A Review of AI Applications in Unconventional Oil and Gas Exploration and Development
The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and develo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/391 |
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| author | Feiyu Chen Linghui Sun Boyu Jiang Xu Huo Xiuxiu Pan Chun Feng Zhirong Zhang |
| author_facet | Feiyu Chen Linghui Sun Boyu Jiang Xu Huo Xiuxiu Pan Chun Feng Zhirong Zhang |
| author_sort | Feiyu Chen |
| collection | DOAJ |
| description | The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and development trends of AI in unconventional oil and gas exploration and development, covering major research achievements in geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced oil recovery; and health, safety, and environment management. This paper reviews how deep learning helps predict gas distribution and classify rock types. It also explains how machine learning improves reservoir simulation and history matching. Additionally, we discuss the use of LSTM and DNN models in production forecasting, showing how AI has progressed from early experiments to fully integrated solutions. However, challenges such as data quality, model generalization, and interpretability remain significant. Based on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with engineering mechanisms; and advancing interpretable modeling and transfer learning techniques. With next-generation intelligent systems, AI will further improve efficiency and sustainability in unconventional oil and gas development. |
| format | Article |
| id | doaj-art-eabe4b9b41534f84a647b82b7b12dcbb |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-eabe4b9b41534f84a647b82b7b12dcbb2025-01-24T13:31:19ZengMDPI AGEnergies1996-10732025-01-0118239110.3390/en18020391A Review of AI Applications in Unconventional Oil and Gas Exploration and DevelopmentFeiyu Chen0Linghui Sun1Boyu Jiang2Xu Huo3Xiuxiu Pan4Chun Feng5Zhirong Zhang6University of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaState Key Laboratory of Enhanced Oil & Gas Recovery, Research Institute of Petroleum Exploration & Development, Beijing 100083, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaThe development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and development trends of AI in unconventional oil and gas exploration and development, covering major research achievements in geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced oil recovery; and health, safety, and environment management. This paper reviews how deep learning helps predict gas distribution and classify rock types. It also explains how machine learning improves reservoir simulation and history matching. Additionally, we discuss the use of LSTM and DNN models in production forecasting, showing how AI has progressed from early experiments to fully integrated solutions. However, challenges such as data quality, model generalization, and interpretability remain significant. Based on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with engineering mechanisms; and advancing interpretable modeling and transfer learning techniques. With next-generation intelligent systems, AI will further improve efficiency and sustainability in unconventional oil and gas development.https://www.mdpi.com/1996-1073/18/2/391artificial intelligenceunconventional reservoirgeological explorationproduction forecasting |
| spellingShingle | Feiyu Chen Linghui Sun Boyu Jiang Xu Huo Xiuxiu Pan Chun Feng Zhirong Zhang A Review of AI Applications in Unconventional Oil and Gas Exploration and Development Energies artificial intelligence unconventional reservoir geological exploration production forecasting |
| title | A Review of AI Applications in Unconventional Oil and Gas Exploration and Development |
| title_full | A Review of AI Applications in Unconventional Oil and Gas Exploration and Development |
| title_fullStr | A Review of AI Applications in Unconventional Oil and Gas Exploration and Development |
| title_full_unstemmed | A Review of AI Applications in Unconventional Oil and Gas Exploration and Development |
| title_short | A Review of AI Applications in Unconventional Oil and Gas Exploration and Development |
| title_sort | review of ai applications in unconventional oil and gas exploration and development |
| topic | artificial intelligence unconventional reservoir geological exploration production forecasting |
| url | https://www.mdpi.com/1996-1073/18/2/391 |
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