Download - Updated Rock Type Definition and Pore Type Classification of a Carbonate Platform, Tengiz Field, Republic of Kazakhstan

Carbonate petrophysical rock typing: integrating geological

attributes and petrophysical properties while linking with

dynamic behaviour

MARK SKALINSKI1* & JEROEN A. M. KENTER1,2

1Chevron Energy Technology Company, 6001 Bollinger Canyon Road,

San Ramon, CA 94583-2324, USA2Present address: ConocoPhillips, 600 N Dairy Ashford, Houston, TX 77079, USA

*Corresponding author (e-mail: [email protected])

Abstract: Carbonate rock typing provides a vehicle to propagate petrophysical properties throughassociation with geological attributes and, therefore, is critical for distributing reservoir properties,such as permeability and water saturation, in the reservoir model. The conventional approaches torock typing have significant gaps in incorporating diagenetic processes, transferring rock typesfrom core to log domain, accounting for fractures and using appropriate methodology to realisti-cally distribute rock types in the static reservoir model. The workflow proposed in this paperaddresses these issues in a comprehensive way by determination of petrophysical rock types(PRTs), which control static properties and dynamic behaviour of the reservoir, while optimallylinking to geological attributes (depositional and diagenetic) and their spatial interrelationshipsand trends. This approach is novel for the fact that it: (1) integrates geological processes, petrophy-sics and Earth modelling aspects of rock typing; (2) integrates core and log scales; and (3) providesa flexible ‘road map’ from core to 3D model for variable data scenarios that can be updated withprogressive changes in data quality and quantity during the life cycle of an asset. This paper intro-duces the rationale behind this workflow, and demonstrates its workings and agility throughdeployment in two large carbonate fields.

Carbonate rock typing drives the quality of the dis-tribution of petrophysical parameters in three-dimensional (3D) Earth models and is fundamentalto reservoir characterization. Despite its recognizedimportance, the oil industry is lacking a commondefinition and standards for carbonate rock typing.If the goal of carbonate rock typing is to properlyand realistically distribute log-derived reservoirproperties in 3D models, and to generate a spatialdistribution of appropriate rock types that controloil-in-place and fluid flow, then most of the existingrock typing definitions are not meeting this goal.The main reasons for this gap are as follows:

† Depositional bias, which assumes that deposi-tional facies are adequately representing reser-voir properties, while, in fact, most carbonatereservoirs are strongly influenced by diageneticmodification. Even when diagenetic modifi-cation is properly noted and included in the rock-typing step, it is often ignored for reasons ofcomplexity in the spatial distribution stage,which is usually driven by depositional trends.

† Lack of integration between rock type defini-tions in the core domain and determinations inthe log domain, which are required to popu-late static models. The current modelling prac-tice is, still, suffering from a common lack of

knowledge and expertise at the interface ofgeology and petrophysics

† Lack of a proper definition of the spatial pro-perties of petrophysical rock types, resulting inobscuring the spatial definition of rock typesand, hence, their distribution in the interwellspace. The relationship between petrophysicalproperties (such as permeability and water sat-uration) and geological attributes (such as depo-sitional texture, diagenetic modification, andtheir spatial trends and juxtaposition) are poorlydocumented and/or understood, while no ade-quate diagenetic analogues from outcrop or sub-surface are available in the public domain.

† Biased focus on reservoir properties while ignor-ing dynamic data. Most of the rock typing studiesare focused only on reservoir rock, ignoring per-meability barriers/baffles that are important forthe flow modelling and definition of the flowunits in reservoir models.

Another consideration is that of scale. Rock typesare scale dependent and can be defined at: (1) thepore scale using dual-energy computerized tom-ography (CT) scan data (Walls et al. 2013); (2) thecore-plug scale using mercury injection capillarypressure (MICP) data, routine core analysis (RCAor conventional core analysis, CCAL) data and

From: Agar, S. M. & Geiger, S. (eds) Fundamental Controls on Fluid Flow in Carbonates.Geological Society, London, Special Publications, 406, http://dx.doi.org/10.1144/SP406.6# The Geological Society of London 2014. Publishing disclaimer: www.geolsoc.org.uk/pub_ethics

at ConocoPhillips on May 30, 2014http://sp.lyellcollection.org/Downloaded from

http://sp.lyellcollection.org/

petrographical thin sections (Amaefule et al. 1993;Marzouk et al. 1998; Cantrell & Hagerty 2003;Masalmeh & Jing 2004; Gomes et al. 2008; Holliset al. 2010); (3) the log scale (Cuddy 1998; Ye &Rabiller 2000; Sagaaf & Nebrija 2003; Skalinskiet al. 2009); or (4) the flow-unit scale (Ebankset al. 1992; Gunter et al. 1997; Corbett & Potter2004; Cortez & Corbett 2005; Kazemi et al.2012). Several papers have made a serious attemptto link different scales and data types (i.e. Vahren-kamp et al. 2008; Zang et al. 2009); however, ifthe ultimate goal is to populate 3D static and dyna-mic models, then there is a scale challenge that hasnot, yet, been addressed in a rigorous manner. Theworkflow presented in this paper is integratingcore and log scale by defining rock types in thecore domain, and predicting/integrating in the logdomain. Further work is needed to develop rigor-ous multiscale approaches that also integrate theinterwell/seismic scale. Rock typing is situatedright at the interface between the disciplines ofgeology, petrophysics and engineering but gener-ally is published mostly in petrophysical journalsand out of sight of the petroleum geologist. There-fore, prior to introducing the new petrophysicalrock type (PRT) workflow, it is critical to reviewthe existing status quo in carbonate rock typingpractices and highlight those issues that the newworkflow attempts to mitigate. The present statusof carbonate rock typing can be summarized infour different schemes. Table 1 summarizes the dif-ferent schemes in terms of data needs, scale, andsuggested advantages and disadvantages that arecore for the rationale of developing the new rocktyping ‘road map’ that we present in this work.

Rock types based on mostly geological

(depositional) facies

Rock types here are defined as lithofacies andassociated attributes, what is commonly referred toas depositional rock types (DRTs). This includespure textural classes (Dunham 1962; Embry &Klovan 1971), generic pore types (Choquette &Pray 1970) or combinations of Dunham/Embry–Klovan classes with grain/pore size, which is rep-resented by the popular Lucia classification (Lucia1995). Such classifications relate to geologists’classification of geological attributes, and are rela-tively simple as they can be linked to depositionalenvironments and associated spatial distributions,but are often not relevant to fluid flow as a result ofdiagenetic modification. Rock fabric and inferredpore types (Lucia 1983, 1995, 2007) can provide alink between petrophysics and spatial geologicaltrends only if pore systems confirm the fabricelement definitions and are relatively uniform. Thisis only valid for a subset of carbonate reservoirs

dominated by depositional or early diagenetic poros-ity or where porosity modification resulted in mostlyfabric selective pore systems. However, in reservoirswith complex and multimodal pore systems, theclassification fails to resolve the defined fabricelements, and, as a result, fails to adequately char-acterize porosity–permeability relationships (e.g.Kenter et al. 2006; Johnson et al. 2010), hydraulicflow units (Bust et al. 2011) or reservoir heterogen-eity (Abbaszadeh et al. 2000). Although there areexamples of successful prediction of such fabric-based rock types in the log domain (i.e. Bagheriet al. 2005), this approach commonly lacks properprediction in the log domain in uncored wells/intervals. One of the underlying reasons for theLucia classification falling short in complex poresystems is the absence of capillary pressure in itsdefinition (Johnson et al. 2010; Chehrazi et al. 2011).

