Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm

Tomasz Tuczyński () and Jerzy Stopa
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Tomasz Tuczyński: Department of Petroleum Engineering, Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland
Jerzy Stopa: Department of Petroleum Engineering, Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland

Energies, 2023, vol. 16, issue 3, 1-16

Abstract: 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.

Keywords: reservoir simulation; history-matching; uncertainty quantification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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