Rate of penetration prediction and optimization using advances in artificial neural networks, a comparative study

An important aspect of oil industry is rate of penetration (ROP) prediction. Many studies have been implemented to predict it. Mainly, multiple regression and artificial neural network models were used. In this paper, the objective is to compare the traditional multiple regression with two artificial intelligence techniques; extreme learning machines (ELM) and radial basis function networks (RBF). ELM and RBF are artificial neural network (ANNs) techniques. ANNs are cellular systems which can acquire, store, and utilize experiential knowledge. The techniques are implemented using MATLAB function codes. For ELM, the activation functions, number of hidden neurons, and number of data points in the training data set are varied to find the best combination. Different input parameters of ELM give different results. The comparison is made based on field data with no correction, then with weight on bit (WOB) correction, and finally with interpolated WOB and rotary speed (RPM) correction. Seven input parameters are used for ROP prediction. These are depth, bit weight, rotary speed, tooth wear, Reynolds number function, ECD and pore gradient. The techniques are compared in terms of training time and accuracy, and testing time and accuracy. Simulation experiments show that ELM gave the best results in terms of accuracy and processing time.