2 water-based foams

Binary foams (CO2 & N2)

Oil-based fluids

Fluid Characteristics

Diesel Minimal damage but safety concern

Lease crude May have PVT changes

Condensate Fire risk due to volatility and flash point

Frac oils Easy to crosslink

Gelled LPG Can be produced back to the lines but

expensive

Figure 2.9 Proppant conductivity pyramid for different types of proppant (from Gallagher 2011).

Top of pyramid represents the highest conductivity (ceramic). Bottom of pyramid represents the

lowest conductivity (sand).

27

Table 2.4 Fracturing Fluid Additives (modified from Abass 2016)

Additive Purpose Examples

Gelling agent Provides viscosity for

proppant transportation Linear gel, Guar, HEC

Crosslinker Provides even greater

viscosity for shearing Borate, zirconate

Breaker Breaks the crosslinked gel for

cleanup

Enzymes, acids,

oxidizers

Surfactant Reduces friction and polymer

residue Methanol, isopropanol

Friction reducer Reduces friction at high

velocity Polyacrylamide

Clay control agent Prevents clay swelling KCl

Iron control agent Prevents precipitation Citric acid

Fluid loss control agent Provides less filtration CaCO3

pH buffer Controls pH to condition fluid

for additives Acetic acid

Corrosion inhibitor Prevents corrosion n-dimethyl formamide

Scale inhibitor Prevents scale precipitation Ammonium chloride

Biocides/bactericides Prevents growth of organisms Gluteraldehyde

2.3.6.1 Diagnostic Fracture Injection Tests (DFITs)

A DFIT is a test pumped prior to performing the main hydraulic fracture for the purpose

of acquiring data about treatment parameters and characterizing the target reservoir. The reservoir

characterization part is what makes DFITs different from traditional mini-frac tests. DFITs are

very common in unconventional reservoirs as they substitute conventional transient analysis tests,

which take long time to run, by providing the falloff data (Barree et al. 2015). A DFIT procedure

starts with pumping at a certain rate to breakdown. Then, the rate is increased and kept constant

for few minutes. Rate step down data can be gathered to determine near wellbore frictions and

fracture extension pressure. Lastly, the falloff portion is used to determine closure pressure and

leakoff mechanism (Barree et al. 2015). Figure 2.10 demonstrates the procedure of a generic DFIT.

DFITs are analyzed using different techniques for closure and after closure parts of the test.

The closure analysis includes G-time function plot of pressure and its derivatives, square root of

28

time plot of pressure and its derivatives, and log-log plot of pressure change and its derivatives

after shut-in (Barree et al. 2009). Each of these plots has its unique signature to determine closure

pressure for the four leakoff mechanisms which are normal leakoff, pressure dependent leakoff

(PDL), fracture tip extension, and height recession or transverse storage (Barree et al. 2009). The

after closure analysis is used to determine reservoir transmissibility and pore pressure.

Figure 2.10 Generic DFIT procedure (from Barree et al. 2015). The black curve represents the

fluid rate that starts low then increases before it ends with a step down until shut-in. The red curve

represents the surface pressure.

2.3.6.2 DAS/DTS Surveys

Distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) surveys are

determined from fiber optic cables that can be deployed in coiled tubing or a by wireline or can be

permanently installed along the back of the casing and cemented in place (Huckabee 2009;

Bhatnagar 2016). A DTS survey is run for the purpose of collecting temperature traces along the

29

wellbore at different times (e.g. completion, stimulation, production) where these temperature

recordings can be later correlated to flow allocation in the reservoir (Kalia et al. 2014). Moreover,

DTS surveys can help identify zonal isolation and fluid communications between stages by

detecting plug leakages in a plug and perf completion (Holley and Kalia 2015; Wheaton et al.

2016).

DAS surveys record the acoustic activities during the different stages of the well life. These

recordings in turn can provide information such as fluid and proppant distributions for different

perforation clusters along the wellbore at stimulation time (Molenaar et al. 2011). Using the

acoustic response, computer algorithms are used to generate quantitative data that present DAS

flow rate total placed proppant in each cluster. Such analysis can be used to assess cluster

efficiency and identify, if any, the existence of clusters that were not effectively stimulated (Holley

and Kalia 2015). Figure 2.11 shows an example of DAS/DTS data captured during a hydraulic

fracturing stimulation treatment. Cooler colors in the DTS test imply fracturing fluids passing

through the cluster. On the other hand, warmer colors in the DAS test refer to high acoustic activity

which implies fluid and proppant passing through the cluster.

2.3.7 Stress Shadow Phenomenon

Introducing a crack or a hydraulic fracture in a rock requires the tensile failure of the subject

rock, which results in changing the stress state around the created crack. The difference between

the pressure of the fracturing fluids and the in-situ minimum horizontal stress constitutes the net

pressure inside the fracture which applies a compressive stress on the surrounding rock. The newly

induced additional stress surrounding the fracture is referred to as a “stress shadow” (Fisher et al.

2004).

30

Figure 2.11 DAS/DTS data (from Wheaton et al. 2016). Top: DTS data changes with time (warmer

colors denote higher temperatures); Middle: DAS data changes with time (warmer colors denote

higher acoustic activity); Bottom: treatment plot showing different curves versus time (black curve

represents rate, dark blue curve represents surface pressure, light blue curve represents bottomhole

pressure, and green curves represents surface and bottomhole proppant concentrations).

2.3.7.1 Impact of Stress Shadowing on Fracture Propagation

In the case of multiple transverse fractures in a horizontal well, the first hydraulic fracture

will open against a minimum horizontal stress of a specific magnitude. The creation of this fracture

will increase the magnitude of the minimum horizontal stress around it. Therefore, a second

hydraulic fracture within the stress shadow extent will need to open against a larger closure

pressure which will limit its growth. A third hydraulic fracture will try to open against a closure

stress whose magnitude is affected by the cumulative stress shadow from both preceding fractures

31

(Fisher et al. 2004; Daneshy 2017). Adding fractures in the stress shadow area of extent will result

in subsequent fractures with limited propagation and less productivity than desired. The

propagation of the middle fractures in a horizontal wellbore will be reduced as they experience

larger magnitudes of closure pressure than the heel and toe fractures. Therefore, the middle

fractures are not expected to contribute to production as much as the outer fractures (Fisher et al.

2004; Wheaton et al. 2014; Barree 2015; Manchanda et al. 2016). Figure 2.12 demonstrates how

the propagation of the middle fractures is hampered by the stress shadow of the heel and toe

fractures. Stress shadowing and fracture propagation are always associated with hydraulic fracture

spacing due its effect on determining the severity of stress shadowing (Ingram et al. 2014).

Hydraulic fracture spacing optimization is discussed in Section 2.3.10.

2.3.7.2 Factors Influencing Stress Shadowing

Studies show that many variables can play a role in altering the stress state, magnitude and

orientation, around a fracture, thus impacting the hydraulic fracture propagation. These variables

include cluster spacing, number of clusters, perforation parameters, reservoir permeability,

mechanical properties, vertical and lateral heterogeneity, reservoir depletion, net pressure, fracture

geometry, original in-situ minimum/maximum horizontal stress ratio, and shut-in time between

stages (Roussel and Sharma 2011; Morrill and Miskimins 2012; Manchanda et al. 2016; Daneshy

2017). Figure 2.13 illustrates the importance of lateral heterogeneity in influencing the stress

shadow. The fractures that propagate in a zone of larger Young’s modulus and a much larger

Poisson’s ratio were able to propagate further even if they were middle fractures. This explains

how the stress shadow effect was minimized by the lateral heterogeneity between the propagating

fractures (Manchanda et al. 2016).

32

Figure 2.12 Effect of stress shadowing in multiple transverse fractures (from Fisher et al. 2004).

Top part shows a top view of a hydraulic fracture where fracture length propagation is limited by

stress shadowing. Bottom part shows a side view of a hydraulic fracture where fracture height is

limited by stress shadowing.

Figure 2.13 Stress shadow effect minimized by lateral heterogeneity between propagating fractures

(from Manchanda et al. 2016). Left two windows show Young’s modulus effect. Right two windows show Poisson’s ratio effect.

33

Increasing the net pressure inside the fracture (fluid pressure minus minimum horizontal

stress) generates more stress shadowing, and therefore, the minimum fracture spacing required to

eliminate stress shadow effect will increase (Morrill and Miskimins 2012). Figure 2.14 illustrates

the net pressure effect on stress shadowing.

The number of fracturing stages in a horizontal wellbore is another factor that affects the

severity of the stress shadow. Some authors suggest that the stress shadow effect becomes less

severe in later stages of fracturing as the early fractures close with time and their induced stress

shadow decreases (Daneshy 2017). Figure 2.15 illustrates this effect in which the slope decreases

with increasing number of stages due to the reduction of the incrementally induced stress shadows.

