Optimizing construction parameters for fractured horizontal wells in shale oil

Abstract

Shale oil is mainly extracted by fracturing. However, it is difficult to determine the optimum construction parameters to obtain maximum productivity. In this paper, a fuzzy comprehensive production evaluation model for fractured shale oil horizontal wells based on random forest algorithm and coordinated principal component analysis is proposed. The fracturing parameters of the target wells are optimized by combining this model with an orthogonal experimental design. The random forest algorithm was used to calculate the importance of data sample factors. The main controlling factors of the production of fractured horizontal wells in shale oil were obtained. To reduce the noise of the sample data, principal component analysis was used to reduce the dimensions of the main control factors. Furthermore, the random forest algorithm was used to determine the weight of the principal components after reducing the dimensionality. The membership function of the main control factors after reducing dimensionality was established by combining the fuzzy statistics and assignment methods. In addition, the membership matrix of the effect prediction of fractured horizontal wells in shale oil was determined. The fuzzy comprehensive evaluation method is used to score and evaluate the effect of fractured horizontal wells. Combined with the orthogonal experimental design method, the optimized parameter design of a fractured horizontal well considering the comprehensive action of multiple parameters is realized. After construction according to the optimized parameters, production following fracturing increases significantly. This verifies the rationality of the optimization method that is proposed in this paper.

Investigation of Key Controlling Factors and Applicability Boundaries of Natural Gas Injection for Shale Oil Development: A Case Study of Chang 7 Reservoir, Ordos Basin, China

“…The random forest method is a machine learning algorithm that is flexible and easy to use. As one of the classification and regression algorithms, the random forest algorithm uses a decision tree as the basic decision unit and generates multiple sample sets randomly by repeated sampling of samples [41][42][43]. The computation of random forest is divided into six main steps, and its decision process is shown in Figure 26.…”