Rock types based on pore typing

Pore types defined using pore size and/or porethroat size distributions are better tied to the flowunits and matrix permeability, but they might nothave the spatial guidance or trends for properspatial distribution in 3D models. Examples arethe Winland R35 method (Kolodzie 1980), the par-titioning model by Ramakrishnan et al. (1999,2001), the pore throat classification by Marzouket al. (1995) and Skalinski et al. (2005), or the‘Rosetta Stone’ approach by Clerke et al. (2008).The Winland R35 is based on the pore throatradius corresponding to 35% of mercury (non-wetting phase) saturation in mercury injection capil-lary pressure (MICP) measurements as an indicatorof effective flow. Marzouk et al. (1998) partitionedthe carbonate pore system into three components(micropores, mesopores and macropores). Thethree components were adopted for porosity parti-tioning in carbonates and are defined by their porethroat radius as measured by MICP or air–watercentrifuge. However, the pore radii to define parti-tioning cutoffs for pore classes are not consistentbetween authors. For example, microporosity wasdefined as pore throats below 0.1 (Chekani &Kharrat 2009), 0.2 (Porras & Campos 2001), 0.3(Marzouk et al. 1998; Skalinski et al. 2009), 0.5(Arfi et al. 2006) or 2 mm (Hulea & Nicholls2012). Marzouk et al. (1998) related pore types tothe Dunham textures for a Middle East reservoirdominated by depositional attributes. However,Waravur et al. (2005) did not observe a satisfactorylink between MICP-derived pore types and lithofa-cies due to diagenetic modification. Clerke et al.(2008) utilized the relationship between perme-ability and Thomeer parameters (Thomeer 1983)and demonstrated that permeability is most stronglycontrolled by the largest pore modes (so-called

M. SKALINSKI & J. A. M. KENTER



Table 1. Summary and comparison of current rock typing schemes including the scheme proposed in this paper

Method/author Data* Scale Plus Minus

DRT basedDunham (1962) Core description Core Defines depositional

textureIgnores diagenesis and pore types

Lucia (1995, 2007) Core observations Core Link to depositionaltrends andpermeability

Ignores later diagenetic events andmicroporosity, large uncertaintyin perm prediction, inferred poretypes

Pore type basedChoquette & Pray

(1970)Core Core Genetic pore types No link to petrophysical properties

and spatial trendsMICP based (PTD,

R35, Pd)MICP Core Link to permeability and

dynamic dataPrediction of pore type in log

domain, ignores pore throatdiameter .140 mm, no link tospatial trends

NMR based NMR Log Log domain Pore size limitations, T2 might notrepresent pore body size

Lønøy (2006) Thin sections Core Pore size and texture,simple

No link to geological trends,semi-quantitative porosity

Ahr (2008) Thin sections Core Link to genetic processes Core domain only

IntegratedArchie (1952) Core observations Core Practical well site

integration of geologicand petrophysicalattributes

Based on ‘visible’ porosity, no linkto spatial trends

Hollis et al. (2010) DRT + DM + PT Core Integration of: DRT, DMand pore types. SCALvalidation.

Transfer to log domain, does notinclude fractures and the 3Dmodel

Salman & Sameer(2009)

DRT+ MICP Core Link to depositionaltrends and pore types

Diagenetic modification notincluded as defining factors

Partitioning – flow unitsAmaefule et al.

(1993)Core FZI Core Link to porosity–

permeabilitytransforms

No link to spatial trends, transfer tolog scale, fails when K and phido not conform

Cortez & Corbett(2005)

Core GHE Core Link to flow units inlarger scale

Poor link to geological trends,challenging prediction from logs

Gunter et al. (1997) Core or logporosity andpermeability

Core orlog

Link to flow units inlarger scale

Need continuous core profiles,does not include fractures

Wibowo & Permadi(2013)

Core RCA Core Link to permeability Core domain, assumesconformance of porosity andpermeability

Partitioning – log clustering or electrofaciesSerra & Abbot

(1980)Logs Log Easy to apply, link to

petrophysicsNo link to geological trends, driven

by input logs

Dynamic rock typesGhedan (2007) SSRT and

wettabilityCore Better dynamic control Need wettability model, not

practical to acquire dynamicSCAL data for rock typing

Gomes et al. (2008) DRT, DM andSCAL

Core Better control of the fluidflow

Only core domain

Petrophysical RockTypes (this paper)

DRT, DM, PT,barrier

Core andlog

Integration of DRT, DM,PT and barrier in logdomain, includes 3DEarth model, adaptableto various datascenarios

Not easy to apply

*MICP, mercury injection capillary pressure; NMR, nuclear magnetic resonance; DRT, depositional rock type; DM, diagenetic modifi-cation; PT, pore type; PTD, pore type distribution; FZI, flow zone indicator (Amaefule et al. 1993); GHE, generalized hydraulic elements(Cortez & Corbett 2005); RCA, routine core analysis; CCAL, conventional core analysis; SSRT, static reservoir rock types (Ghedan 2007);SCAL, special core analysis.

CARBONATE ROCK TYPING



porositons) corresponding to the entry pressure,which were used as rock typing classificationthresholds. Lønøy (2006) defined rock types by esti-mation of conventional pore size and texture fromporosity distributions, but a significant mismatchis observed when comparing resulting rock typeswith porositon-based classification on the samedataset (O. Karoussi, 2013, pers. comm.). Somestudies used MICP-calibrated nuclear magnetic res-onance (NMR) T2 cutoffs to define pore types(Frank et al. 2005; Arfi et al. 2006; Ramamoorthyet al. 2008). NMR is a relatively new (1990s) tech-nique that utilizes short-duration magnetic pulses toexcite and measure the relaxation time of hydrogenatoms, which largely depends on the pore size distri-bution in porous media. T2 is the transverse relax-ation time typically measured in boreholes andgenerally plotted, following inversion, v. thesignal distribution, while the area under the curverepresents the associated pore volume (T1 is thelongitudinal relation time). T2 cutoffs are userdefined and are assigned to ranges in pore sizeand/or type (isolated v. connected). However,NMR has limitations related to pore size (up to100 mm) and the assumption of lack of diffusioneffects. If diffusion effects are strong and poresize is larger than 100 mm, T2 distributions are nolonger considered representative of the pore bodydistribution. Prediction of pore types in the logdomain and uncored intervals can create quite achallenge without a strong link to geological trends.Indeed, the observed pore typing approach in theliterature is generally lacking spatial guidance andhas scaling problems when integrating secon-dary pore types (vugs and fractures). Despite thoseobserved challenges, pore types are the most criticalelement of rock typing due to their strong control onthe fluid flow in carbonates (Sung et al. 2013).

Rock types based on integration of

depositional facies, diagenetic modification

and pore types

Here the goal is to define rock types by integrat-ing depositional lithofacies with diagenetic modifi-cations and pore types. In reality, most of the rocktyping is heavily biased towards depositionalfacies (or lithofacies) that are integrated with poretypes from MICP. Diagenetic modifications aretherefore captured indirectly since there is a strongrelationship between diagenetic processes and poretypes. Spatial trends and modelling are driven bydepositional trends from analogues and core dataobservations, and, in general, a properly and system-atically documented effort to distribute ‘diageneticbodies or trends’ is lacking. Numerous studies(e.g. Rebelle et al. 2005; Lehmann et al. 2008;

Basioni et al. 2008; Salman & Sameer 2009) inte-grated depositional lithofacies with MICP-derivedpore types. However, the distribution in the result-ing models is generally guided by depositionaltrends, distributed within a sequence stratigraphicframework (SSF), but without any clear explanationas to what extent diagenetic modification controlsthe pore throat distributions and rock types.Several studies (Francesconi et al. 2009; Skalinskiet al. 2009) reported the lack of a significantrelationship between lithofacies and petrophysicalproperties and defined rock types based on diage-netic features. Rock type population in the lattermodel was guided by conceptual diagenetic trendsand multiple point statistics (MPS) pixel-basedmodelling. The widely known Archie (Archie 1952)classification scheme is a hybrid approach that com-bines textural (grain size) and petrophysical (poresize) parameters. Archie made a first and successfulattempt to relate texture and pore types to petrophy-sical properties in carbonates. His classificationprimarily estimates porosity, but it also estimatespermeability and capillary properties by subdivid-ing pore space into (invisible) matrix and visibleporosity. Archie classes proved to be useful inwell site classification of cuttings, but they arelacking a link to depositional or diagenetic pro-cesses that have more complex pore networks.Hollis et al. (2010) defined rock types by partition-ing depositional lithofacies on MICP-derived poretypes and validated those, basically, core-definedrock types with relative permeability measurements.This integrated and comprehensive definition ofrock types in core domain, however, lacks an ade-quate prediction in the log domain.

Rock types based on petrophysical partitioning

of core or log data

This rock typing approach is using log clusters(combinations) known also as electrofacies (Serra& Abbott 1980; Wolff & Pellissier-Combescure1982) or core porosity–permeability partitioning onflow units (Amaefule et al. 1993). These methodsare linked to petrophysical properties but are lack-ing the critical link to geology and spatial rules.Serra & Abbott (1980) coined the term ‘electrofa-cies’, which basically captures the set of log res-ponses that uniquely characterizes a rock unit. Inreality, it is ‘log typing’ and was proven success-ful in several siliciclastic reservoir studies where‘natural ordering’ in lithofacies successions wascorrelated with petrophysical ordering observed inthe log domain. Using reservoir quality index(RQI) and flow zone indicators (FZI), rock typesare classified through artificial binning of the poros-ity–permeability space (Amaefule et al. 1993).




