机器学习在CO2提高油气采收率与地质封存中的研究进展

doi:10.13809/j.cnki.cn32-1825/te.2025268

油气藏评价与开发

• • 下一篇

机器学习在CO₂提高油气采收率与地质封存中的研究进展

叶虹莹¹, 曹成^1,2,3, 赵玉龙¹, 张烈辉¹, 朱浩楠¹, 文绍牧⁴, 李清平², 张德平⁵, 赵松⁶, 曹正林⁷

1.西南石油大学油气藏地质及开发工程全国重点实验室,四川成都 610500;
2.怀柔实验室,北京 101400;
3.重庆大学产业技术研究院,重庆 401331;
4.中国石油西南油气田公司,四川成都 610055;
5.中国石油吉林油田分公司二氧化碳捕集埋存与提高采收率（CCS-EOR）开发公司,吉林松原 138000;
6.中国石油长庆油田,陕西西安 710018;
7.中国石油勘探开发研究院,北京 101400

收稿日期:2025-06-09
通讯作者: 曹成（1993—）,男,博士,副研究员,从事CO₂地质利用与封存方向研究。地址：四川省成都市新都区新都大道8号,邮政编码：610500。E-mail：caocheng@swpu.edu.cn
作者简介:叶虹莹（2003—）,女,在读硕士研究生,从事CO₂地质利用与封存方向研究。地址：四川省成都市新都区新都大道8号,邮政编码：610500。E-mail：2746693021@qq.com
基金资助:
国家自然科学基金项目“四川盆地有水气藏CO₂驱提高采收率及其有效封存研究”（U23A2022）; 四川省自然科学基金项目“深层海相碳酸盐岩气藏注CO₂提高采收率协同碳封存机理研究”（2025ZNSFSC1357）; 重庆市自然科学基金项目“多元杂质影响下CO₂-地层水-泥岩作用机理及盖层封闭性演化机制”（CSTB2024NSCQ-MSX0951）; 油气藏地质及开发工程全国重点实验室开放基金课题“咸水层含杂质CO₂-水-岩作用机制与封存潜力评价方法研究”（PLN2023-28）

Research progress of machine learning in CO₂ enhanced oil and gas recovery and geological storage

YE HONGYING¹, CAO CHENG^1,2,3, ZHAO YULONG¹, ZHANG LIEHUI¹, ZHU HAONAN¹, WEN SHAOMU⁴, LI QINGPING², ZHANG DEPING⁵, ZHAO SONG⁶, CAO ZHENGLIN⁷

1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500,China;
2. Huairou Laboratory, Beijing 101400,China;
3. Chongqing University Industrial Technology Research Institute, Chongqing 401331, China;
4. PetroChina Southwest Oil Gas Field Company, Chengdu, Sichuan 610055, China;
5. CCS-EOR Company, PetroChina Jilin Oil Field Company, Songyuan, Jilin 138000, China;
6. Changqing Oilfield, Xi'an, Shaanxi 710018,China;
7. Research Institute of Petroleum Exploration and Development; Beijing 101400,China

Received:2025-06-09

摘要/Abstract

摘要： 碳捕集、利用与封存（Carbon Capture, Utilization and Storage,简称CCUS）是实现碳中和的关键技术,通过CO₂提高油气采收率和地质封存实现能源增产与减少CO₂排放的双重效益。然而CCUS技术在大规模应用中面临工程设计和风险评估等技术挑战。传统解决方法依赖经验公式、实验验证和物理模型,在处理复杂系统时,计算效率低下,模型精度不足,难以处理多维度耦合问题。机器学习（Machine Learning,简称ML）凭借其强大的数据驱动分析能力和自适应优化特性,能训练出高精度预测模型,优化操作参数、预测储层流体行为、评估泄漏风险等,实现对复杂系统的实时监控和智能化决策,提升CCUS技术的安全性和经济性。研究系统梳理了ML在CO₂提高油气采收率与地质封存方面的应用。在CO₂提高油气采收率方面涵盖了渗流机理建模、优化井网设计、产量预测与评价、多目标优化、预测最小混相压力、预测气体吸附曲线、评估CO₂-CH₄扩散等;在CO₂地质封存方面包含储层优选、CO₂溶解与扩散机制研究、地质封存效果预测、风险评估等。ML在提升预测精度、优化操作参数、提高计算效率等方面展现出显著优势,已在储层优选、气体吸附预测、封存效果预测等关键领域取得重要进展,但在复杂地质场景下的适应性、模型普适性、动态数据处理能力、物理解释性等方面仍有待提升。

