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Understanding Flow Behavior in Previous HitCarbonateNext Hit Reservoirs from Previous HitFaciesNext Hit-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 Previous HitFaciesNext Hit-Based Earth Models for Understanding Flow Behavior in Previous HitCarbonateNext Hit 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

Reservoir models attempt to mimic the distribution of reservoir properties in subsurface systems, and in Previous HitcarbonateNext Hit reservoirs should capture geologically meaningful and realistic heterogeneity. In this study, 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 Previous HitfaciesNext Hit.

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

Each model was tested using reservoir simulation and considered different development scenarios. Models were compared on the basis of static measures of OOIP, reservoir 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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uIntroduction

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uAbstract

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uIntroduction

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uAbstract

uFigures

uIntroduction

uMPS

uTraining image

uModeling study

uSummary

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

uAbstract

uFigures

uIntroduction

uMPS

uTraining image

uModeling study

uSummary

 

 

Introduction

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

The objectives of this study are to:

  • Assess the value of Previous HitfaciesNext Hit-based models.
  • Explore stratigraphic and textural uncertainty in grainstone-dominated Previous HitcarbonateNext Hit systems.

Methodology includes using:

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

 

Multiple Point Statistics (MPS)

MPS is an innovative reservoir Previous HitfaciesNext Hit modeling technique that uses conceptual geological models as 3D training images to generate geologically realistic reservoir 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 Previous HitfaciesNext Hit body dimensions and shapes, as well as associations between Previous HitfaciesNext Hit.

 

Previous HitCarbonateNext Hit Reservoir Modeling Study

Givens for study:

  • Models are Previous HitfaciesNext Hit-based.
  • Geologic setting is grainstone-dominated platform consisting of barcrest, barflank, and island reservoir Previous HitfaciesNext Hit and a background Previous HitfaciesNext Hit.
  • There are 5 delineation wells. Previous HitFaciesNext Hit 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 simulation have been analyzed with respect to a range of measures (RF vs time, RF vs PVI, NPV, CumOil, etc).

 

Summary of Experimental Design Study

  • Analyzed the effect of architectural and textural parameters on fluid flow in a synthetic grainstone-dominated Previous HitcarbonateNext Hit platform.
  • Workflow used MPS/FDM to generate Previous HitfaciesNext Hit geobodies.
  • Areal distribution of reservoir Previous HitfaciesNext Hit 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 reservoir Previous HitfaciesNext Hit.
    • Size of reservoir Previous HitfaciesNext Hit geobodies.
    • Ratio of barcrest to barflank Previous HitfaciesTop.

 

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