A similar approach, also based on the Kozeny–Carman model (Carman 1939), was proposedby Wibowo & Permadi (2013). Corbett & Potter(2004) and Corbett (2010) to expand the FZI con-cept, by introducing generalized hydraulic ele-ments (GHEs) that improve the link to geologicalfacies. Although such approaches are more practi-cal and efficient in carbonates with complex poresystems and strong diagenetic modification, thelink between log response and geology is generallyrather weak and leads to inaccurate spatial predic-tion and proportions of resulting rock types. Thisgroup of methods, which includes multi-resolutiongraph-based clustering (MRGC) (Ye & Rabiller2000), is also strongly dependent on the selectionof input logs for the partitioning process. Rocktyping becomes, in essence, an exercise of ‘logtyping’ with an arbitrary selection of logs with therisk of ignoring meaningful geological influence.FZI or electrofacies concepts were applied withvariable degrees of success in reservoir models(Guo et al. 2005). The measure of success wasrelated to the goodness of prediction of core-derivedFZI from logs and/or the strength of the linkbetween FZI, GHE or electrofacies and geologicaltrends. Those links and therefore the resulting pre-dictions are stronger in siliciclastic reservoirs andcarbonates with predominant depositional control.These methods remain to be challenged by carbon-ate reservoirs with strong diagenetic modification.

Static or dynamic rock types

A final consideration in the rock typing method-ology is related to the goal of rock typing. If thegoal is to properly populate static Earth modelswith porosity, permeability and water saturation,the static rock types based on the integration ofdepositional facies, diagenetic modifications andpore types will be adequate. If the goal is to populaterelative permeability and capillary pressure curves,then they might not be appropriate for all reser-voir types (i.e. Hamon 2003; Masalmeh & Jing2004; Ghedan 2007). Gomes et al. (2008) extendedthe definition of rock types to include fluid–rockinteraction/wettability from special core analysis(SCAL). His final reservoir rock types, therefore,are not only a function of reservoir properties butalso of fluid content and the effect of fluid–rockinteractions. Hamon (2003) and Masalmeh & Jing(2004) examined static and dynamic parametersderived from core plugs and concluded that sim-ple static rock types based on RCA or CCAL data(porosity, permeability and water saturation) anddrainage capillary pressure might be not adequateto capture dynamic impact resulting in differentresidual oil saturation (Sor) and relative permea-bility. Static rock types are not adequate for the

mixed wettability rocks under water injection. Der-naika et al. (2012) reported results from relative per-meability drainage and imbibition curves measuredfor static rock types and found coherent results vali-dating initial rock types. Ghedan (2007) and Gomeset al. (2008) reconciled differences between staticrock types and dynamic rock types. They proposedan approach of generating dynamic rock types byimposing wettability or fluid model on the staticrock types. This superposition can be performedon the upscaled simulation model. Wettability dis-tributions can be controlled by a position in thehydrocarbon column (Ghedan 2007; Al Jenaibiet al. 2008) or pore type (Marzouk et al. 1998). Gen-erating dynamic rock type based on relative per-meability data is impractical since SCAL data onpreserved samples are costly, sparse and not fullyrepresentative. SCAL measurements can be per-formed to define/validate relative permeabilityand saturation profiles for final rock types in thedynamic model.

This paper describes a new workflow that intendsto address – and mitigate – the gaps specified aboveand define petrophysical rock types (PRTs) whichaccount for both depositional and diagenetic pro-cesses, are predictable from logs in uncored wells,and can be distributed in a geologically realisticway in 3D Earth models. This workflow is designedas a general road map for carbonate rock typing anddoes not pretend to solve all of the technical chal-lenges related to this process.

Petrophysical rock type (PRT) – workflow

The workflow consists of eight composite andsequential steps, which are represented by thediagram shown in Figure 1. With progressing fieldmaturity and data scenarios, multiple loops arerequired to capture reservoir heterogeneity and tooptimize the representation of the subsurface data(see Fig. 1). The final product is the result of Step8; that is, the 3D static PRT model or series ofmodels capturing recorded uncertainties. Belowfollows a brief description of each of the workflowsteps.

Step 1: Data scenario

PRT workflows are designed to be adaptable to alldata scenarios that are driven by (in order of rel-evance): (1) well density; (2) logging surveys (vin-tage and completeness); (3) available core data;and (4) dynamic data. Representativity is anothercritical parameter linked to each of these datatypes. Representativity is typically an uncertaintythat decreases with data collection during the lifeof a field but is very difficult to estimate, in




particular near the start of a project, and its pro-gression very much depends on the understandingof the reservoir quality distribution. Generally, withfield development, the data scenario will change.This progression is shown in Figure 1, where differ-ent loops of the workflow represent different datascenarios and reservoir maturity. Data scenariosinclude those for prospects, field appraisal and pro-ducing fields, although the lack of core (prospects)will change the workflow towards a log-drivenapproach.

Step 2: Depositional rock typing

The depositional rock type (DRT) Step is designedto define a DRT catalogue in the core domain andto predict lumped DRTs in the log domain. If thesubsequent Step 3 indicates a strong control offlow by DRTs, the predicted DRTs will be used asa primary input into the PRT definition. Step 2 con-sists of three substeps. The DRT determination(Step 2.1; Fig. 2a) requires an unbiased determi-nation of depositional and diagenetic attributes asa function of depth along the cored intervals.Unbiased refers to a DRT determination approach

that is exclusively based on geological attributeswhile ignoring any petrophysical information fromRCA/CCAL or logs, as inclusion of such infor-mation will bias the DRTs towards petrophysicalrock typing and obscure the primary purpose ofthis test. These geological attributes allow the gen-eration of a set of DRTs, which represent categoriesof non-overlapping lithofacies as well as a sepa-rate set of diagenetic attributes that may be usedlater in the workflow to explain the disconfor-mity between reservoir properties and DRTs. TheDRT catalogue summary classification (Step 2.2;Fig. 2a) represents the DRT elements of a deposi-tional model based on core observations and con-cepts from literature and/or analogues. The DRTcatalogue includes one or more alternate scenariosthat combine DRTs according to geological criteriasuch as, for example, depositional regions and faciesbelts to a statistically acceptable number for predic-tion using logs. Generally, no more than 15 DRTsare lumped into a maximum of five DRT associ-ations. DRT prediction from logs (Step 2.3) requiresiterative ‘lumping’ of the DRTs determined fromcore (Fig. 2b). Lumping should follow geologicalassociations, similarity in petrophysical space and

Fig. 1. Schematic diagram showing the PRT workflow, which consists of eight composite and sequential steps thatgradually build to the definition, determination and distribution of PRTs in a static model. The workflow represents aroad map that can handle different data scenarios (data quality and quantity, and representativity) ranging from prospectto appraisal to development. The second, empty loop illustrates that with progressing field maturity and data scenariosmultiple loops are required to capture reservoir heterogeneity and optimize the representation of the subsurface data.




statistical representativity of DRTs in the coredescriptions. The diagram to the right in Figure 2bshows three successive lumping steps on the DRT

set on the left. Prediction of DRTs from logs is gen-erally performed using multivariate statistical tools(such as stepwise discriminant analysis or principle

Fig. 2. Set of diagrams illustrating workflow Step 2. (a) Simplified workflow showing, when core is available,DRT determination, classification and lumping, and prediction steps. (b) Progression of three successive lumpingsteps on the DRT set shown on the left. (c) Graph displaying the progression of prediction scores corresponding tosuccessive DRT lumping steps shown in (b). (d) Cross-plots showing the general improvement of the prediction score asa function of the number of logs used for the prediction (left), and the influence of the quality and type of logs used on theprediction score (right). This clearly warns that care should be taken not to push for a high prediction score but for ageologically acceptable number of representative DRTs.




component analysis), nearest neighbour method,supervised clustering or neural networks combinedwith deterministic methods. Figure 2c presents pro-gression of prediction scores corresponding to suc-cessive DRT lumping steps shown in Figure 2b.The diagram in Figure 2d shows the generalimprovement of the prediction score as a functionof the number of logs used for the prediction.The prediction score will also depend on thequality and type of logs used, and, therefore, thedetermination of the optimal combination of pre-diction logs is critical. These two observed trendsalso signal a warning that while the predictionsscore may increase with progressive lumping andinclusion of more logs (when available), this doesnot necessarily mean that the resulting DRT set isstill maintaining integrity in terms of their geologi-cal (depositional and diagenetic) definition. As arule, it is critical that lumping maintains the criticalgeological definition, since if that is lost the test willlose its significance. The resulting degrees of suc-cess for the final set of lumped DRTs provide impor-tant information for the PRT definition described

in workflow Step 5 (i.e. a tie to depositional and/or diagenetic processes that may require furtherstudy, and potential spatial rules and trends for dis-tribution in the static model).

