Developing a New Model for Drilling Rate of Penetration Prediction Using Convolutional Neural Network

Before adjustable parameters of drilling can be optimized, it is necessary to have a high-accuracy model for predicting the rate of penetration (ROP), which can represent the effects of drilling parameters and formation-related factors on the ROP. Accordingly, the present research attempts to use different algorithms, including convolutional neural network (CNN), simple form of least square support vector machine (LSSVM) and its hybrid forms with either particle swarm optimization (PSO), cuckoo optimization algorithm (COA), or genetic algorithm (GA), and also hybrids of multilayer extreme learning machine with either of COA, PSO, or GA, to model ROP based on mud-logging and petrophysical data along two wells (Wells A and B). For this purpose, firstly, median filtering was applied to the data for the sake of denoising. Next, petrophysical logs were upscaled to make their scales matched to that of mud-logging data. Then, the nondominated sorting genetic algorithm (NSGA-II) was combined with a multilayer perceptron (MLP) neural network to select the set of the most significant features for estimating the ROP on the data from Well A. Feature selection results showed that the accuracy of the estimator models increases with the number of parameters to a maximum of seven, beyond which only subtle enhancements were seen in the modeling accuracy. Accordingly, the ROP was modeled using depth, bit rotary speed, mud weight, weight on bit, compressive wave slowness, total flow rate, and neutron porosity. Training the hybrid, CNN, and LSSVM algorithms using the training data from Well A showed that the model built upon the CNN algorithm tends to produce the smallest root-mean-square error (RMSE = 1.7746 ft/hr), as compared to the other models. In addition, the smaller difference in error between the training and testing phases (RMSE = 2.5356 ft/hr) for this model indicates its high generalizability. This fact was proved by the lower estimation error of this model for predicting the ROP at Well B, as compared to other models.