New Methods of Fracture Effect Forecast of CBM Wells Based on KPCA and Artificial Neural Networks

Abstract

Based on the problems that coal bed methane (CBM) wells had no or low productions after fracturing, this paper combine the knowledge of logging technology, hydraulic fracturing technology, dewatering technology, optimization theory and artificial neural networks(ANN), a method of fracture effect forecast of CBM wells based on Kernel function of nonlinear principal component analysis (KPCA) and ANN is presented. This paper introduces the KPCA, ANN algorithm and sums up the influence of CBM well fracturing effect of the main factors. Through the method to the input of the model parameters were analyzed and the main parameters of the extraction, and based on this, established the ANN prediction model, and presents a case analysis. Application shows that the KPCA method combined with BP artificial neural network prediction of CBM well fracturing effect, simplified network structure, improve the computational speed, good practicability and reliability, is a worthy of promotion methods.