关键词: 机器学习, 智能算法, CCUS（碳捕集、利用与封存）, 提高油气采收率, 地质封存

Abstract: Carbon Capture, Utilization, and Storage (CCUS) is a key technology for achieving carbon neutrality. It enables the dual benefits of increasing energy production and reducing CO₂ emissions through CO₂ enhanced oil and gas recovery (EOR/EGR) and geological storage. However, the large-scale application of CCUS technology faces technical challenges such as engineering design and risk assessment. Traditional solutions rely on empirical formulas, experimental verification, and physical models. When dealing with complex systems, these methods suffer from low computational efficiency, insufficient model accuracy, and difficulties in addressing multi-dimensional coupling issues.Machine Learning (ML), with its powerful data-driven analytical capabilities and adaptive optimization features, can train high-precision prediction models. It can optimize operating parameters, predict reservoir fluid behavior, assess leakage risks, and more, thereby enabling real-time monitoring and intelligent decision-making for complex systems and enhancing the safety and economic efficiency of CCUS technology.This study systematically reviews the applications of ML in CO₂-enhanced oil and gas recovery and geological storage. In terms of CO₂-enhanced oil and gas recovery, the applications cover seepage mechanism modeling, well pattern design optimization, production prediction and evaluation, multi-objective optimization, minimum miscibility pressure prediction, gas adsorption curve prediction, and CO₂-CH₄ diffusion assessment. In the aspect of CO₂ geological storage, the applications include reservoir selection, research on CO₂dissolution and diffusion mechanisms, geological storage effect prediction, and risk assessment.Existing studies show that ML exhibits significant advantages in improving prediction accuracy, optimizing operating parameters, and enhancing computational efficiency. It has made important progress in key fields such as reservoir selection, gas adsorption prediction, and storage effect prediction. Nevertheless, improvements are still needed in terms of adaptability to complex geological scenarios, model universality, dynamic data processing capabilities, and physical interpretability.

Key words: machine learning, intelligent algorithm, CCUS (carbon capture, utilization and storage), enhanced oil and gas recovery, geological storage

中图分类号:

TE357

叶虹莹,曹成,赵玉龙, 等. 机器学习在CO₂提高油气采收率与地质封存中的研究进展[J]. 油气藏评价与开发, 0, (): 0-.

YE HONGYING,CAO CHENG,ZHAO YULONG, et al. Research progress of machine learning in CO₂ enhanced oil and gas recovery and geological storage[J]. Petroleum Reservoir Evaluation and Development, 0, (): 0-.

