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Understanding Flow Behavior in Carbonate Reservoirs from Facies-Based Earth Models*

By

Marjorie Levy1, William Milliken1, Paul M. (Mitch) Harris1, and Sebastien Strebelle1

 

Search and Discovery Article #40288 (2008)

Posted July 12, 2008

 

*Adapted from oral presentation at AAPG and AAPG European Region Energy Conference and Exhibition, Athens, Greece, November 18-21, 2007. See companion article, "Importance of Facies-Based Earth Models for Understanding Flow Behavior in Carbonate Reservoirs", Search and Discovery Article #40306 (2008).

Click to view list of articles adapted from presentations by P.M. (Mitch) Harris or by his co-workers and him at AAPG meetings from 2000 to 2008.

 

1Chevron Energy Technology Company, San Ramon, CA, USA ([email protected], [email protected], [email protected])

 

Abstract

Previous HitReservoirNext Hit models attempt to mimic the distribution of Previous HitreservoirNext Hit properties in subsurface systems, and in carbonate reservoirs should capture geologically meaningful and realistic heterogeneity. In this Previous HitstudyNext Hit, we explore grainstone-dominated systems. On the basis of modern analogs from the Bahamas, grainstone shoals are modeled with linear or crescent-shaped bars, and include barcrest, barflank, and island facies.

A series of flow experiment studies investigate the impact of a variety of model input parameters on different measures of flow performance. The first Previous HitstudyNext Hit focuses on using three different modeling techniques: SGS, a non-facies-based method using a continuous variogram; SIS, a facies-based method using an indicator variogram, and MPS, a facies-based method using a training image and facies probability cube. The second Previous HitstudyNext Hit considers the impact of Previous HitreservoirNext Hit facies percent on flow behavior and how this changes with different modeling methods and different facies geobody shapes. The third Previous HitstudyNext Hit is a Plackett-Burman experimental design varying a) proportions of Previous HitreservoirNext Hit facies vs non-Previous HitreservoirNext Hit facies, b) proportions of barflank/barcrest Previous HitreservoirNext Hit facies, c) dimensions of facies geobodies, d) dissolution or cemented diagenetic zones, e) stratigraphic cyclicity f) the porosity histogram, g) the permeability transform, and h) spatial distribution of Previous HitreservoirNext Hit facies (distributed across platform vs. localized).

Each model was tested using Previous HitreservoirNext Hit Previous HitsimulationNext Hit and considered different development scenarios. Models were compared on the basis of static measures of OOIP, Previous HitreservoirNext Hit connectivity, and permeability heterogeneity and on the basis of dynamic measures of recovery factor vs. time, recovery factor vs. pore volumes injected, net present oil, cumulative oil produced, and water breakthrough time.

 

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uAbstract

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uModeling Previous HitstudyNext Hit

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uAbstract

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uIntroduction

uMPS

uTraining image

uModeling Previous HitstudyNext Hit

uSummary

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uFigures

uIntroduction

uMPS

uTraining image

uModeling Previous HitstudyNext Hit

uSummary

 

 

Introduction

Using experimental design, we examine the uncertainty in input parameters on flow performance using Multiple Point Statistics for a synthetic carbonate platform.

The objectives of this Previous HitstudyNext Hit are to:

  • Assess the value of facies-based models.
  • Explore stratigraphic and textural uncertainty in grainstone-dominated carbonate systems.

Methodology includes using:

  • Modern analogs from the Bahamas for training images.
  • Subsurface data for Previous HitreservoirNext Hit properties.
  • A workflow combining Multiple Point Statistics (MPS) Previous HitsimulationNext Hit and Facies Distribution Modeling (FDM) and streamline Previous HitsimulationNext Hit.

 

Multiple Point Statistics (MPS)

MPS is an innovative Previous HitreservoirNext Hit facies modeling technique that uses conceptual geological models as 3D training images to generate geologically realistic Previous HitreservoirNext Hit models:

  • Ability to reproduce “shapes” of object-based algorithms.
  • Speed, flexibility, and easy data conditioning of variogram-based algorithms.

 

What is a Training Image

The 3D training image is a rendering of the geological model that defines relative facies body dimensions and shapes, as well as associations between facies.

 

Carbonate Previous HitReservoirNext Hit Modeling Previous HitStudyNext Hit

Givens for Previous HitstudyNext Hit:

  • Models are facies-based.
  • Geologic setting is grainstone-dominated platform consisting of barcrest, barflank, and island Previous HitreservoirNext Hit facies and a background facies.
  • There are 5 delineation wells. Facies and porosity data are generated in the wells, and all models are conditioned to that data.
  • All models are simulated assuming a waterflood recovery mechanism.
    • Different well counts and different well patterns are considered.
    • Results of the Previous HitsimulationNext Hit have been analyzed with respect to a range of measures (RF vs time, RF vs PVI, NPV, CumOil, etc).

 

Summary of Experimental Design Previous HitStudyNext Hit

  • Analyzed the effect of architectural and textural parameters on fluid flow in a synthetic grainstone-dominated carbonate platform.
  • Workflow used MPS/FDM to generate facies geobodies.
  • Areal distribution of Previous HitreservoirNext Hit facies shows a first-order impact on flow performance with respect to different measures of flow.
  • With areal distribution held constant, the most significant parameters were:
    • Absolute permeability values.
    • Percent of Previous HitreservoirNext Hit facies.
    • Size of Previous HitreservoirTop facies geobodies.
    • Ratio of barcrest to barflank facies.

 

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