Figure 2.14 Effect of net pressure on fracture spacing (from Morrill and Miskimins 2012). As fluid

net pressure increases, the induced stress shadow increases and so should the hydraulic fracture

spacing required to eliminate stress shadow effects.

2.3.8 Pressures Associated with Hydraulic Fracturing

There are various pressure terms that should be understood when it comes to hydraulic

fracturing. Table 2.5 lists these pressure terms and the meaning of each one.

34

Figure 2.15 Stress shadow effect diminishes in later stages of fracturing (from Daneshy 2017). The

reduction in the curve slope implies lower additional induced stress in the later stages of fracturing.

Minimum horizontal stress, maximum horizontal stress, and initial closing fracture width

coefficient values shown in the plot.

Table 2.5 Pressure terms associated with hydraulic fracturing (modified from Economides and

Martin 2007; Miskimins 2017)

Pressure term Meaning

Wellhead pressure, Same as injection pressure ( ) or surface

treating pressure ( )

Hydrostatic pressure, ℎ Pressure due to the hydrostatic head in the

wellbore, calculated as per Equation 2.10

(IWCF Website 2012)

Pipe friction pressure, Friction pressure that the fluid sees passing

through the pipe

Bottomhole treating pressure,

Consists of the previous three pressures,

calculated as per Equation 2.11 (Willingham

et al. 1993)

Perforation friction pressure loss, ∆ Pressure loss due to perforation, calculated as

per Equation 2.12 (Willingham et al. 1993)

Tortuosity pressure loss, ∆ Stress halo around perforation that causes

pressure loss

Near-wellbore friction pressure loss, ∆

Consist of the previous two pressures,

calculated as per Equation 2.13 (Miskimins

2017)

35

Table 2.5 Continued

Pressure term Meaning

Instantaneous shut-in pressure, Bottomhole pressure at the time injection is

ceased

Breakdown pressure,

Maximum pressure reached in a hydraulic

fracture treatment at which the formation

starts taking the fracturing fluid

Closure pressure,

Pressure exerted by the formation to close the

fracture, usually equal to minimum horizontal

stress, calculated as per Equation 2.8

Fracture extension pressure,

Same as net extension, tip effects, rock fabric,

and process zone stress, which is the pressure

needed for fracture to grow further

Fracturing fluid pressure, Fluid pressure inside the fracture

Net pressure,

Fluid pressure inside the fracture after closure

(excess pressure, used to grow fracture

further)

Critical fissure opening pressure,

The additional pressure above closure stress

required to activate fissures and natural

fractures

ℎ = . × � × (2.10)

Where,

ℎ = hydrostatic pressure, psi, [M][L]− [�]− � = fluid density, lb/gal, [M][L]−

= true vertical depth, ft, [L] = + + ℎ − ∆ (2.11)

Where,

= bottomhole treating pressure, psi, [M][L]− [�]−

= wellhead pressure, psi, [M][L]− [�]−

= fluid friction pressure inside the pipe, psi, [M][L]− [�]− ∆ = friction loss across the perforations, psi, [M][L]− [�]−

∆ = . 9 �2 4 2 (2.12)

36

Where,

= total flow rate, bbl/min, [L] [�]−

= number of perforations, dimensionless

= perforation diameter, in, [L] = coefficient of discharge, dimensionless

∆ = ∆ + ∆ (2.13)

Where,

∆ = near wellbore pressure loss, psi, [M][L]− [�]− ∆ = pressure loss due to tortuosity, psi, [M][L]− [�]−

2.3.9 Engineered Completion

The traditional way of completing horizontal wells in unconventional shale reservoirs

applies geometric designs in spacing perforation clusters. Geometric completion designs place

perforation clusters or fracture stages in a uniform spacing pattern without accounting for the

lateral heterogeneity of reservoir properties along the horizontal wellbore. However, these lateral

variations can play a vital role in stimulation results and production performance of horizontal

wells (Ajisafe et al. 2014). Besides stress shadowing, geometric completion design is another

possible reason for the uneven contribution of perforation clusters or fracture stages to production.

As a result, recent designs deployed engineered completion as a replacement of geometric

completion.

Engineered completions seek to optimize hydraulic fracture staging and perforation cluster

spacing by considering the petrophysical and geomechanical variations of the adjoining rocks

along the lateral. By acquiring characteristic log data along the lateral, an engineered completion

workflow can be applied to quantify rock quality and completion quality. This is performed

through the calculation of parameters such as breakdown pressure, productivity index, and

37

fracability index from the raw log data (Anifowoshe et al. 2016; Sarmah et al. 2016).

Consequently, engineered completion workflow places perforation clusters in the best quality

rocks. It may not lead to uniform spacing but should result in a more uniform contribution to

production from perforation clusters. DAS/DTS tests run in fiber optic cables can be used in

engineered completions to corroborate the feasibility of the design by quantifying each

stage/cluster individual contribution to production. These tests showed a 30% increase in

production performance of wells completed with the engineered approach compared to the ones

completed with the geometric approach (Sun et al. 2015). Engineered completions are also more

cost-efficient as they reduce the number of perforation clusters by excluding the poor-quality rock

from the perforation process.

2.3.10 Spacing Optimization

The induced stress created by the net pressure inside the hydraulic fracture has its

maximum value at the fracture face. The magnitude of the stress shadow diminishes when moving

away from the fracture face as that energy dissipates with distance (Fisher et al. 2004). Therefore,

placing hydraulic fractures far apart will minimize or eliminate the stress shadow effect. However,

further placement of these fractures could leave parts of the reservoir unstimulated. Therefore, it

is very critical to find the fracture spacing that accounts for both factors. Figure 2.16 shows how

different values of cluster spacing can affect fracture propagation due to stress interference (Lu

2016). The 10 m spacing shows a clear dominance of the outer fractures on the middle one whereas

the 50 m spacing shows almost even propagation in all fractures. The colored scale represents the

scalar stiffness degradation variable (SDEG), which is a measure of how damaged the element is.

Zero SDEG means that the element is not damaged, which implies no fracture extension to the

38

subject zone, whereas a value of 1 SDEG indicates that the element is totally damaged (i.e. the

fracture is completely open).

Considering only the stress shadow effect is not sufficient to obtain the optimum hydraulic

fracture or perforation cluster spacing. Uniform spacing with minimized stress interference will

still show uneven production contribution if reservoir rock quality is not considered. Therefore,

the stress shadow effect has to be coupled with reservoir rock quality lateral variations in order to

come up with the most optimized fracture spacing. This can be achieved through the application

of engineered completion design. Figure 2.17 illustrates a comparison between geometric

completion and engineered completion along with respective logs and other calculated parameters

(Ajisafe et al. 2014). Overall, the engineered completion showed a uniform contribution to

production from perforation clusters when compared to the geometric completion.

Figure 2.16 Effect of cluster spacing on fracture propagation due to stress shadow (left: 10m

spacing vs. right: 50m spacing) (from Lu 2016). SDEG means scalar stiffness degradation variable

(SDEG), which is a measure of how damaged the element is. Outer fractures are dominant in the

10 m spacing case, whereas all fractures are equal in terms of dominance in the 50 m spacing case.

39

Figure 2.17 Engineered completion (Track 1 from the top) vs. geometric completion (Track 2 from

the top), and their respective quality evaluations and logs (from Ajisafe et al. 2014). Engineered

completion spacing planned based on the below respective quality logs (e.g. porosity, resistivity,

Young’s modulus, etc.). Geometric completion has the fracturing stages equally spaced with no considerations of any horizontal reservoir quality logs.

40

CHAPTER 3

METHODOLOGY

To achieve the objectives of the research, a hydraulic fracture model was constructed using

the Grid Oriented Hydraulic Fracture Extension Replicator (GOHFERTM) commercial simulator.

This software is capable of creating a geomechanical model, simulating hydraulic fracture

treatments, performing different pressure diagnostics, forecasting, and analyzing production.

GOHFERTM was developed by Dr. Robert D. Barree as a PhD product at the Colorado School of

Mines (Barree 1984) and continues to be updated by Barree & Associates LLC. Also, Predict-KTM

(Core Laboratories 2018) production prediction simulator was used to conduct fluid and proppant

sensitivity analyses and perform production prediction runs. This software is capable of

incorporating the effects of fracturing fluid type, proppant type, and proppant concentration into

the production model.

The methodology used for this project includes creating the base case model, validating

the model using DFIT and DAS data, and performing different sensitivity runs. The following

sections describe the details of each performed step.

3.1 Base Case Model Development

The first step in this research was to build a base case by modeling the Eagle Ford treatment

well (Well A) and generating treatment and production outputs that match the actual field results.

The vertical reference well (Well C) logs were utilized to calculate geomechanical properties and

build the stress model. These logs and geomechanical properties were calibrated to the Well A

through geosteering. Diagnostic fracture injection tests (DFITs) were analyzed to calculate

reservoir parameters (e.g. pore pressure, closure stress, etc.) and calibrate them with the log output.

