Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field

Rate of Penetration (ROP) can be considered as a crucial factor in optimization and cost minimization of drilling operations. In order to predict ROP with satisfactory precision, some hybrid artificial intelligence models were developed in the present research. To achieve this, dataset including petrophysical logs and drilling data was collected from a vertical well drilled in Marun oil field where is in southwest corner of Iran. Then, overall noise of the collected data was reduced with implementing Savitzky-Golay (SG) smoothing filter. In the next stage, a feature selection method with application of genetic algorithm was employed for choosing the unrivaled features and lessening the input vectors. Regarding results of the feature selection method, eight parameters including weight on bit, bit rotational speed, pump flow rate, pump pressure, pore pressure, gamma ray, density log and shear wave velocity were identified as the most influential parameters in drilling rate. After that, eight hybrid artificial neural networks (ANN) were developed and trained by four evolutionary algorithms. These algorithms are genetic algorithm (GA), particle swarm optimization (PSO), biogeography-based optimizer (BBO) and imperialist competitive algorithm (ICA). After model developments, to provide an objective assessment of performances of the proposed hybrid models, their results were compared with results of conventional ANN models and two multiple regression methods (NLMR and LMR) using different performance indices such as root mean square error (RMSE), variance account for (VAF), and performance index (PI). Results showed that PSO-multilayer perception (PSO-MLP) and PSO-radial basis function (PSO-RBF) neural networks with RMSE of 1.12 and 1.4, respectively yielded the highest performance in prediction comparing to results of other developed models. Concluding remark is that hybrid ANNs exhibit much more efficiency and reliability than conventional ANNs and regression methods in terms of ROP prediction, indicating superiority of evolutionary algorithms over back propagation (BP) methods in the ANNs training.