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An application of a genetic algorithm in co-optimization of geological CO2 storage based on artificial neural networks

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CLEAN ENERGY 2024年第1期8卷 111-125页

作者： Vaziri, Pouya Sedaee, Behnam Univ Tehran Inst Petr Engn Coll Engn Chem Engn Dept Tehran Iran

Global warming, driven by human-induced disruptions to the natural carbon dioxide (CO2) cycle, is a pressing concern. To mitigate this, carbon capture and storage has emerged as a key strategy that enables the continued use of fossil fuels while transitioning to cleaner energy sources. deep saline aquifers are of particular interest due to their substantial CO2 storage potential, often located near fossil fuel reservoirs. In this study, a deep saline aquifer model with a saline water production well was constructed to develop the optimization workflow. Due to the time-consuming nature of each realization of the numerical simulation, we introduce a surrogate aquifer model derived from extracted data. The novelty of our work lies in the pioneering of simultaneous optimization using machine learning within an integrated framework. Unlike previous studies, which typically focused on single-parameter optimization, our research addresses this gap by performing multi-objective optimization for CO2 storage and breakthrough time in deep saline aquifers using a data-driven model. Our methodology encompasses preprocessing and feature selection, identifying eight pivotal parameters. Evaluation metrics include root mean square error (RMSE), mean absolute percentage error (MAPE) and R-2. In predicting CO2 storage values, RMSE, MAPE and R-2 in test data were 2.07%, 1.52% and 0.99, respectively, while in blind data, they were 2.5%, 2.05% and 0.99. For the CO2 breakthrough time, RMSE, MAPE and R-2 in the test data were 2.1%, 1.77% and 0.93, while in the blind data they were 2.8%, 2.23% and 0.92, respectively. In addressing the substantial computational demands and time-consuming nature of coupling a numerical simulator with an optimization algorithm, we have adopted a strategy in which the trained artificial neural network is seamlessly integrated with a multi-objective genetic algorithm. Within this framework, we conducted 5000 comprehensive experiments to rigorously validate the d

关键词： carbon capture and storage deep saline aquifer artificial neural network multi-objective optimization genetic algorithm breakthrough time geological storage saline aquiferS CARBON-DIOXIDE SEQUESTRATION SIMULATION DISSOLUTION PREDICTION SYSTEMS MODELS

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Potential assessment of CO_(2)geological storage based on injection scenario simulation:A case study in eastern Junggar Basin

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Journal of Groundwater Science and Engineering 2021年第4期9卷 279-291页

作者： Xin Ma Dong-guang Wen Guo-dong Yang Xu-feng Li Yu-jie Diao Hai-hai Dong Wei Cao Shu-guo Yin Yan-mei Zhang Center for Hydrogeology and Environmental Geology Survey China Geological SurveyBaoding 071051HebeiChina Key Laboratory of Carbon Dioxide Geological Storage China Geological SurveyBaoding 071051HebeiChina College of Resources and Environmental Engineering Wuhan University of Science and TechnologyWuhan 430081China Hubei Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resources Wuhan University of Science and TechnologyWuhan 430081China Exploration and Development Research Institute PetroChina Xinjiang Oilfield CompanyKaramay 834000China Institute of Geology of Pudong Oil Production Plant Sinopec Zhongyuan OilfieldPuyang 457001China Exploration and Development Research Institute of Zhundong Oil Production Plant Petro China Xinjiang Oilfield CompanyFukang831511XinjiangChina

Carbon Capture and Storage(CCS)is one of the effective means to deal with global warming,and saline aquifer storage is considered to be the most promising storage *** Basin,located in the northern part of Xinjiang and with a large distribution area of saline aquifer,is an effective carbon storage *** on well logging data and 2D seismic data,a 3D heterogeneous geological model of the Cretaceous Donggou Formation reservoir near D7 well was constructed,and dynamic simulations under two scenarios of single-well injection and multi-well injection were carried out to explore the storage potential and CO2 storage mechanism of deep saline aquifer with real geological conditions in this *** results show that within 100 km^(2)of the saline aquifer of Donggou Formation in the vicinity of D7 well,the theoretical static CO_(2)storage is 71.967×106 tons(P50)①,and the maximum dynamic CO_(2)storage is 145.295×106 tons(Case2).The heterogeneity of saline aquifer has a great influence on the spatial distribution of CO_(2)in the *** multi-well injection scenario is conducive to the efficient utilization of reservoir space and safer for *** on the results from theoretical static calculation and the dynamic simulation,the effective coefficient of CO_(2)storage in deep saline aquifer in the eastern part of Xinjiang is recommended to be 4.9%.This study can be applied to the engineering practice of CO_(2)sequestration in the deep saline aquifer in Xinjiang.

关键词： CO_(2)geological storage deep saline aquifer Potential assessment Injection scenarios Numerical simulation Junggar Basin

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Feasibility study of CO2 geological storage in China

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Acta Geochimica 2006年第S1期 280-280页

作者： Yanfeng Liu, Xiaohui Lu School of Environmental Studies, China University of Geosciences, Wuhan 430074, China

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