Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin
Abstract Geothermal energy is a large, renewable, and clean source of energy from the earth in the form of heat. Exploring the deeper layers of the Williston Basin has revealed favorable reservoir temperatures, particularly in the western areas where high heat flows are prevalent. The quality of a g...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Geothermal Energy |
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| Online Access: | https://doi.org/10.1186/s40517-024-00323-4 |
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| author | Abdul-Muaizz Koray Emmanuel Gyimah Mohamed Metwally Hamid Rahnema Olusegun Tomomewo |
| author_facet | Abdul-Muaizz Koray Emmanuel Gyimah Mohamed Metwally Hamid Rahnema Olusegun Tomomewo |
| author_sort | Abdul-Muaizz Koray |
| collection | DOAJ |
| description | Abstract Geothermal energy is a large, renewable, and clean source of energy from the earth in the form of heat. Exploring the deeper layers of the Williston Basin has revealed favorable reservoir temperatures, particularly in the western areas where high heat flows are prevalent. The quality of a geothermal hotspot hinges on the reservoir quality index (RQI), which is determined by the accuracy of calculating the field reservoir permeability. The primary goal of this study is to apply machine learning techniques to accurately calculate the field permeability, which is important for optimizing the RQI. To enhance accuracy, we initially applied various clustering algorithms, including the density-based spatial clustering of applications with noise (DBSCAN), K-means, K-median, and hierarchical clustering methods, to delineate hydraulic flow units (HFU) within the reservoir using porosity, permeability and water saturation core data. Subsequently, regression models including supervised ML regression methods such as neural networks, support vector machine (SVM) regression, Gaussian process regression (GPR), ensemble regression, linear regression, and decision trees were employed for each flow unit to establish correlations and calculate field permeability with each of these models validated using cross-validation. In comparison to the other clustering methods, the hierarchical clustering method showed the best performance by showing a strong correlation between the actual and predicted permeability values. Overall, the SVM and GPR regression methods were observed to show consistent results with the training and testing datasets, with the SVM regression technique yielding higher R-squared values through regression across the different clustering techniques. In addition, cross-plots were employed to successfully delineate the Red River formation into distinct regions, aiding in the definition of formation lithology and the estimation of field water saturation. Our study showcases an integrated approach to predicting reservoir permeability, considering limited core data. ML emerges as an effective tool for characterizing the Red River formation as a geothermal hotspot in North Dakota, showcasing the potential for sustainable energy exploration and utilization which reduces the reliance on extensive coring in order to enhance geothermal exploration accuracy. |
| format | Article |
| id | doaj-art-1f8f525e41d24757b2385b6964a48121 |
| institution | Kabale University |
| issn | 2195-9706 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Geothermal Energy |
| spelling | doaj-art-1f8f525e41d24757b2385b6964a481212025-01-26T12:23:35ZengSpringerOpenGeothermal Energy2195-97062025-01-0113113310.1186/s40517-024-00323-4Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston BasinAbdul-Muaizz Koray0Emmanuel Gyimah1Mohamed Metwally2Hamid Rahnema3Olusegun Tomomewo4Department of Petroleum Engineering, New Mexico Institute of Mining and TechnologyCollege of Engineering and Mine Energy Studies, University of North DakotaDepartment of Petroleum Engineering, New Mexico Institute of Mining and TechnologyDepartment of Petroleum Engineering, New Mexico Institute of Mining and TechnologyCollege of Engineering and Mine Energy Studies, University of North DakotaAbstract Geothermal energy is a large, renewable, and clean source of energy from the earth in the form of heat. Exploring the deeper layers of the Williston Basin has revealed favorable reservoir temperatures, particularly in the western areas where high heat flows are prevalent. The quality of a geothermal hotspot hinges on the reservoir quality index (RQI), which is determined by the accuracy of calculating the field reservoir permeability. The primary goal of this study is to apply machine learning techniques to accurately calculate the field permeability, which is important for optimizing the RQI. To enhance accuracy, we initially applied various clustering algorithms, including the density-based spatial clustering of applications with noise (DBSCAN), K-means, K-median, and hierarchical clustering methods, to delineate hydraulic flow units (HFU) within the reservoir using porosity, permeability and water saturation core data. Subsequently, regression models including supervised ML regression methods such as neural networks, support vector machine (SVM) regression, Gaussian process regression (GPR), ensemble regression, linear regression, and decision trees were employed for each flow unit to establish correlations and calculate field permeability with each of these models validated using cross-validation. In comparison to the other clustering methods, the hierarchical clustering method showed the best performance by showing a strong correlation between the actual and predicted permeability values. Overall, the SVM and GPR regression methods were observed to show consistent results with the training and testing datasets, with the SVM regression technique yielding higher R-squared values through regression across the different clustering techniques. In addition, cross-plots were employed to successfully delineate the Red River formation into distinct regions, aiding in the definition of formation lithology and the estimation of field water saturation. Our study showcases an integrated approach to predicting reservoir permeability, considering limited core data. ML emerges as an effective tool for characterizing the Red River formation as a geothermal hotspot in North Dakota, showcasing the potential for sustainable energy exploration and utilization which reduces the reliance on extensive coring in order to enhance geothermal exploration accuracy.https://doi.org/10.1186/s40517-024-00323-4Geothermal energyReservoir characterizationMachine learningNeural networkRed River formationHeat flow |
| spellingShingle | Abdul-Muaizz Koray Emmanuel Gyimah Mohamed Metwally Hamid Rahnema Olusegun Tomomewo Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin Geothermal Energy Geothermal energy Reservoir characterization Machine learning Neural network Red River formation Heat flow |
| title | Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin |
| title_full | Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin |
| title_fullStr | Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin |
| title_full_unstemmed | Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin |
| title_short | Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin |
| title_sort | leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots a case study of the williston basin |
| topic | Geothermal energy Reservoir characterization Machine learning Neural network Red River formation Heat flow |
| url | https://doi.org/10.1186/s40517-024-00323-4 |
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