参考文献

[1] 王国锋, 吕伟峰, 崔凯, 等. 全球二氧化碳捕集、利用与封存产业集群技术进展及应用展望[J]. 石油勘探与开发, 2025, 52(2): 478-487.
WANG Guofeng, LYU Weifeng, CUI Kai,et al.Technical progress and application of global carbon dioxide capture,utilization and storage cluster[J]. Petroleum Exploration and Development, 2025, 52(2): 478-487.
[2] BROWN K, WHITTAKER S, WILSON M, et al.The history and development of the IEA GHG Weyburn-Midale CO₂ Monitoring and Storage Project in Saskatchewan, Canada (the world largest CO₂ for EOR and CCS program)[J]. Petroleum, 2017, 3(1): 3-9.
[3] 李倩文, 马晓潇, 高波, 等. 美国重点页岩油区勘探开发进展及启示[J]. 新疆石油地质, 2021, 42(5): 630-640.
LI Qianwen, MA Xiaoxiao, GAO Bo, et al.Progress and enlightenment of exploration and development of major shale oil zones in the USA[J]. Xinjiang Petroleum Geology, 2021, 42(5): 630-640.
[4] 窦立荣, 孙龙德, 吕伟峰, 等. 全球二氧化碳捕集、利用与封存产业发展趋势及中国面临的挑战与对策[J]. 石油勘探与开发, 2023, 50(5): 1083-1096.
DOU Lirong, SUN Longde, LYU Weifeng,et al.Trend of global carbon dioxide capture,utilization and storage industry and challenges and countermeasures in China[J]. Petroleum Exploration and Development, 2023, 50(5): 1083-1096.
[5] 舒华文. 胜利油田百万吨级 CCUS 输注采关键工程技术[J]. 油气藏评价与开发, 2024, 14(1): 10-17.
SHU Huawen.Key engineering technologies of one-million-ton CCUS transportation-injection-extraction in Shengli Oilfield[J]. Petroleum Reservoir Evaluation and Development, 2024, 14(1): 10-17.
[6] 王峰, 黎政权, 张德平. 吉林油田CCUS-EOR技术攻关与实践新进展[J]. 天然气工业, 2024, 44(4): 76-82.
WANG Feng, LI Zhengquan, ZHANG Deping.New research and practice progresses of CCUS-EOR technology in Jilin Oilfield[J]. Natural Gas Industry, 2024, 44(4): 76-82.
[7] 张烈辉, 曹成, 文绍牧, 等. 碳达峰碳中和背景下发展CO₂-EGR的思考[J]. 天然气工业, 2023, 43(1): 13-22.
ZHANG Liehui, CAO Cheng, WEN Shaomu, et al.Thoughts on the development of CO₂-EGR under the background of carbon peak and carbon neutrality[J]. Natural Gas Industry, 2023, 43(1): 13-22.
[8] 肖江, 宋世杰, 刘兰兰, 等. 二氧化碳捕集及封存技术探索研究: 以陕煤集团榆林化学公司为例[J]. 煤炭科学技术, 2024, 52(5): 316-323.
XIAO Jiang, SONG Shijie, LIU Lanlan, et al.Research on carbon dioxide capture and storage technology: A case study of Shaanxi Coal Group Yulin Chemical Co., Ltd[J]. Coal Science and Technology, 2024, 52(5): 316-323.
[9] 赵云飞, 孙洪国, 张雪玲, 等. 基于特征参数的聚合物驱开发指标组合预测方法[J].大庆石油地质与开发, 2023, 42(1): 57-63.
ZHAO Yunfei, SUN Hongguo, ZHANG Xueling, et al.Combination prediction method of polymer flooding development index based on characteristics parameters[J]. Petroleum Geology Oilfield Development in Daqing, 2023, 42(1): 57-63.
[10] PINGS C J Jr, TEMPELAAR-LIETZ W. Canal field gas injection project[J]. Journal of Petroleum Technology, 1955, 7(8): 25-31.
[11] 成金华. 能源转型中的天然气角色与能源安全: 评《中国天然气全产业链市场安全问题研究》[J]. 华中师范大学学报(自然科学版), 2024, 58(6): 731-732.
CHENG Jinhua.The role of natural gas in energy transformation and energy security: Comment on “research on market security of China natural gas industry chain”[J]. Journal of Central China Normal University (Natural Sciences), 2024, 58(6): 731-732.
[12] 杨勇. 中国碳捕集、驱油与封存技术进展及发展方向[J]. 石油学报, 2024, 45(1): 325-338.
YANG Yong.Technology progress and development direction of carbon capture, oil-flooding and storage in China[J]. Acta Petrolei Sinica, 2024, 45(1): 325-338.
[13] 张凯, 陈掌星, 兰海帆, 等. 碳捕集、利用与封存技术的现状及前景[J]. 特种油气藏, 2023, 30(2): 1-9.
ZHANG Kai, CHEN Zhangxing, LAN Haifan, et al.Status and prospects of carbon capture, utilization and storage technology[J]. Special Oil Gas Reservoirs, 2023, 30(2): 1-9.
[14] SINHA S, DE LIMA R P, LIN Y, et al. Normal or abnormal? Machine learning for the leakage detection in carbon sequestration projects using pressure field data[J]. International Journal of Greenhouse Gas Control, 2020, 103: 103189.
[15] 芮振华, 邓海洋, 胡婷. 基于混合物理数据驱动的油藏地质体CO₂利用与封存代理模型研究[J]. 钻采工艺, 2025, 48(1): 190-198.
RUI Zhenhua, DENG Haiyang, HU Ting.Study on CO₂ Utilization and storage proxy model for reservoir geobodies based on hybrid physics driven-data[J]. Drilling Production Technology, 2025, 48(1): 190-198.
[16] HUSSIN F, MD RAHIM S A N, HATTA N S M, et al. A systematic review of machine learning approaches in carbon capture applications[J]. Journal of CO₂ Utilization, 2023, 71: 102474.
[17] METZ B, DAVIDSON O, DE CONINCK H, et al.IPCC special report on carbon dioxide capture and storage[R]. Intergovernmental Panel on Climate Change, Geneva (Switzerland). Working Group III, 2005.
