Unsupervised Electrofacies Classification and Porosity Characterization in the Offshore Keta Basin Using Wireline Logs
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909001463103488 |
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| author | Adams, Hamdiya Ansah-Narh, Theophilus Asiedu, Daniel Kwadwo Banoeng-Yakubo, Bruce Kofi Atemkeng, Marcellin Armah, Thomas Opoku-Sarkodie, Richmond Davis, Rebecca Nortey, Ezekiel Nii Noye |
| author_facet | Adams, Hamdiya Ansah-Narh, Theophilus Asiedu, Daniel Kwadwo Banoeng-Yakubo, Bruce Kofi Atemkeng, Marcellin Armah, Thomas Opoku-Sarkodie, Richmond Davis, Rebecca Nortey, Ezekiel Nii Noye |
| contents | This study presents an unsupervised machine learning workflow for electrofacies analysis in the offshore Keta Basin, Ghana, where core data are scarce. Six standard wireline logs from Well~C were analysed over a depth interval comprising approximately $11{,}195$ samples. K-means clustering was applied in multivariate log space, with the clustering structure evaluated using inertia and silhouette diagnostics. Four clusters were identified, supported by an average silhouette coefficient of approximately $0.50$, indicating moderate but meaningful separation. The resulting electrofacies exhibit systematic, depth-continuous patterns associated with variations in clay content, porosity, and rock framework properties, forming a geological continuum from shale-dominated to cleaner sandstone-dominated units. The results demonstrate that log-only, unsupervised clustering supported by quantitative metrics provides a robust and reproducible framework for subsurface characterisation. The proposed workflow offers a practical tool for early-stage formation evaluation in frontier offshore basins and a foundation for future integrated studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27126 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unsupervised Electrofacies Classification and Porosity Characterization in the Offshore Keta Basin Using Wireline Logs Adams, Hamdiya Ansah-Narh, Theophilus Asiedu, Daniel Kwadwo Banoeng-Yakubo, Bruce Kofi Atemkeng, Marcellin Armah, Thomas Opoku-Sarkodie, Richmond Davis, Rebecca Nortey, Ezekiel Nii Noye Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Geophysics This study presents an unsupervised machine learning workflow for electrofacies analysis in the offshore Keta Basin, Ghana, where core data are scarce. Six standard wireline logs from Well~C were analysed over a depth interval comprising approximately $11{,}195$ samples. K-means clustering was applied in multivariate log space, with the clustering structure evaluated using inertia and silhouette diagnostics. Four clusters were identified, supported by an average silhouette coefficient of approximately $0.50$, indicating moderate but meaningful separation. The resulting electrofacies exhibit systematic, depth-continuous patterns associated with variations in clay content, porosity, and rock framework properties, forming a geological continuum from shale-dominated to cleaner sandstone-dominated units. The results demonstrate that log-only, unsupervised clustering supported by quantitative metrics provides a robust and reproducible framework for subsurface characterisation. The proposed workflow offers a practical tool for early-stage formation evaluation in frontier offshore basins and a foundation for future integrated studies. |
| title | Unsupervised Electrofacies Classification and Porosity Characterization in the Offshore Keta Basin Using Wireline Logs |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Geophysics |
| url | https://arxiv.org/abs/2604.27126 |