A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil
Predicting fracture intensity is essential for optimising reservoir production and mitigating drilling risks in the Brazilian pre-salt layer. However, previous studies rely excessively on conceptual models and typically do not integrate multiple types of data to perform such task. Moreover, to date,...
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| Format: | Article |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Energy Geoscience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666759225000253 |
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| author | Eberton Rodrigues de Oliveira Neto Fábio Júnior Damasceno Fernandes Tuany Younis Abdul Fatah Raquel Macedo Dias Zoraida Roxana Tejada da Piedade Antonio Fernando Menezes Freire Wagner Moreira Lupinacci |
| author_facet | Eberton Rodrigues de Oliveira Neto Fábio Júnior Damasceno Fernandes Tuany Younis Abdul Fatah Raquel Macedo Dias Zoraida Roxana Tejada da Piedade Antonio Fernando Menezes Freire Wagner Moreira Lupinacci |
| author_sort | Eberton Rodrigues de Oliveira Neto |
| collection | DOAJ |
| description | Predicting fracture intensity is essential for optimising reservoir production and mitigating drilling risks in the Brazilian pre-salt layer. However, previous studies rely excessively on conceptual models and typically do not integrate multiple types of data to perform such task. Moreover, to date, no feasibility-like studies have assessed the reasonableness of such approaches. We propose a data-driven approach that utilises upscaled well logs (Young's modulus, Poisson's ratio, and silica content) alongside seismic attributes (curvature, distance to fault) to predict fracture intensity. The distance to fault is measured using the fault probability volume estimated by a pre-trained convolutional neural network (CNN). We evaluate the effectiveness of this data-driven approach employing two tree-ensemble models, eXtreme Gradient Boosting (XGBoost) and Random Forest, to estimate the volumetric fracture intensity (P32) in the wells. Regression and residual analyses indicate that XGBoost outperforms Random Forest. Results from feature importance methods, such as permutation importance and Shapley Additive explanations (SHAP), highlight curvature as the most important feature, followed by distance to fault, Young's modulus (or P-Impedance), silica content, and Poisson's ratio. The approach has been validated with rock sampling information and two blind tests. Consequently, we believe this workflow can be applied to other wells in nearby fields. The study offers a valuable tool for quantitatively estimating fracture intensity in pre-salt reservoirs. Future research may use this study as a reference for estimating fracture intensity within a seismic volume. The predicted fracture intensity estimates can enhance the reliability of reservoir porosity models and serve as a geohazard indicator to mitigate drilling risks. |
| format | Article |
| id | doaj-art-01b46ac87aee44bfa75250edc43059aa |
| institution | OA Journals |
| issn | 2666-7592 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Energy Geoscience |
| spelling | doaj-art-01b46ac87aee44bfa75250edc43059aa2025-08-20T02:23:11ZengKeAi Communications Co., Ltd.Energy Geoscience2666-75922025-06-016210040410.1016/j.engeos.2025.100404A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, BrazilEberton Rodrigues de Oliveira Neto0Fábio Júnior Damasceno Fernandes1Tuany Younis Abdul Fatah2Raquel Macedo Dias3Zoraida Roxana Tejada da Piedade4Antonio Fernando Menezes Freire5Wagner Moreira Lupinacci6GIECAR, Federal Fluminense University, Niterói, Rio de Janeiro, 24210346, Brazil; Corresponding author.GIECAR, Federal Fluminense University, Niterói, Rio de Janeiro, 24210346, BrazilGIECAR, Federal Fluminense University, Niterói, Rio de Janeiro, 24210346, BrazilGIECAR, Federal Fluminense University, Niterói, Rio de Janeiro, 24210346, BrazilGIECAR, Federal Fluminense University, Niterói, Rio de Janeiro, 24210346, BrazilGIECAR, Federal Fluminense University, Niterói, Rio de Janeiro, 24210346, Brazil; National Institute of Science and Technology of Petroleum Geophysics (INCT-GP/CNPQ), Niterói, Rio de Janeiro, 24210346, BrazilGIECAR, Federal Fluminense University, Niterói, Rio de Janeiro, 24210346, Brazil; National Institute of Science and Technology of Petroleum Geophysics (INCT-GP/CNPQ), Niterói, Rio de Janeiro, 24210346, BrazilPredicting fracture intensity is essential for optimising reservoir production and mitigating drilling risks in the Brazilian pre-salt layer. However, previous studies rely excessively on conceptual models and typically do not integrate multiple types of data to perform such task. Moreover, to date, no feasibility-like studies have assessed the reasonableness of such approaches. We propose a data-driven approach that utilises upscaled well logs (Young's modulus, Poisson's ratio, and silica content) alongside seismic attributes (curvature, distance to fault) to predict fracture intensity. The distance to fault is measured using the fault probability volume estimated by a pre-trained convolutional neural network (CNN). We evaluate the effectiveness of this data-driven approach employing two tree-ensemble models, eXtreme Gradient Boosting (XGBoost) and Random Forest, to estimate the volumetric fracture intensity (P32) in the wells. Regression and residual analyses indicate that XGBoost outperforms Random Forest. Results from feature importance methods, such as permutation importance and Shapley Additive explanations (SHAP), highlight curvature as the most important feature, followed by distance to fault, Young's modulus (or P-Impedance), silica content, and Poisson's ratio. The approach has been validated with rock sampling information and two blind tests. Consequently, we believe this workflow can be applied to other wells in nearby fields. The study offers a valuable tool for quantitatively estimating fracture intensity in pre-salt reservoirs. Future research may use this study as a reference for estimating fracture intensity within a seismic volume. The predicted fracture intensity estimates can enhance the reliability of reservoir porosity models and serve as a geohazard indicator to mitigate drilling risks.http://www.sciencedirect.com/science/article/pii/S2666759225000253K1 curvatureNaturally fractured reservoirsP32Machine learningFeature importance |
| spellingShingle | Eberton Rodrigues de Oliveira Neto Fábio Júnior Damasceno Fernandes Tuany Younis Abdul Fatah Raquel Macedo Dias Zoraida Roxana Tejada da Piedade Antonio Fernando Menezes Freire Wagner Moreira Lupinacci A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil Energy Geoscience K1 curvature Naturally fractured reservoirs P32 Machine learning Feature importance |
| title | A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil |
| title_full | A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil |
| title_fullStr | A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil |
| title_full_unstemmed | A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil |
| title_short | A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil |
| title_sort | data driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs a feasibility study in the mero field santos basin brazil |
| topic | K1 curvature Naturally fractured reservoirs P32 Machine learning Feature importance |
| url | http://www.sciencedirect.com/science/article/pii/S2666759225000253 |
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