[18] 王光付, 李阳, 王锐, 等. 二氧化碳地质封存与利用新进展[J]. 石油与天然气地质, 2024, 45(4): 1168-1179.
WANG Guangfu, LI Yang, WANG Rui, et al.Recent advances in geological carbon dioxide storage and utilization[J] Oil Gas Geology, 2024, 45(4): 1168-1179.
[19] 张代俐, 汪廷华, 朱兴淋. SVM样本约简算法研究综述[J]. 计算机科学, 2024, 51(7): 59-70.
ZHANG Daili, WANG Tinghua, ZHU Xinglin.Overview of sample reduction algorithms for support vector machine[J]. Computer Science, 2024, 51(7): 59-70.
[20] 牟丹, 张丽春, 徐长玲. 3种经典机器学习算法在火山岩测井岩性识别中的对比[J]. 吉林大学学报(地球科学版), 2021, 51(3): 951-956.
MOU Dan, ZHANG Lichun, XU Changling.Comparison of three classical machine learning algorithms for lithology identification of volcanic rocks using well logging data[J]. Journal of Jilin University (Earth Science Edition), 2021, 51(3): 951-956.
[21] 高淑芝, 赵匀琨, 张义民. 改进粒子群算法在可靠性分析中的应用[J]. 机械设计与制造, 2024, (12): 117-120.
GAO Shuzhi, ZHAO Yunkun, ZHANG Yimin.Application of improved particle swarm optimization algorithm in reliability analysis[J]. Machinery Design Manufacture, 2024, (12): 117-120.
[22] 任俊帆, 薛亮, 聂捷, 等. 基于随机森林算法的二氧化碳驱油与封存主控因素研究[J]. 地质科技通报, 2024, 43(3): 147-156.
REN Junfan, XUE Liang, NIE Jie, et al.Research on the main control factors of carbon dioxide flooding and storage based on random forest algorithm[J]. Bulletin of Geological Science and Technology, 2024, 43(3): 147-156.
[23] 赵昕海, 张术臣, 李志深, 等. 基于VMD的故障特征信号提取方法[J]. 振动.测试与诊断, 2018, 38(1): 11-19.
ZHAO Xinhai, ZHANG Shuchen, LI Zhishen, et al.Application of new denoising method based on VMD in fault feature extraction[J]. Journal of Vibration, Measurement Diagnosis 2018, 38(1): 11-19.
[24] 马航, 董卫宇, 唐之卓, 等. 遗传算法在模糊测试中的应用研究综述[J]. 小型微型计算机系统, 2025, 46(4): 948-957.
MA Hang, DONG Weiyu, TANG Zhizhuo, et al.Review of the application of genetic algorithm in fuzzing[J]. Journal of Chinese Computer Systems, 2025, 46(4): 948-957.
[25] 张驰, 郭媛, 黎明. 人工神经网络模型发展及应用综述[J]. 计算机工程与应用, 2021, 57(11): 57-69.
ZHANG Chi, GUO Yuan, LI Ming.Review of development and application of artificial neural network models[J]. Computer Engineering and Applications, 2021, 57(11): 57-69.
[26] 蒋熙梅, 闫伟超, 邢会林, 等. 基于自动超参数优化框架与梯度提升算法融合的储层粒度二维分布预测研究[J]. 地球物理学进展, 2024, 39(5): 1886-1900.
JIANG Ximei, YAN Weichao, XING Huilin, et al.Research on two-dimensional reservoir grain size distribution prediction based on the fusion of automatic hyperparameter optimization framework and gradient boosting algorithm[J]. Progress in Geophysics, 2024, 39(5): 1886-1900.
[27] 丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报, 2011, 40(1): 2-10.
DING Shifei, QI Bingjuan, TAN Hongyan.An overview on theory and algorithm of support vector machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10.
[28] 杨维,李歧强. 粒子群优化算法综述[J]. 中国工程科学, 2004, 6(5): 87-94.
YANG Wei, LI Qiqiang.Survey on particle swarm optimization algorithm[J]. Strategic Study of CAE, 2004, 6(5): 87-94.
[29] 李欣海. 随机森林模型在分类与回归分析中的应用[J]. 应用昆虫学报, 2013, 50(4): 1190-1197.
LI Xinhai.Using “random forest” for classification and regression[J]. Chinese Journal of Applied Entomology, 2013, 50(4): 1190-1197.
[30] 杨晶显, 张帅, 刘继春, 等. 基于VMD和双重注意力机制LSTM的短期光伏功率预测[J]. 电力系统自动化, 2021, 45(3): 174-182.
YANG Jingxian, ZHANG Shuai, LIU Jichun, et al.Short-term photovoltaic power prediction based on variational mode decomposition and long shortterm memory with dual-stage attention mechanism[J]. Automation of Electric Power Systems, 2021, 45(3): 174-182.
[31] 马永杰, 云文霞. 遗传算法研究进展[J]. 计算机应用研究, 2012, 29(4): 1201-1206.
MA Yongjie, YUN Wenxia.Research progress of genetic algorithm[J]. Application Research of Computers, 2012, 29(4): 1201-1206.
[32] 李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016, 36(9): 2508-2515.
LI Yandong, HAO Zongbo, LEI Hang.Survey of convolutional neural network[J]. Journal of Computer Applications, 2016, 36(9): 2508-2515.
[33] 李占山, 刘兆赓. 基于XGBoost的特征选择算法[J]. 通信学报, 2019, 40(10): 101-108.
LI Zhanshan, LIU Zhaogeng.Feature selection algorithm based on XGBoost[J]. Journal on Communications, 2019, 40(10): 101-108.
[34] HUANG Y F, HUANG G H, DONG M Z, et al.Development of an artificial neural network model for predicting minimum miscibility pressure in CO₂ flooding[J]. Journal of Petroleum Science and Engineering, 2003, 37(1/2): 83-95.
