{"title":"基于代理的页岩储层CO2循环注入筛选与优化工作流程","authors":"Ming Ma, Qian Zhang, Hamid Emami-Meybodi","doi":"10.1016/j.fuel.2025.135742","DOIUrl":null,"url":null,"abstract":"<div><div>Optimizing cyclic CO<sub>2</sub> injection (CO<sub>2</sub> HnP) in shale reservoirs is challenging due to the numerous variables in the system, which exhibit complex coupling effects on the final hydrocarbon recovery. A workflow combining a multicomponent species transport model and a proxy model is proposed to identify suitable target blocks and optimize CO<sub>2</sub> HnP operational parameters for maximizing cumulative oil production. A single-well CO<sub>2</sub> HnP compositional simulation is developed based on a multiphase, multicomponent species transport model. This model accounts for key transport mechanisms such as viscous flow, molecular diffusion, and Knudsen diffusion in shale reservoirs. Least-squares support vector machine (LS-SVM) is used as a proxy for the simulation model to reduce computational costs in subsequent optimization processes. The optimal combination of operational parameters, as well as reservoir rock and fluid properties, is investigated to maximize oil recovery. Finally, the LS-SVM proxy model is integrated with a genetic algorithm to perform robust optimization. The Results and Discussion section presents three optimization scenarios derived from baseline parameters of the Eagle Ford shale reservoir, progressively incorporating more variables. The LS-SVM proxy model demonstrates its high predictive accuracy with a small training dataset, outperforming three alternative approaches: Long Short-Term Memory (LSTM), Genetic Algorithm-optimized Back Propagation (GA-BP) neural networks, and Extreme Gradient Boosting (XGBoost). A thorough optimization process is crucial to achieve higher oil recovery, potentially increasing CO<sub>2</sub> HnP recovery from 11.64 % to 19.13 % through the design of operational parameters. The findings also indicate that a larger volume of injected CO<sub>2</sub> leads to greater enhanced oil recovery by enabling deeper penetration into the reservoir and more effective mixing with crude oil. Furthermore, deep reservoirs containing low gas–oil ratio black oil are especially favorable for cyclic CO<sub>2</sub> HnP, as the injected CO<sub>2</sub> substantially enhances oil swelling and improves production potential.</div></div>","PeriodicalId":325,"journal":{"name":"Fuel","volume":"400 ","pages":"Article 135742"},"PeriodicalIF":6.7000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A proxy-based workflow for screening and optimizing cyclic CO2 injection in shale reservoirs\",\"authors\":\"Ming Ma, Qian Zhang, Hamid Emami-Meybodi\",\"doi\":\"10.1016/j.fuel.2025.135742\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Optimizing cyclic CO<sub>2</sub> injection (CO<sub>2</sub> HnP) in shale reservoirs is challenging due to the numerous variables in the system, which exhibit complex coupling effects on the final hydrocarbon recovery. A workflow combining a multicomponent species transport model and a proxy model is proposed to identify suitable target blocks and optimize CO<sub>2</sub> HnP operational parameters for maximizing cumulative oil production. A single-well CO<sub>2</sub> HnP compositional simulation is developed based on a multiphase, multicomponent species transport model. This model accounts for key transport mechanisms such as viscous flow, molecular diffusion, and Knudsen diffusion in shale reservoirs. Least-squares support vector machine (LS-SVM) is used as a proxy for the simulation model to reduce computational costs in subsequent optimization processes. The optimal combination of operational parameters, as well as reservoir rock and fluid properties, is investigated to maximize oil recovery. Finally, the LS-SVM proxy model is integrated with a genetic algorithm to perform robust optimization. The Results and Discussion section presents three optimization scenarios derived from baseline parameters of the Eagle Ford shale reservoir, progressively incorporating more variables. The LS-SVM proxy model demonstrates its high predictive accuracy with a small training dataset, outperforming three alternative approaches: Long Short-Term Memory (LSTM), Genetic Algorithm-optimized Back Propagation (GA-BP) neural networks, and Extreme Gradient Boosting (XGBoost). A thorough optimization process is crucial to achieve higher oil recovery, potentially increasing CO<sub>2</sub> HnP recovery from 11.64 % to 19.13 % through the design of operational parameters. The findings also indicate that a larger volume of injected CO<sub>2</sub> leads to greater enhanced oil recovery by enabling deeper penetration into the reservoir and more effective mixing with crude oil. Furthermore, deep reservoirs containing low gas–oil ratio black oil are especially favorable for cyclic CO<sub>2</sub> HnP, as the injected CO<sub>2</sub> substantially enhances oil swelling and improves production potential.