Refracture candidate selection using hybrid simulation with neural network and data analysis techniques

By now very few analytical models have been developed to select well refracture candidates due to complicated multi-parameter relationships. In this study, we proposed a new method by merging mathematical data analysis with feed forward back propagation neural network utilizing post-fracturing data. The model preference is thereby based on the correlation coefficients of several selected independent variables against production performance. lution to this expense is a tool that can identify restimulation candidates quickly and economically. We employ two mathematical analysis techniques to filter several independent yet influential parameters as inputs. These parameters are supposed to be primary factors with high impact on potential production improvement. Then we use these well data to train an artificial neural network (ANN) to predict post-fracture production. The errors of the best samples should decrease consistently along with the training samples. A minimal error of the training sets is not necessary because over-fitting of the network could be memorizing rather than generalizing. The testing results showed that there is higher than 80% prediction accuracy, which is good enough for decision making. This methodology gives credible prediction results when it is applied in Zhongyuan oilfield and provides the operators with useful recommendations to make decisions for restimulation.