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The AAPG/Datapages Combined Publications Database
AAPG Special Volumes
Abstract
Caers, Jef, and Celine Scheidt, reservoir
performance prediction using a distance-based approach, in
reservoir
modeling
DOI:10.1306/13301414M963032
2011 by The American Association of Petroleum Geologists.
Integration of Engineering and Geological Uncertainty for
Reservoir
Performance Prediction Using a Distance-Based Approach


Jef Caers,1 Celine Scheidt2
1Energy Resources Engineering Department, Stanford University, Stanford, California, U.S.A.
2Energy Resources Engineering Department, Stanford University, Stanford, California, U.S.A.
ACKNOWLEDGMENTS
We thank Chevron for providing data for the first case study and Marathon Oil for providing data for the second case study.
ABSTRACT
Uncertainty is an integral part of risk and decision making. Uncertainty in the reservoir
is caused by a lack of knowledge on key geologic and
reservoir
engineering factors that are required for building geologic and flow models. Traditional approaches to modeling uncertainty, such as those relying on the experimental design technique and Monte Carlo simulation, are either limited in effectiveness (unable to handle general cases) or efficiency (too demanding on the central processing unit). We review a new technique for modeling
reservoir
performance uncertainty that is more general and efficient. The technique relies on the definition of a distance between any two
reservoir
models. This distance should correlate with the difference in
reservoir
response and provides the key missing link between geologic uncertainty and flow uncertainty. We present a workflow combining several statistical tools such as multidimensional scaling and clustering to model
reservoir
response uncertainty when both geologic and engineering parameters are uncertain. At the same time, this workflow allows assessment of the most influential parameters regarding flow, as well as accuracy of the uncertainty assessment. Two real
field
cases illustrate this approach.
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