3D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models
Abstract This study aims to investigate the limitations of geostatistical prediction models outside the observed data range for estimating rock strength in nonreservoir formations in large geological fields with limited wireline data. To address this gap, this method explores alternative approaches...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13202-025-02017-4 |
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| _version_ | 1849345218541780992 |
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| author | Hichem Horra Ahmed Hadjadj Elfakeur Abidi Saad Khalil Moulay Brahim |
| author_facet | Hichem Horra Ahmed Hadjadj Elfakeur Abidi Saad Khalil Moulay Brahim |
| author_sort | Hichem Horra |
| collection | DOAJ |
| description | Abstract This study aims to investigate the limitations of geostatistical prediction models outside the observed data range for estimating rock strength in nonreservoir formations in large geological fields with limited wireline data. To address this gap, this method explores alternative approaches to estimate rock strength using minimum data. A novel 3D rock strength prediction model that integrates geostatistic with deep learning algorithms is proposed. Initially, the deep learning model is trained using the available dataset to capture the complex nonlinear relationships within the data. The developed model is used to increase the dataset size by focusing on nearby data points to mitigate geological variability. geostatistic methods are then applied to establish spatial correlations of rock strength across an extended range compared with those of the actual dataset. The results reveal marked improvements in both the prediction range and spatial resolution of rock strength through the proposed methodology. The developed deep learning models achieved coefficient of determination values ranging from 0.9 to 0.99, demonstrating excellent predictive capability. Cross-validation confirms the model effectively captures local variations. The prediction range in the field expanded by 250% compared to the initial dataset, successfully addressing areas that previously exhibited flat readings when the model was applied to the initial data. This study advances petroleum industry knowledge by integrating deep learning and geostatistical methods to overcome rock strength prediction limitations in nonreservoir formations. The novel 3D model enhances the prediction range and spatial resolution, addresses data gaps and enables better decision-making for areas with limited wireline data. |
| format | Article |
| id | doaj-art-0e740392b5184c739726f79bd5bb472b |
| institution | Kabale University |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-0e740392b5184c739726f79bd5bb472b2025-08-20T03:42:30ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-06-0115711510.1007/s13202-025-02017-43D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning modelsHichem Horra0Ahmed Hadjadj1Elfakeur Abidi Saad2Khalil Moulay Brahim3University of OuarglaUniversity of AdrarUniversity of OuarglaUniversity of OuarglaAbstract This study aims to investigate the limitations of geostatistical prediction models outside the observed data range for estimating rock strength in nonreservoir formations in large geological fields with limited wireline data. To address this gap, this method explores alternative approaches to estimate rock strength using minimum data. A novel 3D rock strength prediction model that integrates geostatistic with deep learning algorithms is proposed. Initially, the deep learning model is trained using the available dataset to capture the complex nonlinear relationships within the data. The developed model is used to increase the dataset size by focusing on nearby data points to mitigate geological variability. geostatistic methods are then applied to establish spatial correlations of rock strength across an extended range compared with those of the actual dataset. The results reveal marked improvements in both the prediction range and spatial resolution of rock strength through the proposed methodology. The developed deep learning models achieved coefficient of determination values ranging from 0.9 to 0.99, demonstrating excellent predictive capability. Cross-validation confirms the model effectively captures local variations. The prediction range in the field expanded by 250% compared to the initial dataset, successfully addressing areas that previously exhibited flat readings when the model was applied to the initial data. This study advances petroleum industry knowledge by integrating deep learning and geostatistical methods to overcome rock strength prediction limitations in nonreservoir formations. The novel 3D model enhances the prediction range and spatial resolution, addresses data gaps and enables better decision-making for areas with limited wireline data.https://doi.org/10.1007/s13202-025-02017-43D rock strength predictionNonreservoir formationsLogging data limitationsPrediction modelGeostatisticsDeep learning model |
| spellingShingle | Hichem Horra Ahmed Hadjadj Elfakeur Abidi Saad Khalil Moulay Brahim 3D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models Journal of Petroleum Exploration and Production Technology 3D rock strength prediction Nonreservoir formations Logging data limitations Prediction model Geostatistics Deep learning model |
| title | 3D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models |
| title_full | 3D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models |
| title_fullStr | 3D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models |
| title_full_unstemmed | 3D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models |
| title_short | 3D rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models |
| title_sort | 3d rock strength prediction by an innovative approach that integrates geostatistics with machine deep learning models |
| topic | 3D rock strength prediction Nonreservoir formations Logging data limitations Prediction model Geostatistics Deep learning model |
| url | https://doi.org/10.1007/s13202-025-02017-4 |
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