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火山岩气藏注CO2提高采收率与地质封存协同优化算法

Co-optimization algorithm of CO2-enhanced gas recovery and geological storage in volcanic gas reservoirs

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【作者】 甯波李俊键郭建林温成粤钟太贤袁贺孟凡坤

【Author】 NING Bo;LI Junjian;GUO Jianlin;WEN Chengyue;ZHONG Taixian;YUAN He;MENG Fankun;College of Petroleum Engineering, China University of Petroleum;Research Institute of Petroleum Exploration and Development,PetroChina;School of Petroleum Engineering, Yangtze University;China National Petroleum Corporation;

【通讯作者】 孟凡坤;

【机构】 中国石油大学(北京)石油工程学院中国石油勘探开发研究院长江大学石油工程学院中国石油天然气集团有限公司

【摘要】 为实现边底水气藏CO2注入气藏采收率最大、CO2封存量最高的目的,以国内某典型火山岩气藏为例,综合采用拉丁超立方采样(LHS)、长短期记忆神经网络(LSTM)与多目标粒子群(MOPSO)优化算法等,建立了基于代理模型的气藏注CO2提高采收率与地质封存多目标优化方法,实现了CO2注入及生产井制度的优化。在该方法中,首先采用LHS方法及油气藏数值模拟器,在给定的注采制度范围内生成1 000组训练与测试样本;然后,采用LSTM进行训练,构建CO2注入生产动态预测代理模型,提高计算效率;最后,利用MOPSO算法获取最优结果构成的帕累托前沿,根据实际需求,求取最优方案下注采制度。研究结果表明,构建的代理模型对气藏采收率、CO2封存率的预测准确度大于0.98,显示其预测准确度较高;对比优化前注采方案,优化后方案气藏采收率、CO2封存率分别提升了9.03、5.53个百分点,证实了方法的可靠性,对类似气藏开发后期注CO2提采-封存方案的编制具有借鉴意义。

【Abstract】 To maximize gas recovery and CO2 storage through CO2 injection in a gas reservoir with edge and bottom water, a typical volcanic gas reservoir in China was taken as an example. By integrating latin hypercube sampling(LHS), long short-term memory neural network(LSTM), and the multi-objective particle swarm optimization(MOPSO) algorithm, a surrogate model-based multi-objective optimization method was established to enhance gas recovery and geological CO2 storage in the gas reservoirs. This method enabled the optimization of CO2 injection and production well strategies. First, LHS and reservoir numerical simulation were used to generate 1 000 training and testing samples within a specified range of injection and production strategies. Next, an LSTM was trained to construct a surrogate model for predicting CO2 injection and production dynamics, significantly improving computational efficiency. Finally, the MOPSO algorithm was applied to obtain the Pareto frontier composed of optimal solutions, from which the best injection-production strategy was selected based on practical requirements. The results demonstrate that the constructed surrogate model achieves a high prediction accuracy, with R2 coefficients exceeding 0. 98 for both gas recovery and CO2 storage rates. Compared to the injection-production strategy before optimization, the optimized solution increases gas recovery and CO2 storage rates by 9. 03 and 5. 53 percentage points, respectively, confirming the reliability of the method. This study also provides valuable insights for designing CO2 injection-enhanced gas recovery and storage schemes in other similar gas reservoirs during late-stage development.

【基金】 国家自然科学基金项目“低渗透油藏CO2驱气窜通道表征识别与调控优化方法研究(编号:52104018)”;国家科技重大专项项目“致密气提高采收率机理与新技术(编号:2025ZD1404306)”;中国石油科技创新基金项目“基于连接单元体系的低渗透油藏CO2驱油封存高效模拟及调控优化研究(编号:2024DQ02-0303)”;中国石油天然气股份有限公司项目“天然气提高采收率重大开发试验(编号:2023YQX105)”;中国石油天然气集团有限公司科技项目“气藏采收率标定方法与复杂气藏提高采收率新技术研究(编号:2024DJ86)”部分研究成果
  • 【文献出处】 中国海上油气 ,China Offshore Oil and Gas , 编辑部邮箱 ,2025年06期
  • 【分类号】TE377
  • 【下载频次】71
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