41

The actual treatment stages (perforations and pumping schedules) of Well A were entered in the

model, then the resultant treating pressure was calibrated with the actual pressure by modifying

the frictional parameters such as pipe friction and tortuosity factors. Cluster contributions based

on total placed proppant for each cluster were calibrated with the DAS results by adjusting the

perforation parameters. Finally, production results were obtained from the model and history-

matched with the actual production of Well A.

3.1.1 Log Processing

Logs from Well C were uploaded into the model including gamma ray (GR), density

(RHOB), resistivity, sonic (DTC & DTS), and neutron porosity (NPHI). For Well A, wireline GR

and caliper logs were uploaded in addition to the directional survey. Sections 3.1.1.1 – 3.1.1.3

explain the calculation procedure of the main reservoir and geomechanical parameters including

effective porosity, permeability, Young’s modulus, Poisson’s ratio, Biot’s coefficient, pore

pressure, and closure stress.

3.1.1.1 Effective Porosity and Permeability

Effective porosity was calculated by subtracting the shale pore volume from the total pore

volume as indicated in Equation 3.1 (Al-Ruwaili and Al-Waheed 2004). The total porosity in the

equation was obtained by averaging both density and neutron porosities. Since neutron porosity is

a raw log measurement, Equation 3.2 was used to calculate density porosity. As the modeled Eagle

Ford section is lithologically dominated by limestone, the matrix density is 2.71 g/cm3 whereas

the fluid density is 1 g/cm3 (Asquith and Krygowski 2004). The resultant effective porosity is

around 5%. This value is very comparable to the mean value of the effective porosity range (3% -

10%) of the Eagle Ford-Austin Chalk systems (Martin et al. 2011).

∅ = ∅ − ∅ ℎ (3.1)

42

Where, ∅ = effective porosity, dimensionless ∅ = total porosity, dimensionless

ℎ = shale volume fraction, dimensionless

∅ = −− (3.2)

Where, ∅ = density-derived porosity, dimensionless � = matrix grain density, g/cm3, [M][L]− � =log measurement of formation bulk density (RHOB), g/cm3, [M][L]− � =fluid density, g/cm3, [M][L]−

Matrix permeability was estimated from effective porosity by applying a permeability

multiplier and a permeability exponent as indicated in Equation 3.3. For shale, the permeability

multiplier and exponent were initially assumed to be 2 and 3, respectively. The resultant matrix

permeability is 230 nD. This value is within the range of the matrix permeability range (3 nD –

405 nD) of the Eagle Ford-Austin Chalk systems (Martin et al. 2011; Kamari et al. 2017). The

effective permeability was eventually calibrated to DFIT results as discussed in Section 3.1.2.

= × ∅ � (3.3)

Where,

= matrix permeability, mD, [L]

= permeability multiplier, mD, [L]

= permeability exponent, dimensionless

3.1.1.2 Geomechanical Properties

To calculate Young’s modulus and Poisson’s ratio, both compressional and shear sonic

logs were used to obtain the ratio R in Equation 3.4. Then, dynamic Poisson’s ratio and dynamic

43

Young’s modulus were calculated as per Equations 3.5 and 3.6, respectively (Barree et al. 2009).

Dynamic Young’s modulus and Poisson’s ratio from GR, DTC, average porosity, and resistivity

logs were also calculated using synthetic correlations (Barree et al. 2009). All calculated values

were averaged to obtain the final dynamic Young’s modulus and Poisson’s ratio. Figure 3.1

demonstrates the averaged values of dynamic Young’s modulus and Poisson’s ratio from the log.

= 22 (3.4)

Where,

= square of shear to compressional travel time ratio, dimensionless

= shear travel time, µsec/ft, [�][L]−

= compressional travel time, µsec/ft, [�][L]−

� = −− (3.5)

Where, � = dynamic Poisson’s ratio, dimensionless

= � −2 − (3.6)

Where,

= dynamic Young’s modulus, MMpsi, [M][L]− [�]−

For purposes of the model, the static Young’s modulus was determined using the modified

Eissa and Kazi (1988) correlation. The Poisson’s ratio dynamic to static correlation was assumed

to be one.

The Biot’s poroelastic constant (�) was estimated from effective porosity using the

correlation in Equation 3.7 (Crain’s Petrophysical Handbook Website 2015) which is used in the

case of low-quality shear sonic data, which is the case in this suite of logs.

44

Figure 3.1 Left: dynamic Young’s modulus log showing different curves (YMERESIST: calculated based on resistivity; YMEPHIA: based on average porosity; YMEGR: based on GR;

YMEACT: based on DTC & DTS logs; YMEDTC: based on DTC log). Right: dynamic Poisson’s ratio log showing different curves (same YME abbreviation meanings apply to PR).

45

� = . + .9 ∅ (3.7)

Where, � = Biot’s coefficient, dimensionless

3.1.1.3 Pore Pressure and Minimum Horizontal Stress

Pore pressure gradients range from 0.4 to 0.8 psi/ft in the Eagle Ford shale (Kamari et al.

2017). In the subject area, the Eagle Ford is an overpressured zone. Based on the DFIT data

analysis that is discussed in Section 3.1.2, an additional pressure offset of around 3900 psi was

applied to the Eagle Ford and Lower Austin Chalk zones to match DFIT results. This additional

pressure offset resulted in a pore pressure gradient of 0.75 psi/ft. Since Young’s modulus,

Poisson’s ratio, Biot’s coefficient, and pore pressure were all determined, the minimum horizontal

stress or closure stress can be calculated as per the aforementioned Equation 2.8 in Section 2.1.3.3.

Horizontal Biot’s coefficient was assumed to be 1 whereas the overburden pressure gradient was

assumed to be 1.04 psi/ft based on the raw density log. Regional strain and stress values were

assumed to be zero initially before any stress calibration.

3.1.1.4 Grid Setup and Geosteering

After all necessary reservoir and geomechanical property logs were calculated for Well C,

various property grids were generated. Figure 3.2 indicates the main raw and processed logs used

to create the grids. The Eagle Ford Marl/Lower Austin Chalk target zone, where Well A was landed

and geosteered, is highlighted in Figure 3.2. Since the logs were generated initially using Well C

vertical section data, they were converted to fit the Well A horizontal section through geosteering,

which was performed by following similar GR signatures in the horizontal section and staying in

the same zone to match the actual geosteer data. Figure 3.3 represents a snap shot of the geosteering

process performed in the simulator.

46

3.1.2 DFIT and Log Calibration

A diagnostic fracture injection test (DFIT) was performed in the first stage of Well A prior

to the main fracture treatment. Figure 3.4 shows the fracture extension period of the DFIT where

the formation breakdown occurred and a stepdown rate test was conducted. The well was then shut

in for a pressure falloff period that lasted for about 56 hours. Freshwater was pumped in the test at

three different rates of 8.7, 6.3, and 3 bpm. The apparent ISIP recorded at shut in time was 6463

psi. The surface true ISIP was picked at 4545 psi by extrapolating a straight line from the end of

the falloff period to the shut in time. The purpose of this common correction is to account for the

immediate pressure drop due to non-reservoir effects such as tortuosity, perforation restriction,

and wellbore fluids decompression (Barree et al. 2015). Figure 3.5 demonstrates the ISIP pick

from the test data.

The performed closure analysis includes G-function, square-root of time, and log-log plots.

The purpose of these plots is to determine the formation minimum horizontal stress. Equations 3.8

to 3.10 were utilized to obtain the required parameters to construct a G-function plot (Nolte 1979).

Figure 3.6 shows the G-function where the pressure, first derivative, and semi-log derivative were

plotted. As the closure pressure should be picked at the deviation point of the semi-log derivative

curve from the straight line, it was picked to be 9563 psi. Similarly, the same value of closure

stress was picked in the square root plot (Figure 3.7). This value was confirmed in the log-log plot

(Figure 3.8) by picking the point where the pressure difference and its semi-log derivative curves

are no longer parallel (Barree et al. 2009). Furthermore, a half slope was detected in the after-

closure data indicating a linear flow regime (Nolte et al. 1997). However, no radial flow regime

was noted in the data. From the linear flow period, the reservoir pore pressure was obtained to be

9434 psi, as illustrated in Figure 3.9. As the early data in the G-function plot shows a hump above

47

Figure 3.2 Well C processed logs. Tracks from left to right (Track 1: density, resistivity, effective porosity, and GR; Track 2: static

Young’s modulus, process zone stress, Poisson’s ratio, and permeability; Track 3: total stress, pore pressure, and caliper; Track 4: lithology volumes). Top Eagle Ford (primary target and zone where Well A is placed) and bottom Eagle Ford (secondary target) zones

are highlighted.

48

Figure 3.3 Geosteering to convert logs from Well C to Well A. GR signatures of Well C vertical and Well A horizontal logs overlap as

indicated in the top left window of the figure

49

the straight line, the expected leakoff mechanism is pressure-dependent leakoff (PDL) with a PDL

coefficient determined to be 0.0039 1/psi, as demonstrated in Figure 3.10.

