Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
Formation damage remains a key challenge in oil and gas exploration and development, requiring effective prediction and diagnostic technologies to mitigate its impact. Despite decades of research, current techniques lack the accuracy and practicality demanded by modern oilfield operations and the fu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/3/1169 |
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| author | Zhe Sun Zhangxing Chen |
| author_facet | Zhe Sun Zhangxing Chen |
| author_sort | Zhe Sun |
| collection | DOAJ |
| description | Formation damage remains a key challenge in oil and gas exploration and development, requiring effective prediction and diagnostic technologies to mitigate its impact. Despite decades of research, current techniques lack the accuracy and practicality demanded by modern oilfield operations and the future of intelligent oil and gas development. This study systematically reviews advancements in formation damage prediction and diagnostics, focusing on wellsite diagnosis, experimental methods, imaging techniques, analytical approaches, numerical modeling, and artificial intelligence applications. The advantages and limitations of these methods are analyzed to provide a comprehensive understanding of their capabilities. The paper emphasizes the need for further research to develop an intelligent expert system that integrates multiple damage factors and accounts for spatial–temporal evolution, paving the way for improved future hydrocarbon production and sustainable energy development. |
| format | Article |
| id | doaj-art-97e018cb199642f3a9c3a4201e1cc166 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-97e018cb199642f3a9c3a4201e1cc1662025-08-20T02:48:01ZengMDPI AGApplied Sciences2076-34172025-01-01153116910.3390/app15031169Research Status and Development Direction of Formation Damage Prediction and Diagnosis TechnologiesZhe Sun0Zhangxing Chen1Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaFormation damage remains a key challenge in oil and gas exploration and development, requiring effective prediction and diagnostic technologies to mitigate its impact. Despite decades of research, current techniques lack the accuracy and practicality demanded by modern oilfield operations and the future of intelligent oil and gas development. This study systematically reviews advancements in formation damage prediction and diagnostics, focusing on wellsite diagnosis, experimental methods, imaging techniques, analytical approaches, numerical modeling, and artificial intelligence applications. The advantages and limitations of these methods are analyzed to provide a comprehensive understanding of their capabilities. The paper emphasizes the need for further research to develop an intelligent expert system that integrates multiple damage factors and accounts for spatial–temporal evolution, paving the way for improved future hydrocarbon production and sustainable energy development.https://www.mdpi.com/2076-3417/15/3/1169formation damageformation damage controlartificial intelligencenumerical simulationmathematical modelingwell testing |
| spellingShingle | Zhe Sun Zhangxing Chen Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies Applied Sciences formation damage formation damage control artificial intelligence numerical simulation mathematical modeling well testing |
| title | Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies |
| title_full | Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies |
| title_fullStr | Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies |
| title_full_unstemmed | Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies |
| title_short | Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies |
| title_sort | research status and development direction of formation damage prediction and diagnosis technologies |
| topic | formation damage formation damage control artificial intelligence numerical simulation mathematical modeling well testing |
| url | https://www.mdpi.com/2076-3417/15/3/1169 |
| work_keys_str_mv | AT zhesun researchstatusanddevelopmentdirectionofformationdamagepredictionanddiagnosistechnologies AT zhangxingchen researchstatusanddevelopmentdirectionofformationdamagepredictionanddiagnosistechnologies |