Sensitivity analysis of parameters for carbon sequestration: Symbolic regression models based on open porous media reservoir simulators predictions
Open Porous Media (OPM) Flow is an open-source reservoir simulator used for solving subsurface porous media flow problems. Focus is placed here on carbon sequestration and the modeling of fluid flow within underground porous reservoirs. In this study, a sensitivity analysis of some input parameters...
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Elsevier
2024-11-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024160756 |
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| author | Pavel Praks Atgeirr Rasmussen Kjetil Olsen Lye Jan Martinovič Renata Praksová Francesca Watson Dejan Brkić |
| author_facet | Pavel Praks Atgeirr Rasmussen Kjetil Olsen Lye Jan Martinovič Renata Praksová Francesca Watson Dejan Brkić |
| author_sort | Pavel Praks |
| collection | DOAJ |
| description | Open Porous Media (OPM) Flow is an open-source reservoir simulator used for solving subsurface porous media flow problems. Focus is placed here on carbon sequestration and the modeling of fluid flow within underground porous reservoirs. In this study, a sensitivity analysis of some input parameters for carbon sequestration is performed using six different uncertain parameters. An ensemble of model realizations is simulated using OPM Flow, and the model output is then calculated based on the values of the six input parameters mentioned above. CO2 injection is simulated for a period of 15 years, while the post-injection migration of CO2 in the saline storage aquifer is simulated for a subsequent period of 200 years, leading to a final analysis after 215 years. The input parameter values are generated using the quasi-Monte Carlo (QMC) method in the region of interest, following specified patterns suitable for analysis. The optimal convergence rate for quasi-Monte Carlo is observed. The aim of this study is to identify important input parameters contributing significantly to the model output, which is accomplished using sensitivity analysis and verified through symbolic regression modeling based on machine learning. Global sensitivity analysis using the Sobol sequence identifies input parameter 3, 'Permeability of shale between sand layers,' as having the most influence on the model output 'Secondary Trapped CO2.' All regression models, including the simplest and least accurate ones, incorporate parameter 3, confirming its significance. These approximations are valid within the designated area of interest for the input parameters and are easily interpretable for human experts. Sensitivity analysis of the developed time-dependent carbon sequestration model shows that the significance of each physical parameter changes over time: Sand porosity is more significant than shale permeability for roughly the first 120 years. Consequently, the presented results show that simulation timescales of at least 200 years are necessary for carbon sequestration evaluation. |
| format | Article |
| id | doaj-art-2dc47d7e2be54f4e87e0ed4cb6d5e357 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-2dc47d7e2be54f4e87e0ed4cb6d5e3572024-11-30T07:11:45ZengElsevierHeliyon2405-84402024-11-011022e40044Sensitivity analysis of parameters for carbon sequestration: Symbolic regression models based on open porous media reservoir simulators predictionsPavel Praks0Atgeirr Rasmussen1Kjetil Olsen Lye2Jan Martinovič3Renata Praksová4Francesca Watson5Dejan Brkić6IT4Innovations, VSB - Technical University of Ostrava, 708 00, Ostrava, Czech RepublicSINTEF Digital, 0373, Oslo, NorwaySINTEF Digital, 0373, Oslo, NorwayIT4Innovations, VSB - Technical University of Ostrava, 708 00, Ostrava, Czech RepublicIT4Innovations, VSB - Technical University of Ostrava, 708 00, Ostrava, Czech RepublicSINTEF Digital, 0373, Oslo, NorwayIT4Innovations, VSB - Technical University of Ostrava, 708 00, Ostrava, Czech Republic; Faculty of Electronic Engineering, University of Niš, 18000, Niš, Serbia; Corresponding author. IT4Innovations, VSB - Technical University of Ostrava, 708 00, Ostrava, Czech Republic.Open Porous Media (OPM) Flow is an open-source reservoir simulator used for solving subsurface porous media flow problems. Focus is placed here on carbon sequestration and the modeling of fluid flow within underground porous reservoirs. In this study, a sensitivity analysis of some input parameters for carbon sequestration is performed using six different uncertain parameters. An ensemble of model realizations is simulated using OPM Flow, and the model output is then calculated based on the values of the six input parameters mentioned above. CO2 injection is simulated for a period of 15 years, while the post-injection migration of CO2 in the saline storage aquifer is simulated for a subsequent period of 200 years, leading to a final analysis after 215 years. The input parameter values are generated using the quasi-Monte Carlo (QMC) method in the region of interest, following specified patterns suitable for analysis. The optimal convergence rate for quasi-Monte Carlo is observed. The aim of this study is to identify important input parameters contributing significantly to the model output, which is accomplished using sensitivity analysis and verified through symbolic regression modeling based on machine learning. Global sensitivity analysis using the Sobol sequence identifies input parameter 3, 'Permeability of shale between sand layers,' as having the most influence on the model output 'Secondary Trapped CO2.' All regression models, including the simplest and least accurate ones, incorporate parameter 3, confirming its significance. These approximations are valid within the designated area of interest for the input parameters and are easily interpretable for human experts. Sensitivity analysis of the developed time-dependent carbon sequestration model shows that the significance of each physical parameter changes over time: Sand porosity is more significant than shale permeability for roughly the first 120 years. Consequently, the presented results show that simulation timescales of at least 200 years are necessary for carbon sequestration evaluation.http://www.sciencedirect.com/science/article/pii/S2405844024160756Open porous media (OPM) flowCarbon sequestrationSensitivity analysisPetroleum engineeringMachine learning modelingSymbolic regression |
| spellingShingle | Pavel Praks Atgeirr Rasmussen Kjetil Olsen Lye Jan Martinovič Renata Praksová Francesca Watson Dejan Brkić Sensitivity analysis of parameters for carbon sequestration: Symbolic regression models based on open porous media reservoir simulators predictions Heliyon Open porous media (OPM) flow Carbon sequestration Sensitivity analysis Petroleum engineering Machine learning modeling Symbolic regression |
| title | Sensitivity analysis of parameters for carbon sequestration: Symbolic regression models based on open porous media reservoir simulators predictions |
| title_full | Sensitivity analysis of parameters for carbon sequestration: Symbolic regression models based on open porous media reservoir simulators predictions |
| title_fullStr | Sensitivity analysis of parameters for carbon sequestration: Symbolic regression models based on open porous media reservoir simulators predictions |
| title_full_unstemmed | Sensitivity analysis of parameters for carbon sequestration: Symbolic regression models based on open porous media reservoir simulators predictions |
| title_short | Sensitivity analysis of parameters for carbon sequestration: Symbolic regression models based on open porous media reservoir simulators predictions |
| title_sort | sensitivity analysis of parameters for carbon sequestration symbolic regression models based on open porous media reservoir simulators predictions |
| topic | Open porous media (OPM) flow Carbon sequestration Sensitivity analysis Petroleum engineering Machine learning modeling Symbolic regression |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024160756 |
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