Figure 3.4 DFIT rate and pressure data plot showing the fracture extension and falloff periods.

Figure 3.5 ISIP pick from DFIT data. The regression line was extrapolated to the shut-in time to

pick ISIP at 4545 psi and eliminate the toe tortuosity effects.

50

∆� = − (3.8)

� ∆� = + ∆� . − ∆� . (3.9) ∆� = � ∆� − � (3.10)

Where, ∆� = dimensionless pumping time, dimensionless � = elapsed time, minutes, [�] � = total pumping time, minutes, [�] � = dimensionless time function, dimensionless

= G-time function, dimensionless � = � at shut in time, dimensionless

Table 3.1 shows the main reservoir parameters obtained from the DFIT. As described in

Section 3.1.1.3, the reservoir pore pressure was updated in the grids so it matches the value from

the DFIT. To calibrate the stress log data to the DFIT, a tectonic strain of -200 microstrains and a

stress offset of -300 psi were incorporated in the total stress equation (Equation 2.8 in Section

2.1.3.3). It is worth noting that uncertainty is associated with the DFIT results due to the test raw

data not being clean. Moreover, the first stage where the DFIT was pumped may not be

representative of the rest of the stages in the subject well.

Table 3.1 DFIT Results Showing the Main Reservoir Parameters

Parameter Value Parameter Value

BH ISIP 9960 psi Fluid efficiency 94%

Fracture gradient 0.80 psi/ft CFOP 97 psi

BH closure stress 9563 psi PDL coefficient 0.0039 1/psi

Closure gradient 0.76 psi/ft Reservoir pressure 9434 psi

Permeability 0.2 µD Pore pressure gradient 0.75 psi/ft

PZS 407 psi

51

Figure 3.6 Well A DFIT G-function plot showing the bottomhole pressure, pressure first derivative

(dP/dG), and pressure semilog derivative (GdP/dG) curves versus G time. The closure pressure

was picked to be 9563 psi at G=35.726.

52

Figure 3.7 Well A DFIT square-root of time plot showing the bottomhole pressure, pressure first

derivative (dP/d(dt)2), and pressure semilog derivative ((dt)2 dP/d(dt)2) curves versus (dt)2. The

closure pressure was picked to be 9563 psi at (dt)2=44.282 min2.

53

Figure 3.8 Well A DFIT log-log plot showing dP, its first derivative (d(dP)/d(dt)), and its pressure

semilog derivative (dt d(dP)/d(dt)) curves versus (dt). The closure pressure was picked to be 9563

psi at (dt)=1960.905 min.

54

Figure 3.9 After-closure linear analysis plot of bottomhole pressure versus time showing pore

pressure determination at 9434 psi

55

Figure 3.10 Fissure leakoff analysis plot of leakoff ratio versus bottomhole pressure showing

leakoff coefficient determination at 0.0039 1/psi.

3.1.3 Treatments

A total of 14 hydraulic fracturing treatments were performed along the horizontal section

of Well A. Figure 3.11 shows the wellbore with locations of the perforation clusters. The treatment

design including the pumped fluids and proppants for a single stage is summarized in Table 3.2.

In most of the treatment stages, three pad stages are pumped with acid, spacer, and diverter in

between these stages.

The model treating pressure was calibrated with the actual treating pressure until a

reasonable match was achieved for the treatment stages. This was performed by adjusting some

design parameters in each stage such as the PDL coefficient, relative permeability factor, tortuosity

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factor, friction factor, tortuosity erosion factor, and the width exponent. Figure 3.12 illustrates the

treating pressure match for one of the 14 stages (Stage 11). The treatment plots for all stages are

provided in the Appendix A, Figures A.1 to A.14.

To perform more accurate analyses, cluster proppant distributions were calculated based

on the total amount of proppant placed in each cluster. These distributions were matched with

actual values obtained from DAS data by modifying the perforation factors for different clusters.

The initial variation could be due to horizontal reservoir quality differences from cluster to cluster

or due to difference in perforation efficiency. Figure 3.13 demonstrates the actual DAS analysis

with the total proppant placed in every cluster. Figure 3.14 shows the model values with an

indication of the error difference between the model and actual data. The maximum error

difference seen in any cluster is 5%. Figures 3.15 – 3.19 show the proppant concentration for the

five perforation clusters of Stage 11, as an example. Figures 3.20 and 3.21 illustrate a top view and

a side view of the entire wellbore showing proppant concentrations in the created fracture planes,

respectively.

Table 3.2 Representative Treatment Schedule of Performed Treatments in Well A

# Stage description Fluid & proppant

1 Breakdown Slickwater

2 Acid 15% HCl

3 Pre-pad Slickwater

4 Main pad 20# water with 100 mesh white sand & 20# Hypor G with

30/50 white sand

5 Acid 15% HCl



30/50 white sand

8 Acid 15% HCl



30/50 white sand

11 Flush Slickwater

57

Figure 3.11 Well A with actual perforation locations shown as green dots. A total of 14 fracturing

stages (64 perforation clusters) were treated.

58

Figure 3.12 Stage 11 treatment data with a matched pressure between model and actual values.

Dotted pressure curve represents the actual surface treating pressure (surface pressure in the

legend), whereas the connected pressure curve represents the model surface treating pressure (well

pressure in the legend).

59

Figure 3.13 Total proppant displaced at each perforation cluster of the 14 stages calculated from

DAS data (from OptaSense 2015). Each color denotes a perforation cluster (e.g. red color

represents Cluster 2 in all stages). Cluster 1 in any stage is the deepest at that stage (i.e. closest to

toe) and is the far left in the plot.

60

Figure 3.14 Modeled individual cluster contribution to production based on modeled proppant

concentration. Error difference between the model and actual values in each cluster is represented

by the black curve.

61

Figure 3.15 Transverse view of the proppant concentration grid for Cluster 5 (heel cluster) in Stage 11. Formation tops and lithology

are shown on the left whereas, the grid scale is shown on the right.

62

Figure 3.16 Transverse view of the proppant concentration grid for Cluster 4 (middle cluster) in Stage 11. Formation tops and lithology

are shown on the left, whereas the grid scale is shown on the right.

63



64



65

Figure 3.19 Transverse view of the proppant concentration grid for Cluster 1 (toe cluster) in Stage 11. Formation tops and lithology are

shown on the left, whereas the grid scale is shown on the right.

66

Figure 3.20 A top view of the entire wellbore of Well A showing the created fracture planes in all

14 stages. The shown property grid is proppant concentration in lb/ ft2 (scale on the right).

67

Figure 3.21 A side view of the entire wellbore of Well A showing the created fracture planes in all

14 stages. The shown property grid is proppant concentration in lb/ft2 (scale on the right).

3.1.4 Production History Matching

After matching treating pressures and cluster proppant distributions, production runs were

performed assuming uniform fracturing stages. From the actual production data, type curve plots

were generated to determine various parameters to help achieve the match. The three pressure-

time plots, including the dimensionless type curve plot, pseudo-plot, and semi-log plot, are

illustrated in Figures 3.22 – 3.24, respectively. From these plots, parameters such as Kh, fracture

half-length, aspect ratio, and drainage area were found and plugged in the production model. As a

result, a good production history match using pressure as the control variable was achieved, as

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demonstrated in Figure 3.25. It is worth noting that the wells in the subject area utilize gas lift

mechanisms to produce oil as they are located in a high GOR area.

3.2 Sensitivity Analyses Creation

A base case model with calibrated logs, matched treating pressures, matched cluster

contributions based on proppant distribution, and history-matched production was constructed.

Such a reliable model now allows for conducting sensitivity analyses. Different parameters from

the base case were altered including matrix permeability, Young’s modulus, Poisson’s ratio, Biot’s

coefficient, cluster spacing, fracturing fluid type, and proppant type as shown in Table 3.3.

Changes in well performance in response to these changes were recorded. Results of the sensitivity

analyses are described in Chapter 4.

Table 3.3 Created Sensitivity Runs

# Sensitized parameter Sensitized fracturing stage(s)

1 Matrix permeability Stages 3, 7, 9, and 13

2 Poisson’s ratio Stages 3, 7, 9, and 13

3 Young’s modulus Stages 3, 7, 9, and 13

4 Biot’s coefficient Stages 3, 7, 9, and 13

5 Perforation cluster spacing Entire wellbore

6 Fracturing fluid type Stage 3

7 Proppant type Stage 3

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Figure 3.22 Type curve plot showing the model dimensionless pressure and its derivative establish

a good fit with actual data.

70

Figure 3.23 A fitted pseudo pressure plot of dp/q versus time showing the representative estimated

ultimate recovery (EUR), drainage area, and aspect ratio.

71

Figure 3.24 A fitted semi-log plot of dp/q versus time showing the representative permeability,

transmissivity, skin, and fracture half-length.

