Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method
Based on on-site construction experience, considering the time-varying characteristics of gas well quantity, production time, effective reservoir thickness, controlled reserves, reserve abundance, formation pressure, and the energy storage coefficient, a data-driven method was used to establish a na...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/21/5461 |
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| author | Haijie Zhang Junwei Pu Li Zhang Hengjian Deng Jihao Yu Yingming Xie Xiaochang Tong Xiangjie Man Zhonghua Liu |
| author_facet | Haijie Zhang Junwei Pu Li Zhang Hengjian Deng Jihao Yu Yingming Xie Xiaochang Tong Xiangjie Man Zhonghua Liu |
| author_sort | Haijie Zhang |
| collection | DOAJ |
| description | Based on on-site construction experience, considering the time-varying characteristics of gas well quantity, production time, effective reservoir thickness, controlled reserves, reserve abundance, formation pressure, and the energy storage coefficient, a data-driven method was used to establish a natural gas production prediction model based on differential simulation theory. The calculation results showed that the average error between the actual production and predicted production was 12.49%, and the model determination coefficient was 0.99, indicating that the model can effectively predict natural gas production. Additionally, we observed that the influence of factors such as reserve abundance, the number of wells in operation, controlled reserves, the previous year’s gas production, formation pressure, the energy storage coefficient, effective matrix thickness, and annual production time on the annual gas production increases progressively as the F-values decrease. These insights are pivotal to a more profound understanding of gas production dynamics in volcanic reservoirs and are instrumental in optimizing stimulation treatments and enhancing resource recovery in such reservoirs and other unconventional hydrocarbon formations. |
| format | Article |
| id | doaj-art-9fb335a2c4cb4f98a3a282ff5e9e31a1 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-9fb335a2c4cb4f98a3a282ff5e9e31a12024-11-08T14:35:46ZengMDPI AGEnergies1996-10732024-10-011721546110.3390/en17215461Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven MethodHaijie Zhang0Junwei Pu1Li Zhang2Hengjian Deng3Jihao Yu4Yingming Xie5Xiaochang Tong6Xiangjie Man7Zhonghua Liu8Chong Qing Shale Gas Exploration and Development, Co., Ltd., Chongqing 401121, ChinaChong Qing Shale Gas Exploration and Development, Co., Ltd., Chongqing 401121, ChinaChong Qing Shale Gas Exploration and Development, Co., Ltd., Chongqing 401121, ChinaChong Qing Shale Gas Exploration and Development, Co., Ltd., Chongqing 401121, ChinaSchool of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaCNOOC EnerTech-Drilling & Production, Co., Beijing 100028, ChinaPancasia Holding Co., Ltd., Chongqing 400000, ChinaPancasia Holding Co., Ltd., Chongqing 400000, ChinaSchool of Petroleum and Natural Gas Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaBased on on-site construction experience, considering the time-varying characteristics of gas well quantity, production time, effective reservoir thickness, controlled reserves, reserve abundance, formation pressure, and the energy storage coefficient, a data-driven method was used to establish a natural gas production prediction model based on differential simulation theory. The calculation results showed that the average error between the actual production and predicted production was 12.49%, and the model determination coefficient was 0.99, indicating that the model can effectively predict natural gas production. Additionally, we observed that the influence of factors such as reserve abundance, the number of wells in operation, controlled reserves, the previous year’s gas production, formation pressure, the energy storage coefficient, effective matrix thickness, and annual production time on the annual gas production increases progressively as the F-values decrease. These insights are pivotal to a more profound understanding of gas production dynamics in volcanic reservoirs and are instrumental in optimizing stimulation treatments and enhancing resource recovery in such reservoirs and other unconventional hydrocarbon formations.https://www.mdpi.com/1996-1073/17/21/5461production prediction modelvolcanic reservoirdata-driven methoddata nondimensionalizationdimension recovery |
| spellingShingle | Haijie Zhang Junwei Pu Li Zhang Hengjian Deng Jihao Yu Yingming Xie Xiaochang Tong Xiangjie Man Zhonghua Liu Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method Energies production prediction model volcanic reservoir data-driven method data nondimensionalization dimension recovery |
| title | Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method |
| title_full | Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method |
| title_fullStr | Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method |
| title_full_unstemmed | Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method |
| title_short | Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method |
| title_sort | gas production prediction model of volcanic reservoir based on data driven method |
| topic | production prediction model volcanic reservoir data-driven method data nondimensionalization dimension recovery |
| url | https://www.mdpi.com/1996-1073/17/21/5461 |
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