Computational prediction of the drilling rate of penetration (ROP): A comparison of various machine learning approaches and traditional models

Abstract

Rate of penetration (ROP) prediction, can assist precise planning of drilling operations and can reduce drilling costs. However, easy estimation of this key factor by traditional or experimental models is very difficult. This requires comparing available models for achieving the best prediction approach. In this study, four machine learning (ML) methods and two traditional ROP models were utilized to predict ROP. ML techniques include multilayer perceptron neural network (MLPNN), radial basis function neural network (RBFNN), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR). MLPNN, ANFIS, and RBFNN methods were trained with four meta-heuristic algorithms including particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and genetic algorithm (GA). The backpropagation (BP) algorithm was also incorporated to train the ANFIS and MLPNN methods as a conventional method. For comparison purposes, the traditional ROP models of Bourgoyne and Young (BYM) and Bingham were also implemented in combination with four meta-heuristic algorithms. The required data were collected from the mud logging unit (MLU) and the final report of a drilled well located in southwestern of Iran. In the MLU, the information of different sensors is firstly collected through the data communications protocols and sent to a master unit to be processed into relevant dimensions (i.e. create operational variables). In order to accurately record information, the sensors are calibrated at regular intervals. After removing the outliers, the overall noise of data was reduced by Savitzky-Golay (SG) smoothing filter. Then, in order to simulate a drilling process in a realistic manner and also to evaluate the performance of the models in approximating the penetration rate at greater depth of hole, the available data were divided into 6 sections based on depth. After that, sections 2 Data collection and preprocessing , 3 Methodology , 4 Model development and ROP estimation , 5 Discussion and results , 6 Conclusion were separately used as a test dataset and all previous sections were considered as a training dataset. Results indicated that PSO-MLPNN achieved the highest performance in comparing with other developed models. The concluding remark is that ML models are more efficient and reliable than traditional models. In addition, combining ML models with meta-heuristic algorithms can achieve better results than conventional algorithms such as BP. The results of this study can be used as a practical guide for the management and planning of future well drilling. • Applying Savitzky-Golay (SG) filter to remove noise from data. • Data were divided into 6 sections. Section 2 to 6 were separately noted as test and the previous one(s) as training dataset. • Comparison of four machine learning (ML) methods and two traditional ROP models. • Coupling models with meta-heuristic algorithms and conventional algorithms. • PSO-MLPNNmodel showed the highest accuracy compared to others.