72

Figure 3.25 Model production history matched with actual production. The matched curves include

bottomhole pressure, oil rate, water rate, cumulative oil production, and cumulative water

production. Matching was established for the available production data of around 470 days.

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CHAPTER 4

MODEL RESULTS AND DISCUSSION

This chapter summarizes the results of the multiple sensitivity analyses performed on the

base case model. The sensitivity analyses were implemented for different parameters on different

fracturing stages of Well A. In addition, the natural fracture density was obtained from image logs

and used to evaluate reservoir quality lateral variations in some stages.

4.1 Parameter Sensitivity Analyses

Sensitivity analyses of different parameters were conducted to address the effect of

reservoir lateral variations on the performance of Well A. Such sensitivity analyses will determine

the effects of running an engineered completion as opposed to the geometric completion run on

the actual base case. The sensitized parameters include matrix permeability, Poisson’s ratio,

Young’s modulus, and Biot’s poroelastic coefficient as shown in Table 3.3. In the base case model,

the value of each parameter is basically identical along the horizontal wellbore (minimal to no

lateral variations). The changes to these parameters were applied to different stages of the wellbore

to measure the extent of these changes. In each sensitivity analysis of these parameters, the

estimated flowing fracture length was the comparison factor between the different sensitivities.

The simulated stages for the aforementioned four parameters are Stages 3, 7, 9, and 13. In addition,

spacing between perforation clusters was altered from the base model to address the effect of stress

shadowing. Created fracture volume, fracture conductivity, and cumulative oil production were

the comparison factors in the cluster spacing sensitivity cases. Various fluid and proppant types

were analyzed and compared to those used in the base case. Consequently, cumulative oil

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production was found and compared for each fluid/proppant type case. In all of the sensitivity runs,

the number of fracturing stages was kept unchanged so that the amount of fluid and proppant

pumped was the same in all stages. This is to avoid running the economic differences of pumping

larger amounts of fluids and proppants, thus providing a more realistic comparison. Also in all

sensitivity runs, only one parameter was changed at a time to allow for comparisons based on that

parameter alone.

4.1.1 Matrix Permeability Sensitivity

In the model grids, the matrix permeability values were varied for the nodes of fracturing

Stages 3, 7, 9, and 13, in the primary target reservoir only (Eagle Ford Marl). Figure 4.1

demonstrates how the permeability values were modified for selective multiple nodes representing

the simulated four stages. Table 4.1 indicates the simulated five cases for permeability sensitivity

analysis. In each case, the same permeability was applied to the simulated four cases and their

flowing fracture length was obtained and compared to that of the base model. Figures 4.2 – 4.5

illustrate the change in flowing fracture length as matrix permeability changes in Stages 3, 7, 9,

and 13, respectively. In all stages, the general trend for the flowing fracture length is to increase

in most clusters as permeability increases. The rate of improvement in the flowing fracture length

is more pronounced in the heel clusters compared to slower improvement in the toe clusters.

Moreover, the toe cluster in Stages 7 and 9 showed a declining trend in flowing fracture length

while increasing the permeability. This attenuation can be ascribed to the high magnitude of stress

shadow that reaches up to 1700 psi in the toe cluster compared to values around 500 psi in the heel

cluster. Overall, the increase in permeability results in enhancing the flowing fracture length. The

response of clusters with less stress shadowing to changing permeability is more distinct than that

of clusters with larger stress shadow magnitudes.

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Figure 4.1 Matrix Permeability grid showing how Stages 3, 7, 9, and 13 permeability values are different from the rest of the wellbore

stages. Case 5 (0.0023 mD permeability) is used here as an example.

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Table 4.1 Matrix Permeability Sensitivity Cases

Case Permeability (mD)

1 0.000023

2 0.0001

3 (base case) 0.00023

4 0.0005

5 0.0023

Figure 4.2 Stage 3 matrix permeability sensitivity plot showing the change in flowing fracture

length as permeability changes. Each colored curve represents a perforation cluster in Stage 3 with

Cluster 3.1 being the closest to the toe and Cluster 3.5 being the closest to the heel. Black ellipse

represents the base case matrix permeability (0.00023 mD).

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length as permeability changes. Each colored curve represents a perforation cluster in Stage 7 with



78


length as permeability changes. Each colored curve represents a perforation Cluster in Stage 9 with



79


length as permeability changes. Each colored curve represents a perforation cluster in Stage 13

with Cluster 13.1 being the closest to the toe and Cluster 13.5 being the closest to the heel. Black

ellipse represents the base case matrix permeability (0.00023 mD).

4.1.2 Poisson’s Ratio Sensitivity

Sensitivity of the Poisson’s ratio was performed in Stages 3, 7, 9, and 13. Poisson’s ratio

of the Eagle Ford Marl was edited in the grid for the simulated four stages as shown in Figure 4.6.

Four different values of Poisson’s ratio were tested, as summarized in Table 4.2. In each case,

flowing fracture length was acquired as a simulation output and the results were compared against

each other. Figures 4.7 – 4.10 show the change in flowing fracture length as a function of Poisson’s

ratio in stages 3, 7, 9, and 13, respectively. As Poisson’s ratio increases, the general trend for

flowing fracture length is to increase in most perforation clusters. However, the amount of increase

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in flowing fracture length differs from case to case. The improvement in flowing fracture length is

sharper moving from a 0.28 PR to a 0.33 PR whereas the change is very minimal in the rest of the

cases. This is applicable in all of the simulated four stages. Nevertheless, the toe cluster in Stages

7 and 9 showed a reverse trend where the flowing fracture length declined as Poisson’s ratio

increased from 0.28 to 0.33. This is attributed to the large stress shadow impact from the preceding

stage that prevents these toe clusters from improving the flowing fracture length. On the other

hand, the heel clusters in all stages showed no rejection to the improvement due to the minimal

stress shadow effect.

Table 4.2 Poisson’s Ratio Sensitivity Cases

Case Poisson’s ratio

1 0.15

2 0.20

3 (base case) 0.28

4 0.33

4.1.3 Young’s Modulus Sensitivity

Five different values of Young’s modulus were studied, as presented in Table 4.3. These

values were altered in the Eagle Ford Marl grid nodes for fracturing Stages 3, 7, 9, 13, as shown

in Figure 4.11. In all sensitivity cases, the values of the flowing fracture length were obtained and

compared amongst each case. Figures 4.12 – 4.15 describe the response of the flowing fracture

length to the change in Young’s modulus. The observed trend in these figures is the gradual

decrease in flowing fracture length when increasing Young’s modulus. However, the toe cluster

of Stage 9 (Cluster 9.1) experienced a sharper decrease compared to other clusters. This additional

deterioration in flowing fracture length was underpinned by the relatively larger stress shadow

acting on this cluster. Unlike the effect of Poisson’s ratio, increasing Young’s modulus negatively

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impacted the flowing fracture length. It is worth noting that Young’s modulus was multiplied by

a negative strain offset in the total stress equation to calibrate the stress model to the DFIT data

which have caused the reverse effect of Young’s modulus.

Table 4.3 Young’s Modulus Sensitivity Cases

Case Young’s modulus (MMpsi) 1 2.5

2 3.5

3 4.5

4 (base case) 5.5

5 6.5

4.1.4 Biot’s Coefficient Sensitivity

For Well A, the value of Biot’s coefficient was found to be high (0.9) due to the low

effective porosity. Therefore, four lower values were used to perform the sensitivity analysis of

Biot’s coefficient, as indicated in Table 4.4. Biot’s coefficient values were changed for the Eagle

Ford Marl in Stages 3, 7, 9, and 13 as shown in Figure 4.16. Consequently, the flowing fracture

length was found and compared between the five simulated cases. Figures 4.17 – 4.20 illustrate

the change in flowing fracture length as a response to changing Biot’s coefficient. Similar to

permeability and Poisson’s ratio, the general trend for the flowing fracture length was to increase

when increasing Biot’s coefficient. However, the toe clusters (first and second clusters) in most

stages did not have a stable trend as it showed a fluctuating behavior while changing Biot’s

coefficient.

The sensitized three parameters (Poisson’s ratio, Young’s modulus, and Biot’s coefficient)

are all components of the total stress equation. Changes that caused the total stress to increase also

caused the flowing fracture length to improve. The increase in total stress created a large

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differential stress vertically which caused most of the fracture energy to be dissipated in the

transverse direction, hence, increasing the flowing fracture length.

Table 4.4 Biot’s coefficient Sensitivity Cases

Case Biot’s coefficient 1 0.1

2 0.3

3 0.5

4 0.7

5 (base case) 0.9

4.1.5 Cluster Spacing Sensitivity

To address the effect of stress shadowing and its relationship to fracture spacing,

sensitivities of spacing between perforation clusters have been conducted. Since the total wellbore

length and the number of fracturing stages are fixed, changing cluster spacing implies changing

the number of perforation clusters. Four different scenarios of cluster spacing were run, as

presented in Table 4.5. Figures 4.21 – 4.24 demonstrate the wellbore trajectory showing the

spacing and number of clusters in Scenarios 1, 2, 3, and 4, respectively.

