Abstract
Due to the unique physicochemical property of strong water sensitive glutenite reservoirs, a series of problems may occur during water injection development, seriously affecting production efficiency and economic benefits. This study analyzed the problems and their causes in the water injection development process, constructed a water injection optimization model based on machine learning algorithms, and rigorously verified and evaluated the performance of the model. The results show that the algorithm proposed in this article has achieved significant advantages in prediction accuracy, reducing prediction error by 28.26%, and maintaining a high recall. In addition, as the sample size increases, the algorithm in this article performs more stably. When facing complex reservoir environments and a large amount of data, this algorithm can provide more accurate and reliable prediction results, providing strong support for the optimization of reservoir water injection development process. The research results can provide an effective decision support tool for the field of petroleum engineering, which helps to improve the efficiency and economy of oil extraction, and promote the sustainable growth of the petroleum industry.
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