Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm

Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm's efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir.

Automated summary

Production forecasting using numerical simulation is standard in the oil and gas industry, but the uncertainty of the modeling outcome needs to be addressed. History-matching can reduce uncertainty for reservoirs with production data, but manual matching is time-consuming and generates only one deterministic realization. This paper describes the uncertainty quantification process for a gas-condensate reservoir using an ensemble-based approach and discusses ways to improve the efficiency of the algorithm and analyze the factors controlling modeling uncertainty.