Table 4.5 Cluster Spacing (Number of Clusters) Sensitivity Scenarios

Scenario Cluster spacing (number of clusters)

1 57 ft spacing (84 clusters)

2 (base scenario) 76 ft spacing (64 clusters)



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Figure 4.6 Poisson’s ratio grid showing how Stages 3, 7, 9, and 13 PR values are different from the rest of the wellbore stages. Case 2 (0.2 Poisson’s ratio) is used here as an example.

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Figure 4.7 Stage 3 Poisson’s ratio sensitivity plot showing the change in flowing fracture length

as Poisson’s ratio changes. Each colored curve represents a perforation cluster in Stage 3 with


represents the base case Poisson’s ratio (0.28).

85

Figure 4.8 Stage 7 Poisson’s ratio sensitivity plot showing the change in flowing fracture length as Poisson’s ratio changes. Each colored curve represents a perforation cluster in Stage 7 with



86

Figure 4.9 Stage 9 Poisson’s ratio sensitivity plot showing the change in flowing fracture length

as Poisson’s ratio changes. Each colored curve represents a perforation cluster in Stage 9 with



87

Figure 4.10 Stage 13 Poisson’s ratio sensitivity plot showing the change in flowing fracture length as Poisson’s ratio changes. Each colored curve represents a perforation cluster in Stage 13 with



88

Figure 4.11 Young’s modulus grid showing how Stages 3, 7, 9, and 13 YM values are different from the rest of the wellbore stages.

Case 1 (2.5 MMpsi Young’s modulus) is used here as an example.

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Figure 4.12 Stage 3 Young’s modulus sensitivity plot showing the change in flowing fracture length as Young’s modulus changes. Each colored curve represents a perforation cluster in Stage

3 with Cluster 3.1 being the closest to the toe and Cluster 3.5 being the closest to the heel. Black

ellipse represents the base case Young’s modulus (5.5 MMpsi).

90




91




92


13 with Cluster 13.1 being the closest to the toe and Cluster 13.5 being the closest to the heel.

Black ellipse represents the base case Young’s modulus (5.5 MMpsi).

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Figure 4.16 Biot’s coefficient grid showing how Stages 3, 7, 9, and 13 Biot’s coefficient values are different from the rest of the wellbore

stages. Case 4 (0.7 Biot’s coefficient) is used here as an example.

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Figure 4.17 Stage 3 Biot’s coefficient sensitivity plot showing the change in flowing fracture length as Biot’s coefficient changes. Each colored curve represents a perforation cluster in Stage


ellipse represents the base case Biot’s coefficient (0.9).

95




96

Figure 4.19 Stage 9 Biot’s coefficient sensitivity plot showing the change in flowing fracture

length as Biot’s coefficient changes. Each colored curve represents a perforation cluster in Stage



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13 with Cluster 13.1 being the closest to the toe and Cluster 13.5 being the closest to the heel.

Black ellipse represents the base case Biot’s coefficient (0.9).

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spacing of 57 ft. This plot represents Scenario 1 from Table 4.5.

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Figure 4.22 Well A trajectory plot showing 64 perforation clusters (green dots) with an average

cluster spacing of 76 ft. This plot represents Scenario 2 from Table 4.5 (actual treatment scenario).

100



101



In all four of the simulated spacing scenarios, the flowing fracture volume for each cluster

was obtained by multiplying flowing fracture length, fracture height, and average fracture width,

then the resultant flowing fracture volumes were summed to obtain the total created fracture

network volume for each scenario. Moreover, effective fracture conductivity was obtained as a

simulation output for each cluster. The effective fracture conductivity values were averaged for

the heel clusters, middle cluster, and toe clusters for each scenario. Both properties of flowing

fracture volume and fracture conductivity were plotted against cluster spacing for all scenarios in

Figure 4.25. Similarly, the total amount of proppant placed in each cluster was found as a

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simulation output for each cluster and averaged for the heel clusters, middle clusters, and toe

clusters for each scenario. Consequently, total proppant and fracture volume were plotted against

cluster spacing for all scenarios in Figure 4.26. Completing the well with a larger number of

perforation clusters (tighter spacing) will create a larger fracture network volume. However, the

fracture conductivity and the total amount of placed proppant per fracture will decline. Also, the

dominance of the heel clusters becomes much more evident. On the other hand, the larger spacing

scenarios result in a smaller fracture network volume but a more conductive one. Moreover, the

fracture conductivity values of the clusters become fairly close to each other indicating a

potentially more uniform contribution from the clusters. As observed in the toe clusters curve, the

fracture conductivity jumped from 5 mD.ft to 21 mD.ft equaling the conductivity value of the heel

clusters. The rate of improvement in fracture conductivity is more pronounced in the toe clusters

as they released the large stress shadow acting on them when the spacing increased. Nonetheless,

the rate of conductivity improvement is less in the heel clusters as they were under relatively lower

stress shadowing effects.

In addition to the obtained average conductivity values for the heel, middle, and toe

clusters, the maximum, minimum, and standard deviation of fracture conductivity values were

calculated for all spacing scenarios. Figures 4.27 – 4.30 present column charts of the average,

maximum, minimum, and standard deviation values of fracture conductivity, respectively, for the

heel, middle, and toe clusters in all spacing scenarios. From Figure 4.27, it can be seen that the

average fracture conductivity distribution becomes more uniform as fracture spacing increases.

The maximum fracture conductivity established was found to be 83.3 mD.ft in scenario 3 (100 ft

cluster spacing). Conversely, the minimum fracture conductivity was found to be 1.169 mD.ft in

Scenario 1 (57 ft cluster spacing). Figure 4.30 suggests that the heel cluster fracture conductivity

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data are spread out from the average value unlike the middle and toe clusters where the data are

close to the fracture conductivity mean value.

The comparison between the different cluster spacing scenarios was further taken to a

production perspective. The Predict-KTM simulator was used to forecast the production for each

spacing scenario. The number of contributing perforation clusters to production in each case was

assumed to be half the total number of clusters. It was assumed that Well A is draining from both

the top and bottom Eagle Ford zones as the top Eagle Ford is the primary target and the bottom

Eagle Ford is the secondary target. Table 4.6 shows the spacing scenarios that went into Predict-

KTM for production simulation. Figures 4.31 – 4.33 illustrate the forecasted oil production rate, a

zoomed-in plot (500 days) of the forecasted oil production rate to show history matching, and the

cumulative oil production for the different cluster spacing scenarios during a period of 30 years,

respectively. Scenario 1 (highest numbers of clusters and tightest spacing between clusters)

forecasted the highest cumulative oil production of 355,000 STB in 30 years compared to 330,000

STB, 301,000 STB, and 256,000 STB production for Scenarios 2, 3, 4, respectively. The difference

in cumulative oil production reduces with time between the simulated scenarios. Despite having

the lowest initial production rate among the four scenarios, Scenario 4 ended the 30-year

simulation period having the highest production rate. It is worth noting that the modeled production

in Predict-KTM is highly controlled by matrix permeability. Appendix B shows the input

parameters for the production model including the input matrix permeability, 0.268 µD, which is

the same permeability value obtained from the DFIT.

It can be deduced that having more contributing perforation clusters (lower spacing) helped

to accelerate the production and drain the reserves faster. Finally, the created fracture network

volume, which is an indication of reservoir contact, was more influential on production than

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fracture conductivity for the studied case. However, this may not be the case in other reservoirs

where fracture conductivity can be as important as reservoir contact. These results agree with

literature papers which suggest that hydraulic fracture designs in low-permeability reservoirs focus

more on increasing fracture surface area than increasing fracture conductivity as the created

fracture network volume strongly influences production (Liang et al. 2016).

Figure 4.25 Change in fracture conductivity (left axis) and fracture network volume (right axis) as

a function of changing cluster spacing. The base scenario of 76 ft cluster spacing is highlighted by

the black ellipse. Fracture network volume, in the figure, is defined as the flowing fracture length

multiplied by fracture height and average fracture width.

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Figure 4.26 Change in total proppant placed (left axis) and fracture network volume (right axis) as

a function of changing cluster spacing. The base scenario of 76 ft cluster spacing is highlighted by

the black ellipse. Fracture network volume, in the figure, is defined as the flowing fracture length

multiplied by fracture height and average fracture width.

Table 4.6 Cluster Spacing (Number of Contributing Clusters) Sensitivity Scenarios

Predict-KTM Scenario Cluster spacing (number of contributing clusters)


2 (base scenario) 76 ft spacing (32 clusters)



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Figure 4.27 Average fracture conductivity distribution between the heel, middle, and toe clusters

of all 14 fracturing stages in all simulated spacing scenarios.

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Figure 4.28 Maximum fracture conductivity distribution between the heel, middle, and toe clusters


108

Figure 4.29 Minimum fracture conductivity distribution between the heel, middle, and toe clusters


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Figure 4.30 Standard deviation fracture conductivity distribution between the heel, middle, and toe

clusters of all 14 fracturing stages in all simulated spacing scenarios.

