Active learning based surrogate ensemble assisted multi-objective optimization framework for reservoir water-flooding optimization

Surrogate-assisted management of water-flooding, as a reservoir production optimization strategy, dynamically adjusts development schemes at each production step, leading to improved economic benefits and enhanced recovery. However, many computationally expensive numerical simulation runs are needed to build the surrogate model in most existing surrogate-assisted reservoir production optimization methods. With that in mind, this study proposes an efficient intelligent optimization method based on active learning strategy and surrogate ensemble for multi-objective reservoir production optimization named ALSA-MOPO. In this proposed ALSA-MOPO method, three frequently-used surrogate models, the radial basis function network, Gaussian process regression, and support vector regression are adopted to construct the surrogate ensemble. In addition, an active learning strategy is adopted to reduce the sample of establishing surrogate model and query for the worse and best samples based on the surrogate ensemble using particle swarm optimization which are infilled to the dataset for improving the accuracy and quality of the surrogate ensemble. The ALSA-MOPO method stands out due to its unique use of an active learning strategy to enhance the accuracy of the surrogate model, combined with a surrogate ensemble to improve robustness. Furthermore, two synthetic reservoirs with different scales and one complex fault block reservoir were utilized to test the effectiveness and practicability of the ALSA-MOPO method. The optimization results indicated that the ALSA-MOPO framework outperformed numerical simulation-based methods by approximately 50, 20, and 35 times in the three respective cases.