A significant body of work has demonstrated both the promise and difficulty of quantifying uncertainty in reservoir simulation forecasts. It is generally accepted that complete and reliable quantification of uncertainty should lead to better decision making and greater profitability. Many of the techniques presented in previous work attempt to quantify uncertainty without sampling the full parameter space, saving on the number of simulation runs, but inherently biasing and underestimating the uncertainty in the resulting forecasts. In addition, much of previous work has looked at uncertainty quantification 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 reliably quantify uncertainty in practice.

In this study a new approach to reservoir simulation is proposed whereby the traditional one-time simulation study is replaced with a 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 enables better estimates of uncertainty in resulting forecasts. This is combined with real-time acquisition of production and pressure data, which is automatically integrated into simulation runs to allow the process to run continuously with little human interaction.

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 test was conducted in real time on a live field. This test demonstrates the continuous simulation process and shows that it is feasible and practical for real world applications.

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