[35] AHMADI M A, ZAHEDZADEH M, SHADIZADEH S R, et al.Connectionist model for predicting minimum gas miscibility pressure: Application to gas injection process[J]. Fuel, 2015, 148: 202-211.
[36] SAYYAD H, MANSHAD A K, ROSTAMI H.Application of hybrid neural particle swarm optimization algorithm for prediction of MMP[J]. Fuel, 2014, 116: 625-633.
[37] CHEN G, FU K, LIANG Z, et al.The genetic algorithm based back propagation neural network for MMP prediction in CO₂-EOR process[J]. Fuel, 2014, 126: 202-212.
[38] BIAN X Q, HAN B, DU Z M, et al.Integrating support vector regression with genetic algorithm for CO₂-oil minimum miscibility pressure (MMP) in pure and impure CO₂ streams[J]. Fuel, 2016, 182: 550-557.
[39] KOUHI M M, KAHZADVAND K, SHAHIN M, et al.New connectionist tools for prediction of CO₂ diffusion coefficient in brine at high pressure and temperature─implications for CO₂ sequestration in deep saline aquifers[J]. Fuel, 2025, 384: 134000.
[40] NI H, BENSON S M. Using unsupervised machine learning to characterize capillary flow and residual trapping[J]. Water Resources Research, 2020, 56(8): e2020WR027473.
[41] AMAR M N, JAHANBANI GHAHFAROKHI A.Prediction of CO₂ diffusivity in brine using white-box machine learning[J]. Journal of Petroleum Science and Engineering, 2020, 190: 107037.
[42] SHARMA L K, VISHAL V, SINGH T N.Predicting CO₂ permeability of bituminous coal using statistical and adaptive neuro-fuzzy analysis[J]. Journal of Natural Gas Science and Engineering, 2017, 42: 216-225.
[43] AHMADI M A, AHMADI A.Applying a sophisticated approach to predict CO₂ solubility in brines: Application to CO₂ sequestration[J]. International Journal of Low-Carbon Technologies, 2016, 11(3): 325-332.
[44] FENG Q, CUI R, WANG S, et al.Estimation of CO₂ diffusivity in brine by use of the genetic algorithm and mixed kernels-based support vector machine model[J]. Journal of Energy Resources Technology-Transactions of the ASME, 2019, 141(4): 041001.
[45] MENAD N A, HEMMATI-SARAPARDEH A, VARAMESH A, et al.Predicting solubility of CO₂ in brine by advanced machine learning systems: Application to carbon capture and sequestration[J]. Journal of CO₂ Utilization, 2019, 33: 83-95.
[46] 魏兵, 陈海龙, 刘帅, 等. 页岩储层CO₂吸附解吸行为及弥散特征[J]. 天然气工业, 2024, 44(12): 176-186.
WEI Bing, Chen Hailong, Liu Shuai, et al.CO₂ adsorption/desorption behaviors and dispersion characteristics in shale reservoirs[J]. Natural Gas Industry, 2024, 44(12): 176-186.
[47] FENG Q, WANG J, ZHANG J, et al.Data-driven modeling of the methane adsorption isotherm on coal using supervised learning methods: A comparative study[J]. Journal of Physics: Conference Series, 2021, 1813(1): 012023.
[48] YAN H, ZHANG J, ZHOU N, et al.Application of hybrid artificial intelligence model to predict coal strength alteration during CO₂ geological sequestration in coal seams[J]. Science of the Total Environment, 2020, 711: 135029.
[49] 范黎利. 基于机器学习的页岩对CH₄与CO₂过剩吸附量预测方法的研究[D]. 长沙: 湖南大学, 2021.
FAN Lili.Research on prediction method of excess adsorption amount of methane and carbon dioxide on shale based on machine learning[D]. Changsha: Hunan University, 2021.
[50] AMAR M N, LARESTANI A, LV Q, et al.Modeling of methane adsorption capacity in shale gas formatins using white-box supervised machine learning techniques[J]. Journal of Petroleum Science and Engineering, 2022, 208: 109226.
[51] 张凯, 吴海洋, 徐耀东, 等. 考虑地质及开发因素约束的三角形井网优化[J]. 中国石油大学学报(自然科学版), 2015, 39(4): 111-118.
ZHANG Kai, WU Haiyang, XU Yaodong, et al.Triangulated well pattern optimization constrained by geological and production factors[J]. Journal of China University of Petroleum(Edition of Natural Science), 2015, 39(4): 111-118.
[52] 范利军, 杨建平, 赵东亚, 等. 基于改进粒子群算法的CO₂驱井网优化设计[J]. 石油工程建设, 2016, 42(5): 6-9.
FAN Lijun, YANG Jianping, ZHAO Dongya, et al.Optimum design of CO₂ flooding well based on improved particle swarm algorithm[J]. Petroleum Engineering Construction, 2016, 42(5): 6-9.
[53] 刘巍, 刘威, 谷建伟, 等. 利用卡尔曼滤波和人工神经网络相结合的油藏井间连通性研究[J]. 油气地质与采收率, 2020, 27(2): 118-124.
LIU Wei, LIU Wei, GU Jianwei, et al.Research on interwell connectivity of oil reservoirs based on Kalman filter and artificial neural network[J]. Petroleum Geology and Recovery Efficiency, 2020, 27(2): 118-124.
[54] VIDA G, SHAHAB M D, MOHAMMAD M.Smart proxy modeling of SACROC CO₂-EOR[J]. Fluids, 2019, 4(2): 85.
