RBF network based on artificial immune algorithm and application of predicting the residual life of injecting water pipeline

In this work, the factors affecting residual life of injecting water pipeline were analyzed. Ten parameters were screened from 9 injecting water pipelines in Shengli oilfield by applying the grey correlation method, 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 10 parameters of 7 injecting water pipelines were used as the input samples and the residual life of injecting water pipeline as the output samples. The nonlinear interrelationship between the input samples and output samples were investigated, and a prediction model of residual life of injecting water pipeline was established. The data of the rest 2 injecting water pipelines were used to test the model. The results showed that the relative errors are all less than 6%, which proved that the novel RBF neural network model based on immune principles has less calculation, high precision and good generalization ability.