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Figure 4.31 Forecasted oil production rate for the different cluster spacing scenarios for 30 years.

The actual production rate is represented by the green curve and shown for the available data

period of 470 days.

111

Figure 4.32 A zoomed-in plot of the forecasted oil production rate for the different cluster spacing

scenarios for 30 years. The actual production rate is represented by the green curve and shown for

the available data period of 470 days.

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Figure 4.33 Forecasted cumulative oil production for the different cluster spacing scenarios for 30

years. The actual cumulative production is represented by the green curve and shown for the

available data period of 470 days overlapping the 32 contributing fractures (base scenario) curve.

4.1.6 Fluid and Proppant Type Sensitivity

Different fluid and proppant types were simulated for Well A. In each case, the cumulative

oil production was forecasted for 11,000 days (~30 years), and the results were compared among

the cases. In addition, the dynamic proppant conductivity was calculated for the proppant type

sensitivity cases. The simulated fracturing fluid types are shown in Table 4.7. For each fluid type

case, Stage 3 was simulated in GOHFERTM and the fracture geometry (fracture length and fracture

height) and simulation results (e.g. proppant concentration) were obtained. The gross fracture

length changed with the fluid type but the flowing fracture length stayed the same for all fluids.

However, the main change was in the fracture height as fluid type changes. Figures 4.34 – 4.36

show the proppant concentration grid for Cluster 5 (as an example) of Stage 3 for the simulated

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fracturing fluid types. Fracturing fluid 50# CMHPG-Zr showed the largest fracture height growth

of around 200 ft compared to 148 ft and 74 ft fracture height for 2% KCl and 45# Guar-Borate 2

fracturing fluids, respectively. GOHFERTM simulation results for the different fracturing fluid

types were exported into Predict-KTM and a production forecast for 30 years was run for every

fluid type case. For the production runs, it was assumed that both the top and bottom Eagle Ford

zones are targets of Well A. Figure 4.37 demonstrates the cumulative oil production curves for the

simulated fracturing fluid types. Fracturing fluid 50# CMHPG-Zr that showed the largest fracture

growth also forecasted the highest 30-year cumulative oil production of 430,000 STB compared

to 330,000 STB and 183,000 STB for 2% KCl and 45# Guar-Borate 2 fracturing fluids,

respectively. Two commercial types of sand and two commercial types of ceramic were simulated

for proppant type sensitivity analysis. The mesh size of all of these proppants was kept fixed at

30/50 to provide a more realistic comparison. The simulated types of proppant are summarized in

Table 4.8.

Stage 3 was simulated in GOHFERTM for each proppant type and the results were taken to

Predict-KTM for production analysis. Figures 4.38 – 4.41 show the proppant concentration grid for

each proppant type case from GOHFERTM. The base case proppant (Sand A) generated the highest

fracture growth with 148 ft fracture height compared to 116 ft, 110 ft, and 106 ft values for Sand

B, Ceramic A, and Ceramic B, respectively. Figures 4.42 and 4.43 illustrate the dynamic proppant

conductivity and cumulative oil production plots for the simulated proppant types. Under the Eagle

Ford stress value of 9600 psi, the difference in proppant conductivity between the simulated

proppant types is minimal with the ceramic types having a slightly higher conductivity than the

sand types. The production runs showed that Sand A (largest fracture height growth) forecasted

the highest 30-year cumulative oil production of 330,000 STB compared to 285,000 STB, 274,000

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STB, and 264,000 STB for Sand B, Ceramic A, and Ceramic B proppants, respectively. These

production results agree with the cluster spacing sensitivity production results which showed that

reservoir contacted area (volume of created fracture) is more important than fracture conductivity

in the studied case of Well A.

Table 4.7 Fracturing Fluid Type Sensitivity Cases

Fracturing fluid type Sensitized fracturing stage

2% KCl (base case) Stage 3

45# Guar-Borate 2 Stage 3

50# CMHPG-Zr Stage 3

Table 4.8 Proppant Type Sensitivity Cases

Proppant type Sensitized fracturing stage

Sand A 30/50 (base case) Stage 3

Sand B 30/50 (curable resin coated proppant) Stage 3

Ceramic A 30/50 (low density) Stage 3

Ceramic B 30/50 (low density) Stage 3

4.2 Natural Fracture Density

A formation micro-imager (FMI) log was run in Well A. The log analysis provided the

natural fracture density in the horizontal lateral. The density of natural fractures can provide an

indication about reservoir quality and its lateral variations. Figure 4.44 shows the natural fracture

density for all stages with the individual perforation clusters shown as red dots. This natural

fracture density log agrees with the mud log in a sense that the depths with larger natural fracture

density correlated with more gas shows. Comparing the natural fracture density plot with the DAS

actual total proppant per cluster plot illustrated in Figure 3.13 (Section 3.1.3), a relationship with

regards to reservoir quality and stress shadowing can be established. As natural fracture density is

an indication of reservoir quality, Stages 9 and 12 are only controlled by stress shadowing impact

115

as they have no fracture density recorded and hence no reservoir quality differences. From Figure

4.44, Stages 9 and 12 show the normal behavior under stress shadowing where the heel clusters

were the dominant ones in terms of total placed proppant and the toe clusters were having the

lowest total placed proppant values. This observation confirms that these clusters are only affected

by stress shadowing where no reservoir quality variations played a role in changing the cluster

total proppant distribution.

Something to be noted that may affected the outcome of the of the fiber optic analysis

results is the plug leakage or the inter-stage communication behind the casing which was captured

by the DTS data. Figures 4.45 and 4.46 demonstrate the DTS data for Stages 1-7 and 8-14

respectively. The communication between some stages is observed as the cooling effect during a

stage hydraulic fracturing shows to extend to the previous stage. Inter-stage communication is

observed in Stages 1-2, 4-5, 5-6, 6-7, 8-9, 9-10, 10-11, 11-12, 12-13, and 13-14. Cluster 3 of Stage

4 and Cluster 4 of Stage 6, which showed a large amount of total placed proppant from DAS data

(Figure 3.13 in Section 3.1.3), might be affected by inter-stage communication as appears during

fracturing of Stages 5 and 7 in DTS data (Figure 4.45).

116

Figure 4.34 Proppant concentration grid for Cluster 5 of Stage 3. The simulated fracturing fluid type is 2% KCl (base case).

117

Figure 4.35 Proppant concentration grid for Cluster 5 of Stage 3. The simulated fracturing fluid type is 50# CMHPG-Zr.

118

Figure 4.36 Proppant concentration grid for Cluster 5 of Stage 3. The simulated fracturing fluid type is 45# Guar-Borate 2.

119

Figure 4.37 Forecasted cumulative oil production for different fracturing fluid types for 30 years.

The current production was matched as seen in the 2% KCl curve during 470 days.

120

Figure 4.38 Proppant concentration grid for Cluster 5 of Stage 3. The simulated proppant type is Sand A 30/50 (base case).

121

Figure 4.39 Proppant concentration grid for Cluster 5 of Stage 3. The simulated proppant type is Sand B 30/50.

122

Figure 4.40 Proppant concentration grid for Cluster 5 of Stage 3. The simulated proppant type is Ceramic A 30/50.

123

Figure 4.41 Proppant concentration grid for Cluster 5 of Stage 3. The simulated proppant type is Ceramic B 30/50.

124

Figure 4.42 Dynamic proppant conductivity for the simulated proppant types plotted against

formation stress. Eagle Ford stress is pointed in the figure.

125

Figure 4.43 Forecasted cumulative oil production for different proppant types for 30 years. The

current production was matched as seen in Sand A curve during 470 days.

126

Figure 4.44 Natural fracture density plotted versus Well A measured depth. The shaded yellow

boxes refer to the location of the 14 stages whereas the red dots represent the depths of the

individual perforation clusters.

127

Figure 4.45 DTS data recorded during hydraulic fracturing for Stages 1-7 indicating communication between some stages (modified

from OptaSense 2015). The x-axis represents time, whereas the y-axis represents the measured depth. The inter-stage communication is

represented by the red squares. Clusters 3 of Stage 4 and Cluster 4 of Stage 6 are highlighted.

128

Figure 4.46 DTS data recorded during hydraulic fracturing for Stages 8-14 indicating communication between some stages (modified

from OptaSense 2015). The x-axis represents time, whereas the y-axis represents the measured depth. The inter-stage communication is

represented by the red squares.

129

CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

This chapter summarizes the main findings of the study and suggests recommendations for

future work using the already accomplished results as a baseline.

5.1 Conclusions

This research investigated the extent of the induced stress shadow due to hydraulic

fracturing, examined the influence of rock quality lateral variations in horizontal wellbores, and

consequently studied how these two factors can play a role in optimizing hydraulic fracture spacing

in unconventional reservoirs. A hydraulic fracture model was constructed via GOHFERTM to

achieve the purpose of the research, and Predict-KTM was used to forecast production behaviors.