[55] AMINI S, MOHAGHEGH S.Application of machine learning and artificial intelligence in proxy modeling for fluid flow in porous media[J]. Fluids, 2019, 4(3): 126.
[56] CHEN B, PAWAR R J.Capacity assessment and co-optimization of CO₂ storage and enhanced oil recovery in residual oil zones[J]. Journal of Petroleum Science and Engineering, 2019, 182: 106342.
[57] ZHANG N, WEI M, FAN J, et al.Development of a hybrid scoring system for EOR screening by combining conventional screening guidelines and random forest algorithm[J]. Fuel, 2019, 256: 115915.
[58] MENAD N A, NOUREDDINE Z.An efficient methodology for multi-objective optimization of water alternating CO₂ EOR process[J]. Journal of the Taiwan Institute of Chemical Engineers, 2019, 99: 154-165.
[59] BOCOUM A O, RASAEI M R.Multi-objective optimization of WAG injection using machine learning and data-driven Proxy models[J]. Applied Energy, 2023, 349: 121593.
[60] CHEN G, TIAN H, YUAN Y, 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: 213380.
[61] SONG Y, SUNG W, JANG Y, et al.Application of an artificial neural network in predicting the effectiveness of trapping mechanisms on CO₂ sequestration in saline aquifers[J]. International Journal of Greenhouse Gas Control, 2020, 98: 103042.
[62] SUN A Y.Optimal carbon storage reservoir management through deep reinforcement learning[J]. Applied Energy, 2020, 278: 115660.
[63] XUE H, WANG G, LI X, et al.Predictive combination model for CH4 separation and CO₂ sequestration with CO₂ injection into coal seams: VMD-STA-BiLSTM-ELM hybrid neural network modeling[J]. Energy, 2024, 313: 133744.
[64] 贺荣权. 基于机器学习的二氧化碳驱油及碳封存项目利润优化研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.
HE Rongquan.Modeling and optimizing for operation of CO₂-EOR project based on machine learning methods[D]. Harbin: Harbin Institute of Technology, 2021.
[65] SHEN B, YANG S, GAO X, et al.A novel CO₂-EOR potential evaluation method based on BO-LightGBM algorithms using hybrid feature mining[J]. Geoenergy Science and Engineering, 2023, 222: 211427.
[66] SHEN B, YANG S, HU J, et al.Interpretable causal-based temporal graph convolutional network framework in complex spatio-temporal systems for CCUS-EOR[J]. Energy, 2024, 309: 133129.
[67] IBRAHIM A F, AL-DHAIF R, ELKATATNY S, et al.Applications of artificial intelligence to predict oil rate for high gas-oil ratio and water-cut wells[J]. ACS Omega, 2021, 6(30): 19484-19493.
[68] BEMANI A, BAGHBAN A, MOSAVI A, et al.Estimating CO₂-Brine diffusivity using hybrid models of ANFIS and evolutionary algorithms[J]. Engineering Applications of Computational Fluid Mechanics, 2020, 14(1): 818-834.
[69] 徐路路. 二氧化碳地质封存中储层物性特征演化及注采优化研究[D]. 长春: 吉林大学, 2024.
XU Lulu.Study on evolution of reservoir physical characteristics and injection-production optimization in carbon dioxide geological storage[D]. Changchun: Jilin University, 2024.
[70] 涂永易. CO₂水气交替注入对低渗油藏采收率的影响规律研究[D]. 大庆: 东北石油大学, 2023.
TU Yongyi.Study on the influence law of CO₂ water alternating gas on the recovery efficiency of low permeability reservoir[D].Daqing: Northeast Petroleum University, 2023.
[71] 苏玉亮, 蔡明玉, 孟凡坤, 等. 低渗透油藏CO₂驱试井解释方法[J]. 油气地质与采收率, 2020, 27(1): 113-119.
SU Yuliang, CAI Mingyu, MENG Fankun, et al.Well testing interpretation method for CO₂ flooding in low permeability oil reservoirs[J]. Petroleum Geology and Recovery Efficiency, 2020, 27(1): 113-119.
[72] SUN Q, AMPOMAH W, KUTSIENYO E J, et al.Assessment of CO₂ trapping mechanisms in partially depleted oil-bearing sands[J]. Fuel, 2020, 278: 118356.
[73] BHATTACHERJEE R, BOTCHWAY K, PASHIN J C, et al.Machine learning-based prediction of CO₂ fugacity coefficients: Application to estimation of CO₂ solubility in aqueous brines as a function of pressure, temperature, and salinity[J]. International Journal of Greenhouse Gas Control, 2023, 128: 103971.
[74] AMPOMAH W, BALCH R S, GRIGG R B, et al.Co‐optimization of CO₂‐EOR and storage processes in mature oil reservoirs[J]. Greenhouse Gases: Science Technology, 2017, 7(1): 128-142.
[75] 滕世婷. 特低渗透滩坝砂油藏CO₂驱层系井网适配性研究[D]. 东营: 中国石油大学(华东), 2021.
TENG Shiting.Adaptation of layers and well patterns of CO₂ drive in ultra-low permeability beach-bar sand reservoir[D]. Dongying: China University of Petroleum (East China), 2021.
[76] 白宏山. CO₂捕集、运输、驱油与封存全流程随机优化算法研究[D]. 东营: 中国石油大学(华东), 2020.
