A Novel RBF Neural Network and Application of Optimizing Fracture Design

Abstract

In this work, the factors affecting performance of fractured wells were analyzed. The static and dynamic geologic data of fractured well and fracturing treatment parameters obtained from 51 fractured wells in sand reservoirs of Zhongyuan oilfield were analyzed by applying the grey correlation method. Ten parameters were screened, including penetrability, porosity, net thickness, oil saturation, water cut, average daily production, and injection rate, amount cementing front spacer, amount sand-carrying agent and amount sand. With the novel RBF neural network model based on immune principles, the 13 parameters of 42 wells out of 51 were used as the input samples and the stimulation ratios as the output samples. The nonlinear interrelationship between the input samples and output samples were investigated, and a productivity prediction model of optimizing fracture design was established. The data of the rest 7 wells were used to test the model. The results showed that the relative errors are all less than 7%, which proved that the novel RBF neural network model based on immune principles has less calculation, high precision and good generalization ability.