Data from two wells targeting the Eagle Ford reservoir were provided by the RCP Consortium and

utilized for this study. Raw data logs were processed and geomechanical and reservoir properties

were calculated. The diagnostic fracture injection test (DFIT) performed on the simulated well was

analyzed and results were calibrated with the log outputs. As a result, a stress model that

encompasses both the log data and the DFIT analysis was developed. Fourteen treatment stages

were created to simulate the actual well treatments then the treating pressures were matched

accordingly for all stages. The individual cluster contribution to stage production was measured

based on the total placed proppant in every cluster. These contributions were matched with the

results from fiber optic distributed acoustic sensing (DAS) hydraulic fracture profiling analysis by

adjusting the perforation factors of these clusters. The cumulative oil production, water production,

and bottomhole pressure were history matched with the actual well production data.

130

After creating a matched stimulation and production base model, sensitivity analyses were

performed on that model. The sensitivity analyses included matrix permeability, Poisson’s ratio,

Young’s modulus, Biot’s coefficient, fracturing fluid, and proppant type. Four scenarios of

different perforation cluster spacing were simulated. The purpose of the parameter sensitivity

analysis was to address the effect of reservoir lateral variations whereas the purpose of the spacing

analysis was to address the effect of stress shadow phenomenon. Finally, the natural fracture

density results from the image log were correlated with reservoir lateral variations and stress

shadow effect. Based on the performed analyses, the following conclusions can be drawn:

1. To design hydraulic fracture spacing in unconventional reservoirs, both stress

shadowing and reservoir quality lateral variations have to be considered. In this study,

lateral variation sensitivity runs were shown to affect the fracture geometry results.

Cluster spacing sensitivity runs showed different stimulation and production results.

2. Fracturing stages with higher matrix permeability were able to create fractures with

larger flowing fracture length because increasing permeability enhances reservoir

quality. Increasing the matrix permeability from the base model value (0.00023 mD)

to 0.0023 mD caused 69%, 68%, and 48% increase in the flowing fracture length of the

heel clusters, middle clusters, and toe clusters, respectively.

3. An improvement in flowing fracture length was observed in most clusters when

increasing the Poisson’s ratio in the simulated stages. When changing the Poisson’s

ratio from a base model value of 0.28 to 0.33, rates of 32%, 41%, and -1.4% of change

in flowing fracture length were measured in the heel clusters, middle clusters, and toe

clusters, respectively. Toe clusters were able to improve flowing fracture length in

Stages 3 and 13 but not Stages 7 and 9.

131

4. Increasing the Young’s modulus from a base model value of 5.5 MMpsi to 6.5 MMpsi

decreased the flowing fracture length of the heel clusters, middle clusters, and toe

clusters at rates of -8%, -3%, and -24%, respectively.

5. Reduction in Biot’s coefficient from 0.9 (base model value) to 0.1 resulted in lower

flowing fracture lengths of the heel clusters, middle clusters, and toe clusters at rates of

-44%, -32%, and -39%, respectively.

6. Overall, the average rate of improvement in flowing fracture length was more

pronounced at the heel and middle clusters, whereas the average rate of deterioration

in flowing fracture length was more evident in the toe clusters. This is attributed to the

fact that the toe clusters are subject to larger stress shadow magnitudes as they are

closer in distance to the previous fracturing stage. This larger stress shadow impact

hinders further enhancement of flowing fracture length.

7. The observed changes in flowing fracture length as a result of changing reservoir and

geomechanical parameters implies that running engineered completions in horizontal

wells can be important to better optimize hydraulic fracture staging design, if effective

stimulation can be achieved.

8. Scenario 1 of the simulated spacing scenarios (tight cluster spacing of 57 ft and 84

perforation clusters) resulted in the largest fracture network volume (656 ft3) among

the four simulated scenarios. However, the average cluster contributions based on

fracture conductivity were 56%, 29%, and 15% for the heel clusters, middle clusters,

and toe clusters, respectively.

9. Scenario 4 of the simulated spacing scenarios (wide cluster spacing of 142 ft and 35

perforation clusters) resulted in the smallest fracture network volume (605 ft3) among

132

the four simulated scenarios. The average cluster contributions based on fracture

conductivity were 36%, 28%, and 36% for the heel clusters, middle clusters, and toe

clusters, respectively.

10. Overall, larger cluster spacing resulted in a smaller fracture network volume but more

conductive fractures with higher amounts of total placed proppant and more uniform

contribution to production from the perforation clusters.

11. Productions forecast runs showed that scenarios with more perforation clusters showed

higher cumulative oil production which implies that reservoir contact (created fracture

network volume) is more influential on production than fracture conductivity for the

studied case. This may not be always the case in other reservoirs where fracture

conductivity could be as important as volume of created fracture.

12. Simulation of different fracturing fluid and proppant types showed that the fluid or

proppant type that created larger fracture height growth resulted in higher cumulative

oil production in the simulated period of 30 years. Fracturing fluid 50# CMHPG-Zr

(best case) showed 135% higher 30-year cumulative oil production than fracturing fluid

45# Guar-Borate 2 (worst case). Sand A (best case) forecasted 25% higher 30-year

cumulative oil production than Ceramic B (worst case).

13. The density of natural fractures calculated from image logs can be a sign of reservoir

quality. When compared to the DAS hydraulic fracture profiling results, stages with

zero natural fracture density (Stages 9 and 12) showed a normal behavior of cluster

contribution with heel clusters being the dominant ones which means they were only

affected by stress shadow interference. Other stages, however, did not show that

133

behavior as they were affected by both stress shadowing and reservoir quality lateral

variations.

It is worth noting that the stated conclusions were based on the simulated and studied data

set and may not be applicable to any other data. Overall, these conclusions can be utilized by the

industry to evaluate the effectiveness of running engineered completion (horizontal logs) and the

impact of stress shadowing on production in unconventional reservoirs.

5.2 Recommendations

Based on the achieved outcome of this study, the research work can be further expanded

in the future as per the following recommendations:

1. Expand the project to a reservoir-wide basis by constructing a reservoir flow model and

including more wells and reservoir features such as natural fractures to help optimize

the accuracy of fracture leakoff parameters (PDL coefficient and transverse storage

coefficient) as well as fracture leakoff to the matrix.

2. Horizontal well log data can be included, as available, to provide a more deep study

about the effects of lateral variations on stimulation results and well performance.

3. Incorporate wells that applied engineered completions to the study in order to evaluate

its effectiveness in characterizing reservoir lateral variations and ultimately optimizing

hydraulic fracture spacing.

4. Study seismic inversion attributes to characterize reservoir lateral variations and

compare the results with those from engineered completion logs.

5. Build an economic model that accounts for the cost of adding more fractures to allow

for a better comparison between the different hydraulic fracture spacing cases.

134

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APPENDIX A

MATCHED TREATING PRESSURE PLOTS

Figure A.1 Base case Stage 1 treatment data with a matched pressure between model and actual

values. Dotted pressure curve represents the actual surface treating pressure (surface pressure in

the plot legend), whereas the connected pressure curve represents the model surface treating

pressure (well pressure in the plot legend).

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APPENDIX B

PREDICT-KTM INPUT DATA

Table B.1 Reservoir Properties Input into Predict-KTM Production Model

Reservoir property Value

Fracture drainage area 1.75 acres

Formation compressibility 8E-6 1/psi

Formation thickness 200 ft

Reservoir pressure 9500 psi

Permeability 0.000268 mD

Total porosity 0.07

Water saturation 0.35

Young’s modulus 5.50 MMpsi

X offset 0.50

Y offset 0.20

Aspect ratio 1.00

Table B.2 Well Properties Input into Predict-KTM Production Model

Well property Value

Wellbore radius 0.325 ft

Tube inner diameter 4.67 in

Tube length 15900 ft

Absolute pipe roughness 0.0006 in

Kv / Kh 0.01

Well temperature 70 ºF

Bottomhole temperature 311.8 ºF

Oilwell GOR 2000 scf/bbl

Lateral length 4928 ft

Lateral vertical position 0.5

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Table B.3 Fluid Properties Input into Predict-KTM Production Model

Fluid property Value

Oil compressibility 2.475E-5 1/psi

Water compressibility 3.3E-6 1/psi

Oil formation volume factor 1.42 bbl/STB

Oil viscosity 0.13 cp

Oil gravity 48.4 API

Gas specific gravity 0.74

Gas CO2 fraction 0.0229

Gas N2 fraction 0.0004

Gas H2S fraction 0

Table B.4 Fracture Properties and Model Parameters Input into Predict-KTM Production Model

Fracture properties

Fracture property Value

Closure gradient 0.76 psi/ft

Model parameters

Model parameter Value

Maximum oil rate 10000 STB/day

Number of stress cycles 5

Total production time 11000 days

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