An Unsupervised Clustering Method for Selection of the Fracturing Stage Design Based on the Gaussian Mixture Model

Abstract

In order to further improve the efficiency and economic benefits of multi-stage fracturing of unconventional oil and gas horizontal wells, it is urgently needed to conduct comprehensive reservoir quality evaluation research on the whole horizontal well section. Firstly, based on logging data, focusing on reservoir quality and completion quality, and comprehensively considering key factors such as reservoir physical property indexes and fracability indexes, a subjective and objective coupled evaluation model of the entropy weight method (EWM) and the analytic hierarchy process (AHP) without bias is established to obtain the composite reservoir quality index. Then, unsupervised gaussian mixture model (GMM) clustering algorithms are used to classify the reservoir comprehensive quality index and finally four grades of fracturing stages are established. Taking shale oil well A and B of the Permian Lucaogou Formation in Jimsar Sag, Junggar Basin, as examples, the comprehensive reservoir quality evaluation and clustering model training, testing, and prediction were carried out. By comparing the clustering results with the actual fracturing stages and oil production, it is found that the evaluation results obtained by the GMM clustering algorithms based on the coupled evaluation model of EWM and AHP can identify the good fracturing grades. The algorithm can also predict the fracturing grades of other wells in the same block. It proves the accuracy of the method proposed in this paper and provides a favorable technical basis for determining the placement of multi-cluster fracturing perforation.