Optimization of co-injecting CH4 with CO2 to enhanced oil recovery and carbon storage: A machine-learning based case study on H59 block of Jilin Oilfield, China
文献类型：期刊论文
| 作者 | Chen, Guangxu4,5; Tian, Hailong4,5; Yuan, Yilong4,5; Xiao, Ting1,3; Lei, Hongwu2; Yang, Shuo4,5 |
| 刊名 | GEOENERGY SCIENCE AND ENGINEERING
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 243页码:14 |
| 关键词 | CO2-EOR CH4 re-injection Machine learning Optimization Reservoir simulation Carbon storage |
| ISSN号 | 2949-8929 |
| DOI | 10.1016/j.geoen.2024.213380 |
| 英文摘要 | During CO2-EOR implementation, CH4 in the reproduced CO2-rich mixture are included in the recycle injection gas. However, improper injection gas composition and operation parameters can reduce the flooding performance. In this study, a machine-learning assisted framework combining Convolutional Neural Networks-Gate Recurrent Unit (CNN-GRU) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) was proposed to obtain the optimal parameters for simultaneous maximum oil production, carbon storage efficiency, and net present value. A case study from the CO2-EOR project in the H59 block of Jilin oilfield, China, was carried out. Base cases were established to evaluate the effects of reinjection CH4 on flooding performance. Results show that the built CNN-GRU model has high prediction accuracy and computational efficiency, and thus can be used as an alternative tool to the reservoir simulator. The proposed framework can find the optimum parameters to improve oil recovery, carbon storage and net present value. The co-injection of CH4 and CO2 improves oil production, and carbon storage performance, but reduces the net present value. This work provides engineers with multiple strategies for decision-making to simultaneously promote flooding performance with CH4 being a co-injectant in CO2-EOR projects. |
| 资助项目 | National Natural Science Foundation of China[42141013] ; CNPC Innovation Found[2021 D002-1102] |
| WOS研究方向 | Energy & Fuels ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001332772700001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42811]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Tian, Hailong; Yuan, Yilong |
| 作者单位 | 1.Univ Utah, Dept Civil & Environm Engn, Salt Lake City, UT 84112 USA 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.Univ Utah, Energy & Geosci Inst, Salt Lake City, UT 84108 USA 4.Jilin Univ, Key Lab Groundwater Resources & Environm, Minist Educ, Changchun 130021, Peoples R China 5.Jilin Univ, Jilin Prov Key Lab Water Resources & Environm, Changchun 130021, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Guangxu,Tian, Hailong,Yuan, Yilong,et al. Optimization of co-injecting CH4 with CO2 to enhanced oil recovery and carbon storage: A machine-learning based case study on H59 block of Jilin Oilfield, China[J]. GEOENERGY SCIENCE AND ENGINEERING,2024,243:14. |
| APA | Chen, Guangxu,Tian, Hailong,Yuan, Yilong,Xiao, Ting,Lei, Hongwu,&Yang, Shuo.(2024).Optimization of co-injecting CH4 with CO2 to enhanced oil recovery and carbon storage: A machine-learning based case study on H59 block of Jilin Oilfield, China.GEOENERGY SCIENCE AND ENGINEERING,243,14. |
| MLA | Chen, Guangxu,et al."Optimization of co-injecting CH4 with CO2 to enhanced oil recovery and carbon storage: A machine-learning based case study on H59 block of Jilin Oilfield, China".GEOENERGY SCIENCE AND ENGINEERING 243(2024):14. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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