Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring
Abstract Sand control is an ongoing challenge in numerous hydrocarbon-producing wells in sand-rich reservoirs. Sand production in these wells can cause damage to equipment, reduce production rates, and lead to erosion that can damage subsea equipment, production equipment, well completions, and surf...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-08-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
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| Online Access: | https://doi.org/10.1007/s13202-024-01860-1 |
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| author | Dejen Teklu Asfha Abdul Halim Abdul Latiff Daniel Asante Otchere Bennet Nii Tackie-Otoo Ismailalwali Babikir Muhammad Rafi Zaky Ahmad Riyadi Ahmad Dedi Putra Bamidele Abdulhakeem Adeniyi |
| author_facet | Dejen Teklu Asfha Abdul Halim Abdul Latiff Daniel Asante Otchere Bennet Nii Tackie-Otoo Ismailalwali Babikir Muhammad Rafi Zaky Ahmad Riyadi Ahmad Dedi Putra Bamidele Abdulhakeem Adeniyi |
| author_sort | Dejen Teklu Asfha |
| collection | DOAJ |
| description | Abstract Sand control is an ongoing challenge in numerous hydrocarbon-producing wells in sand-rich reservoirs. Sand production in these wells can cause damage to equipment, reduce production rates, and lead to erosion that can damage subsea equipment, production equipment, well completions, and surface facilities. This problem can compromise the mechanical integrity of the well, resulting in reduced hydrocarbon production and increased operating expenses. This review evaluates various sand production mechanisms, including geological and mechanical production methodologies, and fluid-related aspects, which are thoroughly investigated to offer a thorough understanding of the complexity of the issue and the state of sand prediction approaches. Empirical correlations, numerical simulations, and analytical models are among the sand production prediction techniques critically assessed in this study. The benefits, drawbacks, and suitability of these techniques for various reservoir environments are discussed. Furthermore, the potential benefits of combining Fiber optic (FO) technologies and machine learning (ML) for real-time monitoring and mitigation are highlighted. This integrated strategy has the potential to transform sand control practices of the industry, as demonstrated by case studies and new research that highlights its effectiveness. The future vision outlined in this review includes developments in automation, data processing methods, and sensor technologies, which should improve the precision and dependability of sand production predictions and mitigation. In conclusion, this review paper provides an extensive analysis of the current level of prediction techniques, as well as the mechanisms behind sand production in oil and gas wells. This highlights how real-time, data-driven solutions for monitoring and addressing sand production problems may be provided by FO and ML, which can ultimately lead to safer and more effective hydrocarbon recovery operations. |
| format | Article |
| id | doaj-art-bb427365e0eb4f4e93d1f1fc4f2f1430 |
| institution | OA Journals |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-bb427365e0eb4f4e93d1f1fc4f2f14302025-08-20T02:18:19ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662024-08-0114102577261610.1007/s13202-024-01860-1Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoringDejen Teklu Asfha0Abdul Halim Abdul Latiff1Daniel Asante Otchere2Bennet Nii Tackie-Otoo3Ismailalwali Babikir4Muhammad Rafi5Zaky Ahmad Riyadi6Ahmad Dedi Putra7Bamidele Abdulhakeem Adeniyi8Centre for Subsurface Imaging, Department of Geosciences, Universiti Teknologi PETRONASCentre for Subsurface Imaging, Department of Geosciences, Universiti Teknologi PETRONASInstitute for Computational and Data Sciences, The Pennsylvania State UniversityCentre for Subsurface Imaging, Department of Geosciences, Universiti Teknologi PETRONASCentre for Subsurface Imaging, Department of Geosciences, Universiti Teknologi PETRONASCentre for Subsurface Imaging, Department of Geosciences, Universiti Teknologi PETRONASCentre for Subsurface Imaging, Department of Geosciences, Universiti Teknologi PETRONASCentre for Subsurface Imaging, Department of Geosciences, Universiti Teknologi PETRONASCentre for Subsurface Imaging, Department of Geosciences, Universiti Teknologi PETRONASAbstract Sand control is an ongoing challenge in numerous hydrocarbon-producing wells in sand-rich reservoirs. Sand production in these wells can cause damage to equipment, reduce production rates, and lead to erosion that can damage subsea equipment, production equipment, well completions, and surface facilities. This problem can compromise the mechanical integrity of the well, resulting in reduced hydrocarbon production and increased operating expenses. This review evaluates various sand production mechanisms, including geological and mechanical production methodologies, and fluid-related aspects, which are thoroughly investigated to offer a thorough understanding of the complexity of the issue and the state of sand prediction approaches. Empirical correlations, numerical simulations, and analytical models are among the sand production prediction techniques critically assessed in this study. The benefits, drawbacks, and suitability of these techniques for various reservoir environments are discussed. Furthermore, the potential benefits of combining Fiber optic (FO) technologies and machine learning (ML) for real-time monitoring and mitigation are highlighted. This integrated strategy has the potential to transform sand control practices of the industry, as demonstrated by case studies and new research that highlights its effectiveness. The future vision outlined in this review includes developments in automation, data processing methods, and sensor technologies, which should improve the precision and dependability of sand production predictions and mitigation. In conclusion, this review paper provides an extensive analysis of the current level of prediction techniques, as well as the mechanisms behind sand production in oil and gas wells. This highlights how real-time, data-driven solutions for monitoring and addressing sand production problems may be provided by FO and ML, which can ultimately lead to safer and more effective hydrocarbon recovery operations.https://doi.org/10.1007/s13202-024-01860-1Sand productionMechanisms of sand productionSand production prediction and monitoringFiber optic (FO) technologyMachine learning (ML) |
| spellingShingle | Dejen Teklu Asfha Abdul Halim Abdul Latiff Daniel Asante Otchere Bennet Nii Tackie-Otoo Ismailalwali Babikir Muhammad Rafi Zaky Ahmad Riyadi Ahmad Dedi Putra Bamidele Abdulhakeem Adeniyi Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring Journal of Petroleum Exploration and Production Technology Sand production Mechanisms of sand production Sand production prediction and monitoring Fiber optic (FO) technology Machine learning (ML) |
| title | Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring |
| title_full | Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring |
| title_fullStr | Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring |
| title_full_unstemmed | Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring |
| title_short | Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring |
| title_sort | mechanisms of sand production prediction a review and the potential for fiber optic technology and machine learning in monitoring |
| topic | Sand production Mechanisms of sand production Sand production prediction and monitoring Fiber optic (FO) technology Machine learning (ML) |
| url | https://doi.org/10.1007/s13202-024-01860-1 |
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