Step 3: Reservoir typing – the effect of

diagenesis

Carbonate reservoirs are highly susceptible to dia-genetic processes that alter their original depo-sitional fabric and petrophysical properties. Inaddition, diagenetic, tectonic and depositional frac-tures can overprint both type of systems. The pro-cess of reservoir typing (RT) is the determinationof the relative contribution on fluid flow by thedegree of diagenetic modification and high-K zones(diagenetic, depositional and tectonic fractures,and well-connected high-permeability stratigraphiczones) on the original DRTs. The outcome of thisstep would be classification of reservoir using fourRT categories: (1) RT1, when PRTs and flow arecontrolled by DRTs; (2) RT2, when flow is

Fig. 3. Set of diagrams illustrating workflow Step 3. (a) Flow diagram with four steps leading to the assessment ofreservoir type (RT). Note that not all data types are necessarily available to reach an RT definition and, with progressionof data availability, the RT may even change. However, for the current workflow, it is critical to have a ‘best’ assessmentto define and determine a first set of PRTs. The four resulting RTs represent combinations of the relative influence ofdepositional, diagenetic control on flow and that of high-K features such as conductive fractures or karst horizons.




Fig. 3. Continued. (b) Schematic diagram visualizing the definitions from (a) in a ‘qualitative’ ternary diagram thatshows the relative contribution to flow in PRTs of diagenetic modification, depositional attributes and high-K pathways.The stars represent plotted positions for the First Eocene and Tengiz platform and margin (modified after Ahr 2008).(c) Porosity–permeability (phi–K) cross-plot for 15 Tengiz platform wells with textures displayed as colours. Thedominant texture in the platform is grain-supported skeletal sand with minor contributions of mud-supported(wackestone–mudstone). Texture has a weak control on porosity–permeability trends; maximum permeability spansover one order of magnitude, which is in the range of the uncertainty in the permeability prediction.




Fig. 3. Continued. (d) Petrographical images confirming the predominance (..33%) of diagenetically altered poresover primary depositional pores, as well as the complex multimodal pore system (modified after Kenter et al. 2006). (A)Overpacked ooid grainstone with minor blocky calcite and bitumen occluding solution-enhanced interparticle porosity.(B) Moldic porosity showing evidence for enhanced dissolution shown by corrosion of the mold boundaries, nearlybreaking through at contacts and corroding earlier cement. (C) Moldic porosity in its early phase: micrite grains aregradually being dissolved following partial fill of interparticle porosity by equant to rhombic spar cement. (D) Advancedstage of enhanced dissolution of moldic and interparticle porosity (and minor intraparticle) leading to the destruction ofcement bridges and the development of vugs. (E) Skeletal and coated grain grainstone dominated by interparticleporosity with minor cement. (F) Example of microporosity, pore size smaller than 25 mm, developed in grains. (G)Intraparticle porosity developed in benthic foraminifera. (H) Skeletal and peloid grainstone with rims of equant sparlining interparticle pores and minor moldic porosity. Width of photomicrographs is 4.20 (A, B, E, F and G) and 1.58 mm(C, D and H).




controlled by diagenesis and truncates DRT trends;(3) RT3, when flow is controlled by the fracturesystem; and (4) a hybrid class, RT4, when flowhas mixed controls (Fig. 3a). The triangle inFigure 3b visualizes this classification with the pos-ition of reservoirs discussed in the ‘Examples’section. This ternary graphic is similar to thegenetic pore type classification of Ahr (2008), butwith a different objective (flow control and reservoirtyping v. pore types) and with a different definitionof the hybrid case. There is a link between RT andpore types captured by predominant pore systemsin each reservoir class. Considering the variabledata scenarios (data quality, quantity and represen-tativity), an assessment of RT at this stage of theworkflow (and, in many cases, field development)is a best guess only. However, even a best guess atthis point in the process is important for concep-tualizing the first-order controls and trends on thereservoir quality distribution (depositional v. diage-netic and role of high-K zones), and the subsequentplanning of further investigations to converge onthese controlling parameters. Several methods areavailable to provide estimates for RT, each withparticular uncertainties that mostly depend on thedata scenario: (1) divergence–convergence of poro-sity– permeability transforms; (2) predictability ofDRTs using logs; (3) contribution of diageneticallyaltered pores; (4) flow regime from productionlogging tools (PLT) and/or the drill stem test(DST); and (5) fractures from borehole image logsand drilling data. Each of these methods is dependenton the data scenario and has particular limitations.Porosity–permeability regression lines by DRTsare generally closer spaced when controlled by dia-genesis but are highly dependent on the representa-tivity of core data within each DRT. Figure 3cshows an example of conformity of porosity–per-meability transforms in a cross-plot using datafrom 15 wells in the Tengiz platform. Transformsfor different textures are very closely spaced andall fall within the range of the uncertainty in the per-meability prediction, suggesting a very weak controlby texture (or DRTs). The prediction score of(unbiased) DRTs using logs will generallyimprove with a reduction (lumping) in the numberof DRTs (Fig. 2d), the quality and quantity of log-ging suites, and the prediction method used (neuralnetworks or nearest neighbour over linear discri-minant analysis). Ahr (2008) emphasized the rel-evance of estimating the diagenetic alteration ofthe pore system from petrographical thin sectionsas an estimate for the contribution of diagenesison reservoir quality. Ahr (2008) provides a detailedoverview with excellent examples of the variousdiagenetic processes, their products, and criteriafor recognition and estimation that can be used inthis context rendering a thorough synthesis from

the scope of this work. Figure 3d shows examplesof the highly complex and heterogeneous, multimo-dal, pore system in the Tengiz platform that is theresult of modifying mineralogy and pores throughmultistage and stacked diagenetic dissolution andcementation (Kenter et al. 2006; Dickson pers.comm. 2012). As a consequence, pore typing attem-pts following Lucia (1995, 2007) and Lønøy(2006) failed to identify single dominant fabricsand associated pore types, but, instead, confirmedthe dominance (..33%), intimate juxtaposition,and mixing of diagenetically altered pores (Kenteret al. 2006). Similar observations have been notedin other studies (e.g. Johnson et al. 2010). Althoughbiased by the individual observer, such estimatescan be used with caution as yet another methodto define RT. Finally, when available, PLT profilesand DST build-ups can be examined to identify frac-ture flow behaviour, while borehole image logs (i.e.formation micro-imager, FMI) or drilling data (fluidlosses, rate of penetration (ROP)) identify fractures.Even though each of those methods has issues withdata scenarios, they do have merits when usedtogether as an (best guess) estimate of the RT forreasons explained earlier. Figure 3a shows the inte-gration of the methods above in a flow diagram thatprovides a first estimate of the RT, which directlyinfluences further attempts to delineate reservoirquality trends and patterns, and steps to define rocktypes and their controls. To achieve the RT, fivesteps are required in an ideal data scenario thatincludes application of the four methods discussedabove and the determination of the RT assignmentbased on the relative contribution of the RT indi-cator methods above, see Figure 3b. The RT step isincluded in the workflow for two main reasons.First, to prioritize flow control and define the nest-ing scheme for the PRT definition in Step 5, and,second, to frame the PRT distribution in the 3Dmodel using methods described in Step 8. The firststep will allow, when present, the definition of PRTsthat host certain high-K phenomena, like deposi-tional fractures in tight marginal microbial bound-stone or high-K stratigraphically bound zones withconnected vugs resulting from extensive meteoricleaching.

Step 4: Pore typing

Carbonate petrophysical heterogeneity (flow prop-erties) is generally the result of complex and multi-modal pore systems including vugs and fractures.Identification and prediction of pore types is there-fore essential for reliable rock typing in carbon-ate. The pore typing workflow (Fig. 4) accountsfor different data scenarios depending on the avail-ability of core, MICP data, digital image analysis(Weger et al. 2009) or other pore characterization




measurements (e.g. NMR, nuclear, gas adsorption),and speciality logs such as nuclear magnetic reson-ance (NMR), ultrasonic borehole imager (BHI)or FMI (Fig. 4). Appropriate pore type identifi-cation can be best performed using MICP data,which provides accurate information on pore throatdistributions controlling flow in the reservoir. MICPderived initial pore throat types (IPT) have to becombined with larger scale observations such asvugs and fractures from specialty logs. In the caseof data scenarios lacking MICP data but includingavailable representative core, an alternative path(B) is to be followed that produces core pore types(CPT). CPT can be defined using other data types(i.e. digital image analysis (DIA)) or conventionalclassification such as Choquette & Pray (1970),but are limited to the pore type and size, and donot provide information on the pore throats. WhenNMR and/or BHI/FMI data are not available,pore types could be defined from IPT or from CPTdata. In the case when neither core nor NMR/BHI/FMI data are available, the determination ofrelevant pore types is less reliable. In that scenario,pore types can be derived from cuttings (MICP, pet-rographic thin section image analysis or micro-CTscans) or from petrophysical analogues such asthose available in public or commercial carbonaterock catalogues.

