Parameter optimization of injection and production is an important method used to enhance recovery rate and reduce water cut, mainly aiming to determine the injection and production strategies during the development process to maximize the economic benefits throughout the reservoir development process. However,
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Parameter optimization of injection and production is an important method used to enhance recovery rate and reduce water cut, mainly aiming to determine the injection and production strategies during the development process to maximize the economic benefits throughout the reservoir development process. However, current optimization approaches for injection and production face challenges such as complex and inefficient optimization models and high-dimensional discrete variables, making it difficult to improve the global optimization ability of the algorithm (avoiding local optimal solutions during the optimization process) and the controllability of the time for completing the optimization of injection and production parameters under real and limited numerical simulations. This paper proposes a high-dimensional multi-discrete injection and production parameter non-gradient optimization method (MNOM), which combines the upper confidence bound (UCB) algorithm and effectively explores better injection and production systems and small-layer water-allocation schemes, achieving the maximization of net present value (NPV) over the entire development period. Specifically, this method models the injection and production parameter optimization problem as a Monte Carlo tree search process (Monte Carlo tree search, MCTS), and implements the optimization of injection and production cycles and small-layer water allocation through a genetic algorithm (CLGA) proxy optimized by a convolutional long short-term memory network (ConvLSTM). This method effectively overcomes the spatial and temporal limitations of the search process, maps production dynamics to the random strategies of injection and production parameters, and estimates the expected return of each policy. The CLGA proxy rapidly identifies suitable well-control schedules and water-allocation schemes in real time based on the production status at different development stages, thereby improving overall production performance. The proposed method has two innovative points. Firstly, MCTS can explore the large-scale discrete space of injection and production parameter optimization variables through tree decomposition, combined with the UCB incentive mechanism, to improve the global optimization ability. Secondly, the model training process is completely based on existing physical laws, with good temporal evolution, and the trained strategy can quickly adapt to the production status of the target layer without the need for a complete re-training from the beginning, enabling offline application and having good real-time controllability. In order to verify the effectiveness of the method proposed in the article, tests were conducted on a 3D reservoir actual model. Compared with gradient-based approaches, classical evolutionary algorithms, and proximal policy optimization (PPO), MNOM not only achieves stronger global search performance and requires 55–78% fewer iterations, but also improves the effective sweep volume by 1.1% to 5.5% compared to other optimization methods; furthermore, when compared with the PPO method, it is found that in offline optimization, if the production regime changes, the training strategy has better real-time controllability. The MNOM method can still maintain the original optimization effect compared to the PPO method when the production regime changes, demonstrating better engineering adaptability. The research results show that the proposed multi-strategy fusion high-dimensional multi-discrete injection and production parameter non-gradient optimization method can effectively improve recovery rate, expand effective sweep volume, and balance global optimization ability, optimization efficiency, and real-time controllability under the constraint of limited numerical simulations, and it has good engineering adaptability and application prospects.
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