Application of artificial bee colony-based neural network in bottom hole pressure prediction in underbalanced drilling

Two phase flow through annulus is a complex area of study in evaluating the bottom hole circulating pressure (BHCP). Based on the over-prediction of empirical correlations and the erroneous assumption of hydraulic diameter concept, both methods suffer from a great deal of error. As a result, it is investigated in this work how artificial neural network (ANN) evolution with artificial bee colony (ABC) improves the efficiency and prediction capability of artificial neural network. The proposed methodology adopts a hybrid ABC-back propagation (BP) strategy (ABC-BP). The proposed algorithm combines the local searching ability of the gradient-based back-propagation (BP) strategy with the global searching ability of artificial bee colony. For an evaluation purpose, the performance and generalization capabilities of ABC-BP are compared with those of models developed with the common technique of BP. The results demonstrate that carefully designed hybrid artificial bee colony-back propagation neural network outperforms the gradient descent-based neural network.