</div></div>\",\"PeriodicalId\":325,\"journal\":{\"name\":\"Fuel\",\"volume\":\"400 \",\"pages\":\"Article 135742\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2025-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fuel\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S001623612501467X\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fuel","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S001623612501467X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
摘要
页岩储层循环CO2注入(CO2 HnP)的优化具有挑战性,因为系统中存在许多变量,这些变量对最终的油气采收率表现出复杂的耦合效应。提出了一种多组分物种迁移模型和代理模型相结合的工作流程,以确定合适的目标区块并优化CO2 HnP操作参数,以最大限度地提高累积产油量。基于多相、多组分物种输运模型,建立了单井CO2 HnP组分模拟。该模型考虑了页岩储层中的粘性流动、分子扩散和Knudsen扩散等关键输运机制。采用最小二乘支持向量机(Least-squares support vector machine, LS-SVM)作为仿真模型的代理,以减少后续优化过程的计算成本。研究了作业参数以及储层岩石和流体性质的最佳组合,以最大限度地提高采收率。最后,将LS-SVM代理模型与遗传算法相结合,进行鲁棒优化。结果和讨论部分给出了从Eagle Ford页岩储层基线参数推导出的三种优化方案,逐步纳入更多变量。LS-SVM代理模型通过小型训练数据集证明了其高预测精度,优于三种替代方法:长短期记忆(LSTM),遗传算法优化的反向传播(GA-BP)神经网络和极端梯度增强(XGBoost)。为了实现更高的采收率,一个彻底的优化过程是至关重要的,通过设计操作参数,有可能将二氧化碳HnP采收率从11.64%提高到19.13%。研究结果还表明,更大的二氧化碳注入量可以通过更深地渗透到储层中,更有效地与原油混合,从而提高石油采收率。此外,含低气油比黑色油的深层油藏尤其有利于循环CO2 HnP,因为注入的CO2大大增强了油的溶胀,提高了生产潜力。
A proxy-based workflow for screening and optimizing cyclic CO2 injection in shale reservoirs
Optimizing cyclic CO2 injection (CO2 HnP) in shale reservoirs is challenging due to the numerous variables in the system, which exhibit complex coupling effects on the final hydrocarbon recovery. A workflow combining a multicomponent species transport model and a proxy model is proposed to identify suitable target blocks and optimize CO2 HnP operational parameters for maximizing cumulative oil production. A single-well CO2 HnP compositional simulation is developed based on a multiphase, multicomponent species transport model. This model accounts for key transport mechanisms such as viscous flow, molecular diffusion, and Knudsen diffusion in shale reservoirs. Least-squares support vector machine (LS-SVM) is used as a proxy for the simulation model to reduce computational costs in subsequent optimization processes. The optimal combination of operational parameters, as well as reservoir rock and fluid properties, is investigated to maximize oil recovery. Finally, the LS-SVM proxy model is integrated with a genetic algorithm to perform robust optimization. The Results and Discussion section presents three optimization scenarios derived from baseline parameters of the Eagle Ford shale reservoir, progressively incorporating more variables. The LS-SVM proxy model demonstrates its high predictive accuracy with a small training dataset, outperforming three alternative approaches: Long Short-Term Memory (LSTM), Genetic Algorithm-optimized Back Propagation (GA-BP) neural networks, and Extreme Gradient Boosting (XGBoost). A thorough optimization process is crucial to achieve higher oil recovery, potentially increasing CO2 HnP recovery from 11.64 % to 19.13 % through the design of operational parameters. The findings also indicate that a larger volume of injected CO2 leads to greater enhanced oil recovery by enabling deeper penetration into the reservoir and more effective mixing with crude oil. Furthermore, deep reservoirs containing low gas–oil ratio black oil are especially favorable for cyclic CO2 HnP, as the injected CO2 substantially enhances oil swelling and improves production potential.
期刊介绍:
The exploration of energy sources remains a critical matter of study. For the past nine decades, fuel has consistently held the forefront in primary research efforts within the field of energy science. This area of investigation encompasses a wide range of subjects, with a particular emphasis on emerging concerns like environmental factors and pollution.