BAI Hongshan.Research on stochastic optimization algorithms of whole process for CO₂ capture, transportation, utilization and storage[D]. Dongying: China University of Petroleum (East China) , 2020.
[77] ANYEBE A P, YEBOAH O K K, BAKINSON O I, et al. Optimizing carbon capture efficiency through AI-driven process automation for enhancing predictive maintenance and CO₂ sequestration in oil and gas facilities[J]. American Journal of Environment and Climate, 2024, 3(3): 44-58.
[78] 李承龙. 一种二氧化碳驱油开发效果综合评价新方法[J]. 中外能源, 2022, 27(4): 45-50.
LI Chenglong.New comprehensive evaluation method for development effect of CO₂ flooding[J]. Sino-Global Energy, 2022, 27(4): 45-50.
[79] NASSABEH M, YOU Z, KESHAVARZ A, et al.Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection[J]. Energy, 2024, 305: 132086.
[80] NARAYAN S, KUMAR V, MUKHERJEE B, et al.Machine learning assisted reservoir characterization for CO₂ sequestration: A case study from the Penobscot field, Canada offshore[J]. Marine and Petroleum Geology, 2024, 169: 107054.
[81] 张延旭, 姜晶, 王涛, 等. 神经网络法在油藏埋存CO₂效果预测中的应用[J]. 精细石油化工进展, 2018, 19(2): 29-32.
ZHANG Yanxu, JIANG Jing, WANG Tao, et al.Application of artificial neural network in forecast of carbon dioxide storage in reservoir[J]. Advances in Fine Petrochemicals, 2018, 19(2): 29-32.
[82] SAFAEI-FAROUJI M, VO THANH H, DAI Z, et al.Exploring the power of machine learning to predict carbon dioxide trapping efficiency in saline aquifers for carbon geological storage project[J]. Journal of Cleaner Production, 2022, 372: 133778.
[83] XING X, BIAN X Q, ZHANG J, et al.A data-driven dual-optimization hybrid machine learning model for predicting carbon dioxide trapping efficiency in saline aquifers: Application in carbon capture and storage[J]. Geoenergy Science and Engineering, 2024, 243: 213363.
[84] ZHONG Z, SUN A Y, YANG Q, et al.A deep learning approach to anomaly detection in geological carbon sequestration sites using pressure measurements[J]. Journal of Hydrology, 2019, 573: 885-894.
[85] CHEN B, HARP D R, LIN Y, et al.Geologic CO₂ sequestration monitoring design: A machine learning and uncertainty quantification based approach[J]. Applied Energy, 2018, 225: 332-345.
[86] ZHOU Z, LIN Y, ZHANG Z, et al.A data-driven CO₂ leakage detection using seismic data and spatial-temporal densely connected convolutional neural networks[J]. International Journal of Greenhouse Gas Control, 2019, 90: 102790.
[87] MORALES M M, MEHANA M, TORRES-VERDÍN C, et al. Optimal monitoring design for uncertainty quantification during geologic CO₂ sequestration: A machine learning approach[J]. Geoenergy Science and Engineering, 2025, 244: 213402.
[88] PRAKS P, RASMUSSEN A, LYE K O, et al.Sensitivity analysis of parameters for carbon sequestration: Symbolic regression models based on open porous media reservoir simulators predictions[J]. Heliyon, 2024, 10(22): e40044.
[89] YOU J, AMPOMAH W, SUN Q, et al.Machine learning based co-optimization of carbon dioxide sequestration and oil recovery in CO₂-EOR project[J]. Journal of Cleaner Production, 2020, 260: 120866.
[90] 范利军, 赵东亚, 刘佳佳, 等. CO₂井网驱油封存联合优化设计与敏感性分析[J]. 石油工程建设, 2017, 43(4): 7-12.
FAN Lijun, ZHAO Dongya, LIU Jiajia, et al.Joint optimization design and sensitivity analysis of CO₂ well pattern EOR and storage[J]. Petroleum Engineering Construction, 2017, 43(4): 7-12.
[91] ABDULKHALEQ H B, IBRAHEEM I K, AL-MUDHAFAR W J, et al. Harnessing the power of machine learning for the optimization of CO₂ sequestration in saline aquifers: Applied on the tensleep formation at teapot dome in Wyoming[J]. Geoenergy Science and Engineering, 2025, 245: 213522.
[92] KARACAN C Ö.A fuzzy logic approach for estimating recovery factors of miscible CO₂-EOR projects in the United States[J]. Journal of Petroleum Science and Engineering, 2020, 184: 106533.
[93] LI Q, HAO Y R, WANG P T, et al.Research on safety evaluation of CO₂ enhanced oil and gas recovery (CO₂-EOR) project: A case study of dagang oilfield[J]. Processes, 2025, 13(1): 28.
[94] 姚健, 郭红强, 张金元, 等. 延长油田低渗油藏CO₂封存适宜度评价方法研究及试验[J]. 河南科技, 2024, 51(13): 40-45.
YAO Jian, GUO Hongqiang, ZHANG Jinyuan, et al.Study and experiment on evaluation method of CO₂ Storage suitability for low permeability reservoir in Yanchang Oilfield[J]. Henan Science and Technology, 2024, 51(13): 40-45.