Step 5: PRT Definition

In the context of this workflow, PRTs are definedas: (1) the category of rocks that are characterizedby specific ranges of petrophysical properties (e.g.porosity, permeability and water saturation); (2)exhibiting distinct petrophysical relationships rel-evant for the reservoir modelling (e.g. porosity v.permeability, water saturation v. capillary pressure);(3) identified by wireline or logging while drilling(LWD) logging surveys; and (4) linked to geologi-cal attributes such as primary texture or diageneticmodifications. In this critical step, PRTs are definedaccording to the relative influence of the followingattributes: permeability barriers, DRTs, diageneticmodifications and pore types. The permeabilitybarrier(s) are either non-reservoir rocks or low-permeability rocks that act as flow barriers orbaffles, as indicated by dynamic data. They can bea product of either depositional or diagenetic pro-cesses. In carbonates they can be represented byhard grounds, evaporite layers, zones with dispersedvolcanic ash, bitumen-tarmac layers, cementedzones or interbeds or laminations with a high shaleor volcanic ash contribution. In summary, PRTsare defined by combining elements such as the pre-dicted DRT, PT, barriers and other diagenetic modi-fiers affecting the log response. The combination

Fig. 4. Diagram showing the pore typing elements required for the determination and definition of the PRTs. Similar toother steps in the workflow, the pore typing step is flexible and accounts for different data scenarios and integrates poretypes from different scales and measurements, such as pore types derived from MICP, digital image analysis (DIA),NMR, gas absorption, nuclear, high-resolution micro-CT or industrial whole core CT, and speciality logs such as NMR,acoustic borehole images (BHI) or formation microscanner (FM).




order is defined by RT (see Fig. 5), which is definedin Step 3 (see Fig. 3a). The definition of PRTs has totake into account the logging data scenario; that is,the ability to predict PRTs from logs in the majorityof wells. The PRT definition at the beginning of thissubsection contains the primary criteria used todefine PRT; the final PRTs have to conform to allfour segments of the definition.

Step 6: PRT determination in a multi-well

setting and quality control using maps

This step includes the determination of PRTs in allwells using algorithms for PRT prediction fromlogs developed in the previous steps (see Step 2and Fig. 2c, d). These algorithms are designed topredict lumped DRTs (Step 2), pore types (Step4), permeability barriers (Step 5) or diageneticmodifications (Step 3). Usually a master programis designed to predict PRTs in a logical successionas defined in Step 5. For example, if we have RT1,the PRTs could be determined by predicting DRTsfirst and distributing pore type based groups nestedwithin the DRTs (see the Wafra Field example inthe next section on ‘Examples’). The resultingPRT logs are mapped across the field in all (coredand uncored) wells using kriging (or comparable)techniques. Following an assessment (and possibleremoval) of outliers, spatial trends and relationshipsbetween PRTs are extracted and used as input

to designing the distribution approach in the staticmodel (Step 8). This data interrogation step is extre-mely important to constrain PRT patterns andtrends, and juxtaposition rules that need carefulcontrast and comparison with the geological obser-vations from Steps 1–4. This step is especially criti-cal when RT is greater than 1 and dominated bydiagenetic modification since little or no informa-tion on spatial rules of diagenetic processes is avail-able in the literature, and the user may be limitedto forward modelling of diagenetic processes (forreferences, see Step 8). The PRT determinationfollows three steps: (6.1) development of algorithmsor models for PRT prediction; (6.2) PRT determi-nation in all wells; and (6.3) quality control (andtrend mapping and extraction) (see Fig. 6).

Step 7: Dynamic validation of PRTs

A quantitative validation of the link betweenPRTs and available flow indicators is performedby: (1) comparing PRTs with core RCA data (suchas porosity–permeability cross-plots and modifiedLorenz plots (Gunter et al. 1997); and (2) thecomparison of PRTs with dynamic data such asPLTs, DSTs, wireline formation test (WFT) dataor injection profiles. The goal of this step is toconfirm that PRTs are linked to flow profilesobserved in dynamic data (Fig. 7). In particular, bar-riers and flow zones should be positively correlated

Fig. 5. PRTs are defined by combining elements including the predicted DRT, pore type (PT), barriers and otherdiagenetic modifiers affecting the log response. Their combination order is mapped to the four reservoir types that weredefined in Figure 3a. See the text for more discussion.




to appropriate PRTs. In case the linkage is poor, aloopback to Step 5 is required. Definition of rejec-tion criteria is field specific and depends on thetype and reliability of dynamic data. If dynamicdata are available, the workflow includes the follow-ing three steps: (7.1) validation of PRTs with coredata; (7.2) validation of PRTs with dynamic data;and (7.3) PRT conformance assessment. This stepmight be skipped if reliable dynamic data arenot available.

Step 8: 3D PRT realization and spatial

validation

This step comprises the completion of the spatialtrends and interrelation rules for PRTs extracted inStep 6. Those rules and trends can be used as softconstraints (probability maps) and/or to designtraining images or variograms for the distributionof PRTs in 3D static models. Trend maps can begenerated in many of the existing geomodellingplatforms and are generally labelled as facies pro-portion maps. These PRT proportion maps can bemanually modified to fit patterns and trends fromconcepts or forward models in case the data den-sity is low. Depending on the sequence stratigraphiccharacter of the grid layering (i.e. packages of layersthat represent transgressive systems tracts (TST),highstand systems tracts (HST), lowstand systemstracts (LST) or regressive wedges (RST), or any

other preferred diagenetic zonation scheme), onecan choose to generate PRT maps per selected inter-val. Obviously, data density may limit the optionsduring this phase of investigation. The resultingmaps serve as input to a (so-called) facies prob-ability cube or for soft conditioning during thestatic modelling step. The distribution method isalso a function of RT and data scenario. In otherwords, spatial patterns of – and interrelationshipsbetween – PRTs are controlled by the relative con-tribution of depositional v. diagenetic processes(Fig. 2). In case RT ¼ 1, PRTs and flow are con-trolled by DRTs. In addition to RT, the datadensity also controls the choice of methods to distri-bute PRTs in the static model. With increas-ing density of data, the spatial trends are driven bywell control rather than concepts. Multiple pointstatistics (MPS) is a pixel-based technique and isone of the more powerful geostatistical tools tohonour and control proportions and trends of –and spatial interrelationships among – PRTs (i.e.Strebelle 2000, 2002; Strebelle & Zhang 2004;Caers 2005). Examples of PRT definitions and dis-tribution using MPS are shown in the section on‘Examples’, later in this paper. High-resolutionand quality 3D seismic (attribute) data can be usedas a soft constraint for PRTs, especially for greenfields or intermediate fields where well density isnot capturing spatial heterogeneity. Seismic attri-butes can be built into the PRT definition follow-ing careful validation with log-derived acoustic

Fig. 6. The PRT determination is represented by three simple steps. The first step builds on algorithms developed duringStep 2 (DRT prediction) and Step 4 (pore typing), and supplemented with information on permeability barriers (Step 2)or diagenetic modifications (Step 3). In general, a tailor-made set of algorithms is captured in a master program designedto predict PRTs in a logical succession, as defined in Step 5.




Fig. 7. (a) In case reliable dynamic data are available, a quantitative validation of the link between PRTs and availableflow indicators is performed. This should include a comparison of PRTs with core RCA data and modified Lorenz plots,and PRTs with dynamic data. The ultimate purpose of this step is to evaluate the conformance between PRTs and flowprofiles observed from dynamic data. (b) Diagram showing an example of the application of Step 7 to Tengiz. Here aquick visual inspection confirmed that non-reservoir PRT1–PRT3 correspond to non-flow zones, while reservoirPRT4–PRT6 correspond to the inflow zones (green bars). CGR/SGR, total gamma ray minus uranium contribution andtotal gamma ray (CGR, when available, preferred over SGR); PHIE, effective porosity PRT, petrophysical rock type;PLT, production logging tools; PERM, permeability.




