Continuous reservoir simulation incorporating uncertainty quantification and real-time data
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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.
Holmes, Jay Cuthbert (2006). Continuous reservoir simulation incorporating uncertainty quantification and real-time data. Master's thesis, Texas A&M University. Available electronically from