A significant body of work has demonstrated both the promise and difficulty of
quantifying uncertainty in reservoir simulation forecasts. It is generally accepted that
accurate and complete quantification of uncertainty should lead to better decision
making and greater profitability. Many of the techniques presented in past work attempt
to quantify uncertainty without sampling the full parameter space, saving on the number
of simulation runs, but inherently limiting and biasing the uncertainty quantification in
the resulting forecasts. In addition, past work generally has looked at uncertainty in
synthetic models and does not address the practical issues of quantifying uncertainty in
an actual field. Both of these issues must be addressed in order to rigorously quantify
uncertainty in practice.
In this study a new approach to reservoir simulation is taken whereby the
traditional one-time simulation study is replaced with a new continuous process
potentially spanning the life of the reservoir. In this process, reservoir models are
generated and run 24 hours a day, seven days a week, allowing many more runs than
previously possible and yielding a more thorough exploration of possible reservoir descriptions. In turn, more runs enabled better estimates of uncertainty in resulting
forecasts. A new technology to allow this process to run continuously with little human
interaction is real-time production and pressure data, which can be automatically
integrated into runs.
Two tests of this continuous simulation process were conducted. The first test
was conducted on the Production with Uncertainty Quantification (PUNQ) synthetic
reservoir. Comparison of our results with previous studies shows that the continuous
approach gives consistent and reasonable estimates of uncertainty. The second study was
conducted in real time on a live field. This study demonstrates the continuous simulation
process and shows that it is feasible and practical for real world applications.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-1179 |
| Date | 15 May 2009 |
| Creators | Holmes, Jay Cuthbert |
| Contributors | McVay, Duane A. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
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