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 unc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/3/1153 |
| _version_ | 1797624743456669696 |
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| author | Tomasz Tuczyński Jerzy Stopa |
| author_facet | Tomasz Tuczyński Jerzy Stopa |
| author_sort | Tomasz Tuczyński |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:46:51Z |
| format | Article |
| id | doaj.art-4e0c62c0f5454128b181d469a2cf9cc4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T09:46:51Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4e0c62c0f5454128b181d469a2cf9cc42023-11-16T16:33:31ZengMDPI AGEnergies1996-10732023-01-01163115310.3390/en16031153Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation AlgorithmTomasz Tuczyński0Jerzy Stopa1Department of Petroleum Engineering, Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, PolandDepartment of Petroleum Engineering, Faculty of Drilling, Oil and Gas, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, PolandProduction 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.https://www.mdpi.com/1996-1073/16/3/1153reservoir simulationhistory-matchinguncertainty quantification |
| spellingShingle | Tomasz Tuczyński Jerzy Stopa Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm Energies reservoir simulation history-matching uncertainty quantification |
| title | Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm |
| title_full | Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm |
| title_fullStr | Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm |
| title_full_unstemmed | Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm |
| title_short | Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm |
| title_sort | uncertainty quantification in reservoir simulation using modern data assimilation algorithm |
| topic | reservoir simulation history-matching uncertainty quantification |
| url | https://www.mdpi.com/1996-1073/16/3/1153 |
| work_keys_str_mv | AT tomasztuczynski uncertaintyquantificationinreservoirsimulationusingmoderndataassimilationalgorithm AT jerzystopa uncertaintyquantificationinreservoirsimulationusingmoderndataassimilationalgorithm |