Constructing Reservoir Flow Simulator Proxies Using Genetic Programming for History Matching and Production Forecast Uncertainty Analysis
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- @Article{Yu:2008:JAEA,
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author = "Tina Yu and Dave Wilkinson and Alexandre Castellini",
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title = "Constructing Reservoir Flow Simulator Proxies Using
Genetic Programming for History Matching and Production
Forecast Uncertainty Analysis",
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journal = "Journal of Artificial Evolution and Applications",
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year = "2008",
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volume = "2008",
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pages = "Article ID 263108",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://www.cs.mun.ca/~tinayu/Publications_files/JAEA.pdf",
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URL = "http://downloads.hindawi.com/archive/2008/263108.pdf",
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DOI = "doi:10.1155/2008/263108",
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size = "13 pages",
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abstract = "Reservoir modelling is a critical step in the planning
and development of oil fields. Before a reservoir model
can be accepted for forecasting future production, the
model has to be updated with historical production
data. This process is called history matching. History
matching requires computer flow simulation, which is
very time-consuming. As a result, only a small number
of simulation runs are conducted and the
history-matching results are normally unsatisfactory.
This is particularly evident when the reservoir has a
long production history and the quality of production
data is poor. The inadequacy of the history-matching
results frequently leads to high uncertainty of
production forecasting. To enhance the quality of the
history-matching results and improve the confidence of
production forecasts, we introduce a methodology using
genetic programming (GP) to construct proxies for
reservoir simulators. Acting as surrogates for the
computer simulators, the cheap GP proxies can evaluate
a large number (millions) of reservoir models within a
very short time frame. With such a large sampling size,
the reservoir history-matching results are more
informative and the production forecasts are more
reliable than those based on a small number of
simulation models. We have developed a workflow which
incorporates the two GP proxies into the history
matching and production forecast process. Additionally,
we conducted a case study to demonstrate the
effectiveness of this approach. The study has revealed
useful reservoir information and delivered more
reliable production forecasts. All of these were
accomplished without introducing new computer
simulation runs.",
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notes = "Department of Computer Science, Memorial University of
Newfoundland. Chevron Energy Technology Company",
- }
Genetic Programming entries for
Tina Yu
Dave Wilkinson
Alexandre Castellini
Citations