properties. In poor data scenarios, many other tech-niques can serve to reduce uncertainty in the finaldistribution of PRTs. For example, analogues fromoutcrops or the literature or forward stratigraphicmodelling tools can assist to reduce uncertaintyand to generate trends and juxtaposition patterns(or variogram design) in RT1 cases when datadensity is poor and not capturing heterogeneityand/or core is not representative of (anticipated)reservoir quality. Forward stratigraphic modell-ing platforms include SEDPAK (Kendall et al.1989), STRATA (Flemings & Grotzinger 1996),CARBONATE 3D (Bosence & Waltham 1990),REPRO (Hussner & Roessler 1996) and DIONISOS(Granjeon 1997). In data scenarios where diageneticmodification is dominant (RT2– RT3), diageneticforward modelling tools can be used to generatetrends and patterns for the associated PRTs. Suchsoftware platforms have been designed and pub-lished and are available to the user. Predictivediagenesis modelling techniques in carbonates arein their infancy. CARB3D+ is a forward simula-tion model for carbonate platforms developed bythe University of Bristol, which incorporates near-surface (eogenic) diagenetic processes and predictsthe distribution of diagenetic products (Whitakeret al. 1997). Reactive transport modelling (RTM)code, such as TOUGHREACT developed by theLawrence Berkeley National Laboratory forenvironmental studies, has been used in a varietyof carbonate studies both on outcrops and reservoirs,but requires a separately built ‘stratigraphic’ geocel-lular model as input to the reactor (Pruess et al.1999). Recent publications have clearly dem-onstrated their value in assessing the spatial dis-tribution of diagenetic products, and, hence, indesigning trends and patterns (e.g. Whitaker et al.1997; Jones et al. 2003; Jones & Xiao 2006; Pater-son et al. 2006; Garcia-Fresca 2009; Barbier et al.2012; Frazer et al. 2012). Ahr (2008) not onlyprovides a comprehensive workflow for assessingassociated pore types and systems, as well as thegenetic models of diagenetic modification, theworkflow also provides suggestions on their spa-tial trends. The application and use of forwardmodelling platforms varies with the data scenarioand specific geological demands, and cannot befurther detailed in this workflow. What is critical,however, is that certain PRTs have been designedto contain high-permeability (high-K ) elements intheir definition and, hence, distribution. This is rel-evant for further upscaling and design of PRTsand flow features in the simulation model. Inaddition to forward modelling depositional anddiagenetic spatial trends at the metre to hundredsof metres scales, promising results of 3D pore archi-tecture models have been obtained for modellingthe evolution of porosity and permeability as a

function of diagenesis at the sub-micron scale (e.g.Algive et al. 2012; van der Land et al. 2013). Dis-crete fracture networks can be distributed using con-cepts derived from analogues (e.g. Narr & Flodin2012) or predicted using mechanical Earth model-ling (MEM) or geomechanical forward modelling.These mechanical modelling techniques providedeformation and stress information that are usedto model fracture distributions (e.g. Xi et al. 2011;Abul Khair et al. 2013), as well as to allow esti-mation of the fracture behaviour (and flow) duringthe development of the field. Fracture flow proper-ties can be embedded in the PRT definition byreconciling the combined effect of matrix and frac-tures on porosity and permeability. However, such adecision is complex and depends on the observedcontribution to flow from dynamic data. Alternati-vely, fractures can be distributed as discrete objectsand co-located with PRTs (if desired) in the staticor dynamic model (i.e. Dershowitz et al. 1998;Bourbiaux et al. 2002; Jonoud et al. 2013). Sincethe modelling and distribution approach of fracturesin the static and/or dynamic model is very complexand receiving much discussion, such an extensivereview is excluded from this paper.

Figure 8 summarizes the discussion above on therelationship between data scenario, RT, and toolsto improve or optimize the distribution of PRT pat-terns and juxtaposition. In addition, it shows thepositions of the fields that are used in the following‘Examples’ section of this paper. The followingfour steps are included in the PRT distribution andspatial validation process: (8.1) determination ofPRT trends and spatial interrelationships assistedby forward modelling techniques and/or ana-logues; (8.2) determination of optimal geostatisticalmethod; (8.3.1) when applying stochastic geomo-delling algorithms, analyse variogram lengths, gen-erate the PRT probability cube and run the PRTdistribution; (8.3.2) in the case of MPS, design train-ing image(s) (TIs), generate the PRT probabilitycube and run the PRT distribution; and (8.4)quality control and evaluation of the resulting PRTdistribution and, if required, loop back.

Examples

The workflow described above was applied to thesupergiant Tengiz Field in Kazakhstan, as well asthe high-porosity First Eocene reservoir interval ofthe Wafra Field in the Partitioned Zone (Kuwait–Saudi Arabia).

Tengiz Field

The Tengiz Field is located near the NE shoreof the Caspian Sea in Kazakhstan (Fig. 9). It is




one of several large hydrocarbon accumulationsin the carbonate formations found around the edgeof the Pricaspian Basin (Lisovsky et al. 1992).

Tengiz is a stratigraphic trap consisting of a car-bonate build-up with a large central platform area(10 × 15 km), a raised rim area (up to 1–2 km

Fig. 8. Schematic diagram summarizing the relationship between data scenario, RT and tools to improve or optimizethe distribution of PRT patterns and juxtaposition following the discussion in the text. In addition, it shows positionsof the fields that are used in the ‘Examples’ section of this paper (see also Figure 3b). The diagram assists theintegrated reservoir management team by offering the appropriate combinations of PRT trends, data scenario and tools,and/or concepts that may help to optimize a realistic set of spatial distributions of PRTs to capture the uncertainty inreservoir quality and flow. Clearly, this is part of the road map and many deviations to those solutions shown abovewill exist.

Fig. 9. Simplified map showing the locations of the Tengiz Field and Wafra (First Eocene) Field.




wide), and is surrounded by an upper-slope micro-bial boundstone sector and a lower-slope debrisapron. The field is divided into platform and slopeelements (Weber et al. 2003; Collins et al. 2006;Kenter et al. 2006). The platform deposits are divi-ded into Unit 1 (Late Visean–Bashkirian), Unit 2(Tournaisian–Late Visean) and Unit 3 (Devonian).The Unit 1 platform is the best reservoir with anaverage porosity of 9% and target of this example.

PRT workflow application. The Tengiz Field wasused as a first application of the petrophysical rocktyping (PRT) workflow. The following is a shortdescription of the workflow steps outlined earlierin this paper:

† Step 1: Data scenario. The PRT definition for theplatform was based on 14 cored wells with a totalof 2650 m of core, which were assigned DRTs,and plugged every 1 ft for permeability, porosityand water-saturation analyses (CCAL or RCA).Plug trims were subject to total organic carbon(TOC) determination to quantify solid bitumen.More than 350 MICP measurements were usedfor matrix pore typing. Modern logging suiteswere available for 15 wells and included spectral

gamma, array induction, neutron, density, dipolesonic, FMI, and NMR tools. Pressure mea-surements from the well modular formationdynamics tester tool (MDT), PLT and verticalsonic profile (VSP) data were also available forevery well. As a result, the data scenario forthe platform is borderline to capturing reservoirheterogeneity, while that for the margin, withfewer wells but similar data quality, is con-sidered moderate (see Fig. 8).

† Step 2: Depositional rock typing. The Unit 1platform area is generally flat with paralleldepositional cycles, which are made up of a suc-cession of generally shoaling DRTs overlying asharp base with evidence for subaerial exposureand/or flooding. Cycle boundaries are gener-ally associated with early cementation and thepresence of solid and dispersed volcanic ash,and are well defined by log expression andrelatively easy to correlate across the centralplatform. The outer platform is the gently basin-ward-dipping (up to 98) sector, several hundredsof metres in width, connecting the central plat-form with the upper slope. Here, cycles are gen-erally poorly defined as a result of increasing

Fig. 10. Diagrams illustrating steps 2 and 3 in the Tengiz PRT workflow. (a) Diagram showing the lumped DRT setconsisting of a generally shoaling-upward cycle of DRTs; DRT7 is only present in the deeper outermost platform.(b) Diagram showing four major porosity enhancing/occluding episodes, each of which had a particular process andassociated spatial trend (modified after Kenter et al. 2010).




diagenetic overprint and the presence of tightmicrobial boundstone intervals depositedduring early flooding of the platform (Kenteret al. 2006). The ‘reservoir’ in between suchbaffled cycle boundaries was overprinted bymeteoric and (to a lesser extend) late burial dia-genesis increasing porosity by dissolution in thecentre of the platform, and reduced porositythrough compaction, calcite cementation andpyrobitumen cementation towards the margins.Geological descriptions identified a set of 15DRTs using texture, depositional setting andgrain types. Those DRTs were reduced to afinal set of seven DRTs (Fig. 10a), which werecorrelated across the platform using sequencestratigraphic principles. However, due to pre-dominantly diagenetic control (see below),only the DRT corresponding to volcanic layers

was used for the PRT definition. The resultingSSF was used for further PRT distribution inStep 6.

