Quantifying the potential of the Raniganj Basin for shale gas exploration and CO2 sequestration using a deep learning framework
Abstract One key parameter for estimating shale gas potential and evaluating CO2 storage capacity in shale formation is total organic carbon (TOC) content. Traditional TOC estimation methods, such as the ΔlogR technique and shallow artificial neural networks (ANN), often exhibit low correlation with...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-03-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
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| Online Access: | https://doi.org/10.1007/s13202-025-01978-w |
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| author | Nasif Ahmed Shaik Mannat Khanna Nimisha Vedanti |
| author_facet | Nasif Ahmed Shaik Mannat Khanna Nimisha Vedanti |
| author_sort | Nasif Ahmed Shaik |
| collection | DOAJ |
| description | Abstract One key parameter for estimating shale gas potential and evaluating CO2 storage capacity in shale formation is total organic carbon (TOC) content. Traditional TOC estimation methods, such as the ΔlogR technique and shallow artificial neural networks (ANN), often exhibit low correlation with laboratory measurements, limiting their applicability. This study presents a deep learning model (DLM) designed for improved prediction of TOC. The proposed model is a multilayer perceptron neural network trained using core-derived TOC values and geophysical well logs (density, resistivity, gamma ray, and caliper logs). The Pearson correlation coefficient (r) for the model predicted TOC compared with the lab-measured TOC is 0.8, demonstrating better performance than conventional techniques. The model was further trained to predict the S2 (pyrolyzable hydrocarbons) factor and enhance shale characterization. Predicted TOC and S2 values were then used to calculate the Hydrogen Index (HI) and classify kerogen type. Integrating this predictive modeling with Rock-Eval, mineralogical, and geomechanical analyses establishes a comprehensive methodology for evaluating shale gas prospects and CO2 storage potential. High TOC content (averaging 6.38%), Type III gas-prone kerogen, and favorable elastic and mechanical properties for hydraulic fracturing highlight the potential of Barren Measures shales in the Raniganj Basin, India, for shale gas exploration and CO2 sequestration. |
| format | Article |
| id | doaj-art-f320ea9b2d3d4fca965e242cbf863ce5 |
| institution | DOAJ |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-f320ea9b2d3d4fca965e242cbf863ce52025-08-20T03:14:09ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-03-0115411710.1007/s13202-025-01978-wQuantifying the potential of the Raniganj Basin for shale gas exploration and CO2 sequestration using a deep learning frameworkNasif Ahmed Shaik0Mannat Khanna1Nimisha Vedanti2CSIR-National Geophysical Research InstituteRezlytix TechnologiesCSIR-National Geophysical Research InstituteAbstract One key parameter for estimating shale gas potential and evaluating CO2 storage capacity in shale formation is total organic carbon (TOC) content. Traditional TOC estimation methods, such as the ΔlogR technique and shallow artificial neural networks (ANN), often exhibit low correlation with laboratory measurements, limiting their applicability. This study presents a deep learning model (DLM) designed for improved prediction of TOC. The proposed model is a multilayer perceptron neural network trained using core-derived TOC values and geophysical well logs (density, resistivity, gamma ray, and caliper logs). The Pearson correlation coefficient (r) for the model predicted TOC compared with the lab-measured TOC is 0.8, demonstrating better performance than conventional techniques. The model was further trained to predict the S2 (pyrolyzable hydrocarbons) factor and enhance shale characterization. Predicted TOC and S2 values were then used to calculate the Hydrogen Index (HI) and classify kerogen type. Integrating this predictive modeling with Rock-Eval, mineralogical, and geomechanical analyses establishes a comprehensive methodology for evaluating shale gas prospects and CO2 storage potential. High TOC content (averaging 6.38%), Type III gas-prone kerogen, and favorable elastic and mechanical properties for hydraulic fracturing highlight the potential of Barren Measures shales in the Raniganj Basin, India, for shale gas exploration and CO2 sequestration.https://doi.org/10.1007/s13202-025-01978-wDeep learning modelTotal organic carbonShale gasCO2 sequestrationBarren Measures shaleRaniganj Basin |
| spellingShingle | Nasif Ahmed Shaik Mannat Khanna Nimisha Vedanti Quantifying the potential of the Raniganj Basin for shale gas exploration and CO2 sequestration using a deep learning framework Journal of Petroleum Exploration and Production Technology Deep learning model Total organic carbon Shale gas CO2 sequestration Barren Measures shale Raniganj Basin |
| title | Quantifying the potential of the Raniganj Basin for shale gas exploration and CO2 sequestration using a deep learning framework |
| title_full | Quantifying the potential of the Raniganj Basin for shale gas exploration and CO2 sequestration using a deep learning framework |
| title_fullStr | Quantifying the potential of the Raniganj Basin for shale gas exploration and CO2 sequestration using a deep learning framework |
| title_full_unstemmed | Quantifying the potential of the Raniganj Basin for shale gas exploration and CO2 sequestration using a deep learning framework |
| title_short | Quantifying the potential of the Raniganj Basin for shale gas exploration and CO2 sequestration using a deep learning framework |
| title_sort | quantifying the potential of the raniganj basin for shale gas exploration and co2 sequestration using a deep learning framework |
| topic | Deep learning model Total organic carbon Shale gas CO2 sequestration Barren Measures shale Raniganj Basin |
| url | https://doi.org/10.1007/s13202-025-01978-w |
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