combining various approaches in geostatistical reservoir ...

COMBINING VARIOUS APPROACHES IN GEOSTATISTICAL RESERVOIR MODELING

- METHODS AND BENEFITS -

Nicolas Jeannée, Matthieu Bourges (Geovariances)

Geostats Rendezvous Perth, 26-27 February 2013

Geomodeling context

Production forecast

Reservoir grid Upscaling

Flow simulation

Integration of production data

structural model

Well and seismic data

proportions of facies

Stratigraphic model

Integration of 4D seismic data

Courtesy of I F P

Geological model: Facies, porosity, permeability

Context Reservoir Modeling

Describe as well as possible the reservoir heterogeneity Numerous methods available for facies modeling, with pros / cons: o Pixel- o Object- o Process-

Key issues addressed here Geological realism Data conditioning Non stationarity

Objectives Illustrate how we can benefit from combining/nesting algorithms Simple synthetic case study with the following workflow: o Facies simulation using Flumy (Geological realism) o Data conditioning using MPS o Reservoir property using Local Geostatistics

Methodology: Flumy Flumy

Mines ParisTech Consortium

Process-based approach

Reproduce over time the accumulation of material in a meandering fluvial system

Based on geological parameters (channels width, erodibility,

although not easy

Methodology: MPS

Principle of Multiple-Points Statistics Pixel-based algorithm aiming at « mimic-ing » the facies distributions and relationships as observed on a training image Parallelized algorithm based on a List approach (IMPALA) Capture small and large scale structures Integration of facies local proportions and seismic attributes for refined models

Key points Finding a training image?? Data conditioning

Methodology: MPS

Training imageShould describe the geological context to be reproduced (in 2D/3D) Is derived from the geological knowledge: Analogs, outcrops,

Should be larger than the largest patterns to be reproduced in the reservoir (ergodicity)

Methodology: MPS

Data conditioning Conditioning data (wells) are migrated to the closest grid node and considered as already simulated. In case multigrids are used, conditioning data are propagated through them in order to be taken into account at each simulation stage.

Methodology: LGS Local Geostatistics

The majority of geostatistical models used in the Oil & Gas industry are variogram-based models

Main estimation and simulation algorithms

Estimation Simulations

Simple kriging

Ordinary kriging

Factorial kriging

Collocated co-kriging

Kriging with external

drift

Sequential Gaussian

(SGS)

Turning Bands

Sequential Indicator

(SIS)

Truncated PluriGaussian

(PGS)

MPS Boolean

VB VB VB VB VB VB VB VB VB

VB Variogram-Based

Methodology: LGS Classical Geostatistics

Basic assumption: STATIONARITY OF THE SPATIAL STRUCTURE! Common issues addresses by Local Geostatistics:

Small scale structures, strong anisotropy

Highly continuous

« Structural » non stationarity Local accuracy

Reality Data Non-conditional simulation Random extraction from the reality

Goal: estimate the reality from the data (estimation problem)

Methodology: LGS illustrative example

Data

Conventional model

Cubic function Range X = 38 (constant) Range Y= 18 (constant) Orientation = 42° (constant)

42°

Interpretation

Model using varying parameters

Interpretation degrees

Cubic function Local Range X Local Range Y Local Orientation


Reality Data

Conventional kriging

LGS kriging

Errors : ² = 8,5

Errors : ² = 5,5

Reality is better restored by using a locally optimized model


Methodology: LGS

Idea: estimate and use parameter maps

Main challenge: determining local parameters Several approaches: o Local cross-validation o Local variogram analysis

Ability to integrate exogenous information

Then, use of the local parameters in estimation or simulation algorithms

Typical examples: tomorrow

Case Study: workflow Meandering fluvial system

Objectives

with better continuity in the channel direction

FLUMY

MPS

LGS

Case Study: Flumy simulation Meandering fluvial system

Simple lithological model, 3 lithotypes point bar, levee, overbank alluvium.

Case Study: MPS simulation Extraction of a 2D time slice (for illustration)

MPS simulation honoring 30 facies data

Case Study: Local geostatistics 40 porosity data available within two channels

42°

-15°

Channel orientation

Case Study: Local geostatistics SGS simulations

Isotropic Variogram Locally Varying Anisotropy

Conclusions & Perspectives

Simple illustration on how to combine various algorithms in order to refine the modeling of Oil & Gas reservoir heterogeneities

Methods addressed: Flumy Multiple-Point Statistics Local Geostatistics

Perspectives: Applying the workflow directly on 3D Comparing with MPS conditioning

combining various approaches in geostatistical reservoir ...

Documents

Transcript of combining various approaches in geostatistical reservoir ...