Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools
The study evaluates the geothermal energy potential of two depleted oil and gas reservoirs representing two different lithostratigraphic formations—the carbonate formation of the Visean age from the basement of the Carpathian Flysch and the Rotliegend sandstone formation from the Eastern part of the...
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MDPI AG
2023-07-01
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Online Access: | https://www.mdpi.com/1996-1073/16/13/5211 |
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author | Tomasz Topór Małgorzata Słota-Valim Rafał Kudrewicz |
author_facet | Tomasz Topór Małgorzata Słota-Valim Rafał Kudrewicz |
author_sort | Tomasz Topór |
collection | DOAJ |
description | The study evaluates the geothermal energy potential of two depleted oil and gas reservoirs representing two different lithostratigraphic formations—the carbonate formation of the Visean age from the basement of the Carpathian Flysch and the Rotliegend sandstone formation from the Eastern part of the Foresudetic Monocline, Poland. Advanced modeling techniques were employed to analyze the studied formations’ heat, storage, and transport properties. The obtained results were then used to calculate the heat in place (HIP) and evaluate the recoverable heat (Hrec) for both water and CO<sub>2</sub> as working fluids, considering a geothermal system lifetime of 50 years. The petrophysical parameters and Hrec were subsequently utilized in the generalized c-means (GFCM) clustering analysis, which helped to identify plays with the greatest geothermal potential within the studied formations. The central block emerged as the most promising area for the studied carbonate formation with Hrec values of ~1.12 and 0.26 MW when H<sub>2</sub>O and CO<sub>2</sub> were used as working fluids, respectively. The central block has three wells that can be easily adapted for geothermal production. The area, however, may require permeability enhancement techniques to increase reservoir permeability. Two prospective zones were determined for the analyzed Rotliegend sandstone formation: one in the NW region and the other in the SE region. In the NW region, the estimated Hrec was 23.16 MW and 4.36 MW, while in the SE region, it was 19.76 MW and 3.51 MW, using H<sub>2</sub>O and CO<sub>2</sub> as working fluids, respectively. Both areas have high porosity and permeability, providing good storage and transport properties for the working fluid, and abundant wells that can be configured for multiple injection-production systems. When comparing the efficiency of geothermal systems, the water-driven system in the Visean carbonate formation turned out to be over four times more efficient than the CO<sub>2</sub>-driven one. Furthermore, in the case of the Rotliegend sandstone formation, it was possible to access over five times more heat using water-driven system. |
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issn | 1996-1073 |
language | English |
last_indexed | 2024-03-11T01:41:56Z |
publishDate | 2023-07-01 |
publisher | MDPI AG |
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series | Energies |
spelling | doaj.art-522eb410d8494f2e8762c6e4bf4de9762023-11-18T16:32:02ZengMDPI AGEnergies1996-10732023-07-011613521110.3390/en16135211Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning ToolsTomasz Topór0Małgorzata Słota-Valim1Rafał Kudrewicz2Oil and Gas Institute—National Research Institute, 25 A Lubicz Str., 31-503 Cracow, PolandOil and Gas Institute—National Research Institute, 25 A Lubicz Str., 31-503 Cracow, PolandPKN Orlen-PGNiG Exploration & Production Division, Kasprzaka 25a, 01-224 Warszawa, PolandThe study evaluates the geothermal energy potential of two depleted oil and gas reservoirs representing two different lithostratigraphic formations—the carbonate formation of the Visean age from the basement of the Carpathian Flysch and the Rotliegend sandstone formation from the Eastern part of the Foresudetic Monocline, Poland. Advanced modeling techniques were employed to analyze the studied formations’ heat, storage, and transport properties. The obtained results were then used to calculate the heat in place (HIP) and evaluate the recoverable heat (Hrec) for both water and CO<sub>2</sub> as working fluids, considering a geothermal system lifetime of 50 years. The petrophysical parameters and Hrec were subsequently utilized in the generalized c-means (GFCM) clustering analysis, which helped to identify plays with the greatest geothermal potential within the studied formations. The central block emerged as the most promising area for the studied carbonate formation with Hrec values of ~1.12 and 0.26 MW when H<sub>2</sub>O and CO<sub>2</sub> were used as working fluids, respectively. The central block has three wells that can be easily adapted for geothermal production. The area, however, may require permeability enhancement techniques to increase reservoir permeability. Two prospective zones were determined for the analyzed Rotliegend sandstone formation: one in the NW region and the other in the SE region. In the NW region, the estimated Hrec was 23.16 MW and 4.36 MW, while in the SE region, it was 19.76 MW and 3.51 MW, using H<sub>2</sub>O and CO<sub>2</sub> as working fluids, respectively. Both areas have high porosity and permeability, providing good storage and transport properties for the working fluid, and abundant wells that can be configured for multiple injection-production systems. When comparing the efficiency of geothermal systems, the water-driven system in the Visean carbonate formation turned out to be over four times more efficient than the CO<sub>2</sub>-driven one. Furthermore, in the case of the Rotliegend sandstone formation, it was possible to access over five times more heat using water-driven system.https://www.mdpi.com/1996-1073/16/13/5211machine learningGFCM clusteringgeological modelinggeothermal resource exploration |
spellingShingle | Tomasz Topór Małgorzata Słota-Valim Rafał Kudrewicz Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools Energies machine learning GFCM clustering geological modeling geothermal resource exploration |
title | Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools |
title_full | Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools |
title_fullStr | Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools |
title_full_unstemmed | Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools |
title_short | Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools |
title_sort | assessing the geothermal potential of selected depleted oil and gas reservoirs based on geological modeling and machine learning tools |
topic | machine learning GFCM clustering geological modeling geothermal resource exploration |
url | https://www.mdpi.com/1996-1073/16/13/5211 |
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