To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) s...To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.展开更多
To ensure a safe and stable CO_(2)storage,pressure responses at an observation well are expected to be an important and useful field monitoring item to estimate the CO_(2)storage behaviors and the aquifer parameters d...To ensure a safe and stable CO_(2)storage,pressure responses at an observation well are expected to be an important and useful field monitoring item to estimate the CO_(2)storage behaviors and the aquifer parameters during and after injecting CO_(2),because it can detect whether the injected CO_(2)leaks to the ground surface or the bottom of the sea.In this study,pressure responses were simulated to present design factors such as well location and pressure transmitter of the observation well.Numerical simulations on the pressure response and the time-delay from pressure build-up after CO_(2)injection were conducted by considering aquifer parameters and distance from the CO_(2)injection well to an observation well.The measurement resolution of a pressure transmitter installed in the observation well was presented based on numerical simulation results of the pressure response against pressure build-up at the injection well and CO_(2)plume front propagations.Furthermore,the pressure response at an observation well was estimated by comparing the numerical simulation results with the curve of CO_(2)saturation and relative permeability.It was also suggested that the analytical solution can be used for the analysis of the pressure response tendency using pressure build-up and dimensionless parameters of hydraulic diffusivity.Thus,a criterion was established for selecting a pressure transducer installed at an observation well to monitor the pressure responses with sufficient accuracy and resolution,considering the distance from the injection well and the pressure build-up at the injection well,for future carbon capture and storage(CCS)projects.展开更多
结合储层CO_(2)埋存技术,自主搭建了地层温度压力条件下CO_(2)埋存实验装置,开展了多介质辅助CO_(2)埋存实验研究。研究结果表明,乙醇-KOH体系能够有效进行CO_(2)矿化埋存,其中96%乙醇+3 g KOH 500 mL溶液捕集CO_(2)能力最强,是最佳的CO...结合储层CO_(2)埋存技术,自主搭建了地层温度压力条件下CO_(2)埋存实验装置,开展了多介质辅助CO_(2)埋存实验研究。研究结果表明,乙醇-KOH体系能够有效进行CO_(2)矿化埋存,其中96%乙醇+3 g KOH 500 mL溶液捕集CO_(2)能力最强,是最佳的CO_(2)矿化埋存溶液配比。经CO_(2)矿化埋存后,低渗透岩心孔隙度平均降低7.07%,孔隙度变化率与孔隙度呈正相关关系,渗透率平均降低16.01%。因此,96%乙醇+3 g KOH能够加速CO_(2)在储层中的CO_(2)沉淀过程,缩短CO_(2)在储层中的矿化埋存时间。该研究可重复性、准确性和可扩展性较强,能够激发学生自主设计实验的积极性及创新意识,培养学生的独立思考能力,有利于学生将理论知识与实际工程问题相结合,实现科研能力与创新能力的相互促进。展开更多
基金This work is funded by National Natural Science Foundation of China(Nos.42202292,42141011)the Program for Jilin University(JLU)Science and Technology Innovative Research Team(No.2019TD-35).The authors would also like to thank the reviewers and editors whose critical comments are very helpful in preparing this article.
文摘To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.
基金We acknowledge the funding support from the Research Fund for the special projects in key fields of Guangdong Universities(Grant No.2021ZDZX4074)the Japan Society for the Promotion of Science(Grant No.JP-20K21163)Scientific Research Fund of Hainan University(Approval No.KYQD(ZR)-22122).
文摘To ensure a safe and stable CO_(2)storage,pressure responses at an observation well are expected to be an important and useful field monitoring item to estimate the CO_(2)storage behaviors and the aquifer parameters during and after injecting CO_(2),because it can detect whether the injected CO_(2)leaks to the ground surface or the bottom of the sea.In this study,pressure responses were simulated to present design factors such as well location and pressure transmitter of the observation well.Numerical simulations on the pressure response and the time-delay from pressure build-up after CO_(2)injection were conducted by considering aquifer parameters and distance from the CO_(2)injection well to an observation well.The measurement resolution of a pressure transmitter installed in the observation well was presented based on numerical simulation results of the pressure response against pressure build-up at the injection well and CO_(2)plume front propagations.Furthermore,the pressure response at an observation well was estimated by comparing the numerical simulation results with the curve of CO_(2)saturation and relative permeability.It was also suggested that the analytical solution can be used for the analysis of the pressure response tendency using pressure build-up and dimensionless parameters of hydraulic diffusivity.Thus,a criterion was established for selecting a pressure transducer installed at an observation well to monitor the pressure responses with sufficient accuracy and resolution,considering the distance from the injection well and the pressure build-up at the injection well,for future carbon capture and storage(CCS)projects.
文摘结合储层CO_(2)埋存技术,自主搭建了地层温度压力条件下CO_(2)埋存实验装置,开展了多介质辅助CO_(2)埋存实验研究。研究结果表明,乙醇-KOH体系能够有效进行CO_(2)矿化埋存,其中96%乙醇+3 g KOH 500 mL溶液捕集CO_(2)能力最强,是最佳的CO_(2)矿化埋存溶液配比。经CO_(2)矿化埋存后,低渗透岩心孔隙度平均降低7.07%,孔隙度变化率与孔隙度呈正相关关系,渗透率平均降低16.01%。因此,96%乙醇+3 g KOH能够加速CO_(2)在储层中的CO_(2)沉淀过程,缩短CO_(2)在储层中的矿化埋存时间。该研究可重复性、准确性和可扩展性较强,能够激发学生自主设计实验的积极性及创新意识,培养学生的独立思考能力,有利于学生将理论知识与实际工程问题相结合,实现科研能力与创新能力的相互促进。