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<p>To obtain the maximum field oil recovery (<i>FOR</i>) and CO<sub>2</sub> sequestration ratio (<i>CSR</i>), it is imperative to optimize the CO<sub>2</sub> injection and liquid production rate. However, previous studies ignore the geomechanical risks indeed. Therefore, a hybrid optimization framework was designed that combines artificial intelligence methods (Support Vector Regression with the Gaussian kernel, Gaussian-SVR or Long Short-Term Memory, LSTM) and multi-objective optimization algorithms (multiple objective particle swarm optimization, MOPSO or Non-dominated Sorting Genetic Algorithm II, NSGA-II) to find the optimal CO<sub>2</sub> injection and production strategies under different water cut. With this framework, the largest oil recovery and CO<sub>2</sub> storage under the lowest fault slip displacement (<i>FSD</i>) can be obtained simultaneously. In this framework, Latin hypercube sampling (LHS) is used to produce the samples for training and testing for cases with water cut 0.7, 0.8, 0.9 and 0.95, and the corresponding results are obtained from numerical simulations. Thus, Gaussian-SVR and LSTM are trained as the proxy model to substitute the numerical simulator. Thus, the MOPSO and NSGA-II are utilized to determine the Pareto Front of the optimum result and work schedules. A synthetic case reservoir model with high-water cut and one fault is employed to test the robustness of this framework. The results show that compared with <i>FOR</i> and <i>CSR</i>, due to the serious nonlinearity, the training and prediction of <i>FSD</i> with the proxy model are not very good. The prediction errors increase with the water cut, and when the field water cut is larger than 0.9, the practical requirements (± 20% errors) are not yet met. In general, the performance of proxy model with LSTM is superior to the Gaussian-SVR. The solutions obtained from the Pareto optimal set for the NSGA-II algorithm exhibit faster convergence, better superiority and reliability than MOPSO. As the rise of water cut, the optimal average field gas injection rate (<i>FGIR</i>) decreases, while the average field liquid production rate (<i>FLPR</i>) increases. The novelty of this work mainly lies in the consideration of fault slip during CO<sub>2</sub> injection for multi-objective optimization in high-water cut oil reservoirs, which can provide some guidance for the design of schemes.</p>

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Multiobjective optimization of CO2 injection under geomechanical risk in high water cut oil reservoirs using artificial intelligence approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月15日

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  • Fankun Meng,
  • Jia Liu,
  • Gang Tong,
  • Hui Zhao,
  • Chengyue Wen,
  • Yuhui Zhou,
  • Vamegh Rasouli,
  • Minou Rabiei

摘要

To obtain the maximum field oil recovery (FOR) and CO2 sequestration ratio (CSR), it is imperative to optimize the CO2 injection and liquid production rate. However, previous studies ignore the geomechanical risks indeed. Therefore, a hybrid optimization framework was designed that combines artificial intelligence methods (Support Vector Regression with the Gaussian kernel, Gaussian-SVR or Long Short-Term Memory, LSTM) and multi-objective optimization algorithms (multiple objective particle swarm optimization, MOPSO or Non-dominated Sorting Genetic Algorithm II, NSGA-II) to find the optimal CO2 injection and production strategies under different water cut. With this framework, the largest oil recovery and CO2 storage under the lowest fault slip displacement (FSD) can be obtained simultaneously. In this framework, Latin hypercube sampling (LHS) is used to produce the samples for training and testing for cases with water cut 0.7, 0.8, 0.9 and 0.95, and the corresponding results are obtained from numerical simulations. Thus, Gaussian-SVR and LSTM are trained as the proxy model to substitute the numerical simulator. Thus, the MOPSO and NSGA-II are utilized to determine the Pareto Front of the optimum result and work schedules. A synthetic case reservoir model with high-water cut and one fault is employed to test the robustness of this framework. The results show that compared with FOR and CSR, due to the serious nonlinearity, the training and prediction of FSD with the proxy model are not very good. The prediction errors increase with the water cut, and when the field water cut is larger than 0.9, the practical requirements (± 20% errors) are not yet met. In general, the performance of proxy model with LSTM is superior to the Gaussian-SVR. The solutions obtained from the Pareto optimal set for the NSGA-II algorithm exhibit faster convergence, better superiority and reliability than MOPSO. As the rise of water cut, the optimal average field gas injection rate (FGIR) decreases, while the average field liquid production rate (FLPR) increases. The novelty of this work mainly lies in the consideration of fault slip during CO2 injection for multi-objective optimization in high-water cut oil reservoirs, which can provide some guidance for the design of schemes.