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

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 ,
  • 曹成 ,
  • 赵玉龙 ,
  • 张烈辉 ,
  • 朱浩楠 ,
  • 文绍牧 ,
  • 李清平 ,
  • 张德平 ,
  • 赵松 ,
  • 曹正林
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  • 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
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
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

收稿日期: 2025-06-09

  网络出版日期: 2025-10-11

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）

收起

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

  • YE HONGYING ,
  • CAO CHENG ,
  • ZHAO YULONG ,
  • ZHANG LIEHUI ,
  • ZHU HAONAN ,
  • WEN SHAOMU ,
  • LI QINGPING ,
  • ZHANG DEPING ,
  • ZHAO SONG ,
  • CAO ZHENGLIN
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  • 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 date: 2025-06-09

  Online published: 2025-10-11

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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

本文引用格式

叶虹莹 , 曹成 , 赵玉龙 , 张烈辉 , 朱浩楠 , 文绍牧 , 李清平 , 张德平 , 赵松 , 曹正林 . 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 . DOI: 10.13809/j.cnki.cn32-1825/te.2025268

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 CO2 dissolution 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

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