† Step 3: Reservoir typing. The study of the diage-netic modification of the original DRTs revealednine steps in a complete paragenetic sequencethat collapsed to four major porosity enhancingand/or occluding episodes (Fig. 10b). Eachof these diagenetic episodes had a particularprocess and associated spatial trend, whichwere used to construct diagenetic trend maps.Figures 3c, d illustrate, respectively, the confor-mity of porosity–permeability trends and thepredominance of diagenetically altered pores inthe Tengiz platform, while limited conformitywas observed between DRTs and logs. Theobserved high-K features, strata-bound zonesof connected vugs and high-permeability matrix,

Fig. 11. Diagrams explaining the PRT definition step in the Tengiz Platform. (a) PRT1 corresponds to the volcanic tuffrecognized from spectral gamma (thorium), and is related to DRT2 and linked to the early cementation stage. PRT2 wasdefined as a rock with solid bitumen volume exceeding an effective porosity (occupying more than 40% of the porespace), determined from Multimin analysis. (b) PRT3 corresponds to tight cemented rock as a result of late cementationand was defined as a rock dominated by microporosity, with PHIE (effective porosity) below 5%. (c) The PRT4–PRT6definition was derived from clustering NMR T2 distributions, and combining them with the effective porosity scale (30clusters or bins of relaxation times) and cross-plotted against porosity. (d) PRT4–PRT6 correspond to an increasingdegree of the corrosion and represent the reservoir quality rock (modified after Skalinski et al. 2009).




and conductive fractures from BHI and FMI,were confirmed by well tests and productionlogs. As a result, the RT was defined as aHybrid RT4 for the margin, while close to diage-netic RT2 for the platform (see Fig. 3b).

† Step 4: Pore typing. MICP-based pore throat dis-tributions led to the definition of five matrix poretypes; that is, nano-, micro-, meso-, macro- andmegaporosity (Skalinski et al. 2009). Pore typeswere used to validate NMR-derived PRT4–PRT6 from the clustering of T2 distributions.Vuggy porosity was captured by NMR andFMI logs.

† Step 5: PRT definition. PRTs were defined at thelog scale, as indicated in Figure 9 (Skalinskiet al. 2009). PRT1 corresponds to the volcanictuff recognized from spectral gamma (thorium)logs, using a 15 API cutoff (American Petroleum

Institute common scale units from a US test pit).This PRT is related to DRT2 and linked to theearly cementation stage (Fig. 10a, b). PRT2was defined as a rock with solid bitumen satur-ation exceeding 40%. Bitumen saturation wasdefined as a percentage of bitumen volume inreference to the original porosity (Fig. 11a),determined from multi-log mineralogical analy-sis (i.e. using Multimin in Geolog from Paradigmformation evaluation software; Quanti.Elan inTechlog from Schlumberger). The Multiminmodel was calibrated to core data to assure reli-able quantification of both parameters. Bitumenwas formed during late burial cementation (Fig.10b). PRT3 corresponds to tight-cemented rockas a result of late cementation. This PRT wasdefined as a rock dominated by microporosity(pore throat ,0.3 mm) and porosities below

Fig. 12. (a) T2 distributions combined over the approximately 500 m reservoir interval partitioned by PRT. Thesignal distribution on the vertical axis reflects the volumetric proportions, normalized to 1, corresponding to the porevolumes associated with each pore size shown on the horizontal axis. (b) Well plot confirming that non-reservoirPRT1–PRT3 have a large spread of T2 distribution mostly below 200 ms, while reservoir PRT4–PRT6 arecharacterized by higher T2, indicating increased amount of vuggy porosity (modified after Skalinski et al. 2009).




5% (Fig. 11b). PRT4–PRT6 correspond to anincreasing degree of the corrosion and representthe reservoir quality rock. Those PRTs weredefined from clustering NMR T2 distribu-tions and combining them with the effectiveporosity scale. This process allowed the captureof both pore types (degree of ‘vugginess’) andporosity. Figure 11c illustrates the definitionof PRT4–PRT6 from T2 clusters (groupings)cross-plotted against log porosity. Figure 11dshows a depth plot with (from left to right): spec-tral gamma ray, depth, core and log porosity,PRT and T2 distribution. Figure 12a contains T2

distributions combined over the approximately500 m reservoir interval partitioned by PRT.As expected, non-reservoir PRT1–PRT3 have a

large spread of T2 distribution mostly below200 ms, while reservoir PRT4–PRT6 are charac-terized by higher T2, indicating an increasedamount of vuggy porosity.

† Step 6: Determination in a multi-well setting andquality control using maps. PRTs defined in Step5 were determined in all platform wells withmodern well suits following specific cutoffsor clustering incorporated into the master petro-physical evaluation code (i.e. Loglan in Geolog;Paradigm formation evaluation software; sim-ilar routines available in Techlog from Schlum-berger). The quality control step consisted ofcomparing PRT maps with diagenetic and depo-sitional trends. Care was taken to eliminate out-liers and minimize overlap. The PRT maps were

Fig. 12. Continued.




used to define lateral trends used for the creationof MPS training images in Step 8.

† Step 7: Dynamic validation of PRTs. In Tengiz,the dynamic validation was performed by com-paring PRTs with PLT profiles (see Fig. 7b).This exercise confirmed that non-reservoirPRT1–PRT 3 correspond to non-flow zones,while reservoir PRT4–PRT6 correspond to theinflow zones (green bars in Fig. 7b). This confor-mance was successfully assessed in all wells withPLTs.

† Step 8: PRT realization and spatial validation.During steps 5–7 the relationship betweenPRTs and DRTs was carefully monitored tomaintain optimal relationships for spatial distri-bution in the model. The resulting mapping ofprocesses and spatial characteristics of DTs inPRTs is shown in Figure 13a. Following theprediction of the carefully defined – and vali-dated by dynamic data – set of PRTs in allwells, kriged PRT maps were generated for allSSF zones. Those maps, along with the earlierobservations on geological attributes (deposi-tional and diagenetic), resulted in a linkagebetween DRTs and PRTs that providedadditional information on spatial trends and

juxtaposition rules (Fig. 13a, b) required forbuilding appropriate training images, as well asthe soft probability cube for PRT conditioning.Some of the PRTs, although diageneticallymodified, still mostly resemble the originalDRTs and conform to primary depositionalspatial trends. Other PRTs have trends that arethe result of diagenetic processes, truncate theSSF and change within cycles (Fig. 13a, b).The interesting observation is that the timing ofthose diagenetic processes played a crucialrole: PRT1 and PRT3 were modified early andformed baffles with reduced porosity and per-meability bordering cycles or ‘containers’.These containers were later overprinted by cor-rosion, mostly in the centre of the platform,and cementation towards the NE and easternsectors in the field where calcite and bitumencements occluded porosity. Key elements ofthe static model workflow included trainingimages, vertical proportion curves (VPC) andfacies probability cubes. The static geomodel-ling process utilizes MPS, since this handlescomplex juxtaposition while preserving pro-portions better than most other available model-ling platforms. The MPS modelling workflow

Fig. 13. (a) Illustration showing the mapped relationship between DRTs and resulting PRTs. (b) Simplifiedexplanation of the PRTs in terms of relative depositional and diagenetic control, where DRT1–DRT3 are non-reservoir,and PRT4–PRT6 represent fair to very good reservoir.




used for the Tengiz platform has been publishedby Kenter et al. (2010) and is briefly summarizedbelow. The training images (Fig. 14a) depictPRT body shapes, relative dimensions, size–frequency distribution and (in this scenario) 3Dspatial interrelationships from core and pre-dicted well observations, as well as from geo-logical concepts supplemented by informationfrom forward models and outcrops analogues(for more detailed information, see Kenter et al.2010). Vertical proportion curves (Fig. 14b)display the proportion of facies per layer in thegeocellular model, while facies probabilitycubes integrate information from the wellsthrough kriged PRT maps, seismic or geologicalinterpretation, and the VPC into facies probabil-ities for each cell. Kriged PRT maps (Fig. 14c)were manually adjusted to generate more con-ceptually realistic trends (Fig. 14d), especially

in the interwell areas, and, next, convolved withthe VPCs to generate a soft probability cube(Fig. 14e). Following a declustering step toaccount for clustering of pre-Joint Venture andmodern wells, for each sequence the soft prob-ability cubes were convolved with the trainingimages. Finally, porosity, water saturation andpermeability were distributed in the PRT modelusing standard stochastic methods.

Wafra Field

The First Eocene reservoir at Wafra Field, located inthe onshore area of the Partitioned Zone betweenSaudi Arabia and Kuwait (Fig. 9), is a heavy oil car-bonate reservoir with an average porosity of 37%,which is exceptionally high for carbonates (Med-daugh et al. 2013). The total onshore oil productionin the Partitioned Zone reached 3 billion (3 × 109)

Fig. 14. Simplified diagram summarizing the geomodelling steps using multiple point statistics (MPS). Only five PRTswere distributed in this realization as the pre-Joint Venture (Russian) well group was not capable of predicting PRT6.The key in the lower-left corner shows the ‘lumping’, while Figure 13b provides the key for the PRTs. The modellingsteps run from (a) the generation of training images capturing relative proportions and juxtaposition rules, to (b) thecalculation (and, if required, manual modification) of vertical proportion curves, to (c & d) kriged PRT maps (from thedetermined well PRTs) that were manually adjusted to generate more conceptually realistic trends, to (e) the softprobability cube. Cross-sections of the final (base case) realization are shown in the centre of the diagram (modified afterKenter et al. 2010).




barrels in late 2004, with production from fourfields. In 2009, a pilot steam injection projectbegan at the carbonate First Eocene reservoir atthe Wafra Field. Steam flooding involves injectingsteam into heavy oil reservoirs to heat the crudeoil underground, reducing its viscosity and allowingits extraction through wells.

