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

作     者：Vaziri, Pouya Sedaee, Behnam 

作者机构：Univ Tehran Inst Petr Engn Coll Engn Chem Engn Dept Tehran Iran 

出 版 物：《CLEAN ENERGY》 (清洁能源（英文）)

年 卷 期：2024年第8卷第1期

页      面：111-125页

核心收录：

学科分类：0820[工学-石油与天然气工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 081803[工学-地质工程] 08[工学] 0818[工学-地质资源与地质工程] 

主　　题：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 

摘      要：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