机器学习在CO₂提高油气采收率与地质封存中的研究进展

Research progress of machine learning in CO₂ enhanced oil and gas recovery and geological storage

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

Metrics

本文评价

推荐阅读 0

[1]	孙秋分, 秦佳正, 冯乔, 乔宇, 刘雅昕, 赵启阳, 徐良, 闫春. 基于机器学习的裂缝水驱气藏采收率预测方法 [J]. 油气藏评价与开发, 2025, 15(5): 834-843.
[2]	杨术刚, 任金蔓, 蔡明玉, 刘浩童, 刘双星, 薛明, 张坤峰. 气田采出水回注地层CO₂封存赋存状态研究 [J]. 油气藏评价与开发, 2025, 15(4): 656-663.
[3]	孙东升, 张顺康, 王智林, 葛政俊, 林波. 苏北断块型圈闭基于安全性CO₂地质封存能力计算方法研究 [J]. 油气藏评价与开发, 2025, 15(4): 641-645.
[4]	张章, 孟鹏, 杨威, 张小龙, 黄奇, 王浩然. 基于地震属性堆叠泛化集成学习的辫状河储层构型表征——以渤海湾盆地C-2油田为例 [J]. 油气藏评价与开发, 2025, 15(1): 64-72.
[5]	陈宏举, 刘强, 孙丽丽, 于航. 海上油气低碳发展现状与展望 [J]. 油气藏评价与开发, 2024, 14(6): 981-989.
[6]	聂云丽, 高国忠. 基于随机森林的页岩气“甜点”分类方法 [J]. 油气藏评价与开发, 2023, 13(3): 358-367.
[7]	李士伦,汤勇,段胜才,秦佳正,陈一诺,刘雅昕,郑鹏,赵国庆. CO₂地质封存源汇匹配及安全性评价进展 [J]. 油气藏评价与开发, 2023, 13(3): 269-297.
[8]	张庆,何封,何佑伟. 基于机器学习的页岩气井井间干扰评价及预测 [J]. 油气藏评价与开发, 2022, 12(3): 487-495.
[9]	黄家宸,张金川. 机器学习预测油气产量现状 [J]. 油气藏评价与开发, 2021, 11(4): 613-620.
[10]	李昌,沈安江,常少英,梁正中,李振林,孟贺. 机器学习法在碳酸盐岩岩相测井识别中应用及对比——以四川盆地MX地区龙王庙组地层为例 [J]. 油气藏评价与开发, 2021, 11(4): 586-596.
[11]	张金川,陈世敬,李中明,郎岳,王春艳,王东升,李振,唐玄,刘飏,李沛,仝忠正. 页岩气资源智能评价 [J]. 油气藏评价与开发, 2021, 11(4): 476-486.