Genetic Clustering Algorithm Based on the Division and Combination of Layer Series of Development

Abstract

Petroleum is a critical industrial material. In the process of oilfield development, the Fisher’s optimal segmentation method is often used to solve the problem of division and combination of layer series of development. However, when encountering the problem of long samples, this method has particularly obvious drawbacks due to the high storage requirements of the calculation process. Therefore, in practical work, the Fisher’s optimal binary segmentation method is generally used instead. Although it avoids storage problems, it is prone to falling into local optima. On the basis of analyzing the shortcomings of Fisher’s optimal segmentation and optimal binary segmentation algorithms, this paper processes a genetic clustering algorithm. This algorithm overcomes the problem of Fisher’s optimal binary segmentation algorithm easily falling into local optima and solves the problem of high storage capacity requirements in the calculation process of Fisher’s optimal segmentation algorithm. Taking the data of 17 subzones in S-2 8-11 sand groups of the Shahejie Formation of the Lower Tertiary in the Dongxin area as an example, this algorithm is applied to divide and combine layer series of development. The experimental results show that the algorithm’s partitioning results are reasonable and can optimize the selection of development layers and provide decision support for the production of oil and gas resources.