Optimization of fracture design for horizontal wells in Mahu region based on machine learning

Abstract

The Mahu tight glutenite reservoir is highly heterogeneous, and the geology and construction conditions of each well are uneven. The fracturing design scheme lacks pertinence. The main control factors of the fracturing effect need to be further understood. Therefore, we establish a set of machine learning-based process frameworks for horizontal well productivity prediction and fracturing design optimization in the Mahu area. Taking 75 fractured horizontal wells in the Mahu area as an example, based on 16 influencing factors of 2 types of geology and engineering, the random forest algorithm is used to determine the main control factors of post-fracturing productivity, and a error back Propagation (BP) neural network productivity prediction model optimized by genetic algorithm is established. Based on this, the optimal design of fracturing for horizontal wells is carried out. The results show that the productivity of two wells in the field is increased by 9.3% and 37.3% respectively after the fracturing parameter scheme is optimized. The established productivity prediction and fracturing design optimization methods can be applied to other oil fields and provide targeted guidance for on-site construction.