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The AAPG/Datapages Combined Publications Database
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Abstract
Liu, Yuhong, Peter G. Rigsby, Rohit Sinha, Steve Peterson, Joy Thomas, and Gregory Zimmerman, modeling
and uncertainty analysis of a Gulf of Mexico reservoir, in
modeling
DOI:10.1306/13301408M963479
2011 by The American Association of Petroleum Geologists.
Geologic
Modeling
and Uncertainty Analysis of a Gulf of Mexico Reservoir
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Yuhong Liu,1 Peter G. Rigsby,2 Rohit Sinha,3 Steve Peterson,4 Joy Thomas,5 Gregory Zimmerman6
1Marathon Oil Corporation, Houston, Texas, U.S.A.
2Marathon Oil Corporation, Houston, Texas, U.S.A.
3Marathon Oil Corporation, Houston, Texas, U.S.A.
4Marathon Oil Corporation, Houston, Texas, U.S.A.
5Marathon Oil Corporation, Houston, Texas, U.S.A.
6Marathon Oil Corporation, Houston, Texas, U.S.A.
ACKNOWLEDGMENTS
We thank Marathon Oil Corporation for the support and permission to publish this work. We also thank our coworkers, Ron Stoltz, Rob Sutton, Robert Spaeth, Zhucheng (Bruce) Shang, and Mark Quakenbush, for their support during the work. The authors also thank our partner, Marubeni, for permission to publish the article.
ABSTRACT
Uncertainty analysis in a deep-water turbidite reservoir in the Gulf of Mexico is discussed in this chapter. Six major factors were addressed in the study to capture uncertainty: reservoir compartmentalization; acoustic impedance (AI) attenuation caused by the salt overhang; AI attenuation caused by fluid effects; petrophysical properties; pressure, volume, and temperature; and aquifer strength uncertainty. We characterized and quantified the uncertainties in the reservoir by integrating information from various disciplines: geophysics, petrophysics, geology, and engineering. This uncertainty workflow enabled us to make sound business decisions in a timely manner.
A base case scenario model was first built integrating well and seismic
data. Multiple realizations were then simulated by varying petrophysical parameters such as porosity, permeability, and saturation; turning on and off various potential barriers; and processing the impedance volume for different scenarios. The effect of the drive mechanism on ultimate recovery was also studied by running scenarios with varying amounts of aquifer size and strength. The outputs of various scenarios were compiled to generate a probability distribution curve of the expected reserves and production rates. The outputs were then incorporated into an economic model to evaluate the viability of the project.
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