PRT workflow application. The rock typing was per-formed as part of the model to support the steaminjection project. The eight-step PRT workflowwas applied to predict and distribute the dominantpetrophysical rock types:

† Step 1: Data scenario. The resulting PRT set wasdefined using a subset of 10 cored wells andapplied to 113 wells in the model area. Nearly120 MICP samples were used to define throat-based pore types. The 56 wells in the steaminjection pilot area include 15 wells with triplecombo (standard set of measurements used information evaluation and wireline logging:gamma ray, porosity and resistivity), and NMRand FMI. However, for the sake of consistency,PRTs were derived from the triple combo logsuite present in all wells. Although the welldensity is high locally and capturing reservoir

heterogeneity, it also has a high degree of clus-tering. Since logging suites are generally poor,the data scenario is considered moderate (seeFig. 8).

† Step 2: Depositional rock typing. DRTs weredescribed in 10 cored wells with eight initialgroups, which were lumped to three faciesassemblages (Fig. 16). The heavy oil reservoirconsists of dolomitized subtidal packstone andgrainstone deposited under arid or semi-aridconditions in a shallow, very gently dippingrestricted ramp environment. The shallowing-upward cycles are capped by mud-dominatedtidal flat facies, which are followed or replacedby evaporites indicating occasionally hypersa-line lagoons and sabkhas. DRTs were furtherlumped to three to allow reliable prediction fromlogs: (1) evaporites (anhydrite and gypsum);(2) tidal flat capping facies including algal-dominated grainstone, and dolomitic wackes-tone and packstone; and (3) subtidal (dolomitic)packstone. The lumping honoured both deposi-tional patterns and similarity in logging space.The three lumped DRTs were predicted in areliable way from logs using Multimin for eva-porites and multivariate stepwise discriminant

Fig. 15. Graph showing the pore throat radius histograms (from MICP) groupings that were used for further lumping.




analysis for the remaining DRTs, with a predic-tion score of 80% (Fig. 2c).

† Step 3: Reservoir typing. The determinationof RT was based on the conformance ofDRT prediction from logs and examinationsof porosity–permeability cross-plots by DRTs.The strong diagenetic modification by dolomiti-zation did not cross-cut depositional trends andtherefore the reservoir type was consideredas RT1 (Fig. 3b).

† Step 4: Pore typing. MICP data allowed the defi-nition of seven PT groups, which were furtherlumped into three lumped pore types predictablefrom core and log data. Figure 15 shows the first-generation MICP groupings, and Figure 16agives a porosity–permeability cross-plot withlumped pore types as colours. Lumped poretypes were: PT1, dominated by macroporosity;PT2, dominated by mesoporosity; and PT3,dominated by microporosity. For the purpose

of further prediction from logs, pore types werefurther lumped to two groups: macro- (PT1 andPT2) and microporosity (PT3, see Fig. 16b).Examination of NMR and FMI logs helped toidentify moldic porosity, which was lumpedwith macroporosity as it did not stand out onthe porosity–permeability (phi–K ) cross-plotas a distinct group.

† Step 5: PRT definition. The PRTs were firstdefined as a combination of predicted DRTsand pore types. Since the reservoir type is closeto depositional, the PRTs were defined by con-sidering first DRT and incorporating diageneticpore types in the second step in which, both,the tidal flat DRT2 and subtidal DRT3 were sub-divided into two groups based on pore types (seeFig. 16c). The five resulting PRTs defined thisway were predicted from logs using stepwise dis-criminant analysis with a prediction score of77% using conventional logs only. In order to

Fig. 16. (a) Porosity–permeability cross-plot with lumped pore types from seven MICP analysis to three lumped poretypes that were predictable from core and log data. (b) For the purpose of further prediction from logs, pore types werefurther lumped to two groups: macro- (macro-lumped with meso-) and microporosity. (c) As a result of the RT1assessment, PRTs were defined by first considering DRT and then by incorporating diagenetic pore types. Hence, boththe tidal flat DRT2 and subtidal DRT3 were subdivided into two groups based on pore types. See the text for discussion.




Fig. 17. Diagram showing the strategy of nesting PRTs within DRTs for the top stratigraphic interval as an example ofthe areal quality control check of the predicted PRTs. Nesting of PRTs followed the initial distribution of DRTs usingdedicated DRT training images defined using ramp analogues and available core descriptions. Note that TF, ST and Estand for, respectively, tidal flat facies, subtidal facies and evaporite, see the text for discussion. Figure is courtesy ofM. Andres and M. Levy.

Fig. 18. Cross-section over the resulting sector model showing the distribution of PRTs, which were simulated using‘PRTs nested in DRTs’ in MPS. Note that TF, ST and E stand for, respectively, tidal flat facies, subtidal facies andevaporite, see the text for discussion. Figure courtesy of M. Andres & M. Levy.




preserve low-permeability baffles in the model,PRT2 was further subdivided into two PRTs,based on an evaporite threshold of 35%.

† Step 6: Determination in a multi-well setting andquality control using maps. The PRTs defined inStep 5 were determined in all wells from themodel area. The PRT maps helped to definethe dimension and shape of the depositional‘bodies’ and their spatial trends.

† Step 7: Dynamic validation of PRTs. Themodular formation dynamics tester tool pressureprofiles were successfully used to validateevaporite barriers.

† Step 8: PRT realization and spatial validation.The resulting PRTs at the wells were sub-sequently analysed in map and in cross-sectionview to unravel their spatial trends and juxta-position relationships, necessary to constructtraining images and distribute the PRTs in the3D static model. PRTs were distributed in themodel using MPS. First, DRT training imageswere defined using ramp analogues and availablecore descriptions. Next, PRT distribution andtraining images were nested within correspond-ing DRTs as per definition (Fig. 17). Figure 18shows a cross-section over the sector model,with PRTs simulated using MPS.

Conclusions

Conventional carbonate rock typing workflows arebased either on textural properties, pore type clas-sification, the application of electrofacies orflow zone indicators (FZI), and are biased eithertowards depositional or towards diagenetic attri-butes. The resulting proportions and spatial distri-butions of rock types and estimated flow may,therefore, be poor estimates of the real subsurfacedistributions. This workflow addresses this bias ina comprehensive way by determining petrophysicalrock types, which control static properties anddynamic behaviour of the reservoir while optimallylinking the geological attributes (depositional anddiagenetic as well as their hybrid), and their spatialinterrelationships and trends. The integrated work-flow consists of eight composite and sequentialsteps, which account for progressing field maturityand data scenarios, including matrix and secondarypore types such as vugs and fractures. Our newworkflow validates to dynamic data and optimizesthe representation of geological attributes (deposi-tional and diagenetic), as well as their spatial trendsand juxtaposition rules. Initial tests on Tengiz Fieldand Wafra Field were highly successful and sup-port the applicability of the workflow in differentdata scenarios and reservoir types. This paper intro-duces the rationale behind this integrated

workflow and demonstrates its workings andagility through deployment in two large carbonatefields. This workflow is novel in several ways: (1)it combines geological processes, petrophysics andEarth modelling aspects of rock typing in one com-prehensive approach; (2) it integrates core and logscales, and provides consistent input into reservoirmodels in the log domain; and (3) it provides a flex-ible ‘road map’ from core to 3D model for variabledata scenarios such as prospects, appraisal and pro-ducing fields that can be updated with progressivechanges in data quality and quantity during the lifecycle of an asset.

The authors would like to thank the Kingdom of SaudiArabia Ministry of Petroleum, Saudi Arabian Chevron,the Kuwait Gulf Oil Company, the Kuwait Ministry ofOil and TengizChevroil for permission to publish thepaper. Discussions with, and support and encouragementfrom, many peers and colleagues significantly improvedthe workflow and contributed to this paper. In particular,we would like to acknowledge M. Andres and M. Levyfor their contribution to the PRT modelling in the (FirstEocene) Wafra example, M. Harris for continuouslytesting the underlying principles and concepts, andA. Latham and F. Harris for trust and committing fundsto the research project. Lastly, we would like to thankthe reviewers, J. Lucia, P. Corbett and S. Geiger, for con-structive and positive critique, which greatly improved thequality of this paper.

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