Prediction and Optimization of Rate of Penetration using a Hybrid Artificial Intelligence Method based on an Improved Genetic Algorithm and Artificial Neural Network

Abstract

Abstract Oil and gas exploration is facing an ever-increasing demand for cost-efficient drilling operations. Improvement of the rate of penetration (ROP) of the drill bit is key in solving the aforementioned challenge. The objective of this study is to develop a more accurate and effective predictive and optimization model for ROP that utilizes a hybrid artificial intelligence model based on an improved genetic algorithm (IGA) and artificial neural network (ANN) for further optimization of drilling processes. Real field drilling datasets such as the bit type, bit drilling time, rotation per minute, weight on bit, torque, formation type, rock properties, hydraulics, and drilling mud properties are collected and input to train, validate and test the developed IGA-ANN model for ROP prediction and optimization. We apply a Savitzky-Golay (SG) smoothing filter to reduce the noise from the raw datasets. We apply IGA to find the optimal structures, parameters, and types of input of the ANN. By using supplementary population, multi-type crossover and mutation and adaptive dynamics probability adjustments, the developed model, IGA-ANN, avoid the limited optimization and local convergence problems in the classical Genetic Algorithm (GA). Using the developed prediction model, we obtain the optimal operational parameter within a region considering drilling equipment capability and wear to maximize ROP. From numerical results, we find that the optimal structures and parameters of the ANN can be obtained efficiently by the developed method. For comparison, we compare IGA-ANN with the classical wrapper algorithm for parameter selection. The results indicate that IGA-ANN is more stable and accurate than the wrapper algorithm. We compare the true ROP and predicted ROP from the developed IGA-ANN model using accuracy indicators such as root mean square error, mean absolute error, and regression coefficient (R2). For comparison, the accuracy of the classical regression model is presented. We find that IGA-ANN yielded more accurate test results (R2 = 0.97). We compare the results of IGA-ANN trained using SG smoothing filter processed data and raw data. The test results show that the noise reduction approach used is very efficient in increasing the accuracy of IGA-ANN. Using the developed model, we optimize the choice of drilling operational parameters within a region considering drilling equipment capability and wear. We find that the optimization can increase average ROP significantly. We develop an efficient and robust algorithm, IGA-ANN, for ROP prediction and optimization. Compared with the classical wrapper algorithm and multiple regression model, the IGA-ANN can efficiently optimize the structures, parameters and types of inputs of ANN to achieve higher ROP prediction accuracy. By utilizing the developed model, we can efficiently maximize ROP and minimize the drilling operation cost.