%T Comparison of machine learning classifiers for accurate prediction of real-time stuck pipe incidents
%A J.A. Khan
%A M. Irfan
%A S. Irawan
%A F.K. Yao
%A Md. Shokor Abdul Rahaman
%A A.R. Shahari
%A A. Glowacz
%A N. Zeb
%I MDPI AG
%V 13
%O cited By 14
%L scholars12937
%J Energies
%D 2020
%R 10.3390/en13143683
%N 14
%X Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions-namely, the logistic activation function and hyperbolic tangent activation function-were tested. Additionally, all the possible combinations of network structures, from 19, 1, 1, 1, 1 to 19, 10, 10, 10, 1, were tested for each activation function. For the SVM, three kernel functions-namely, linear, Radial Basis Function (RBF) and polynomial-were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (�) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
%K Bottom-hole assembly; Chemical activation; Drilling fluids; Forecasting; Hyperbolic functions; Infill drilling; Predictive analytics; Sensitivity analysis; Support vector machines; Well drilling, Activation functions; Drilling parameters; Machine learning models; Operating condition; Prediction performance; Predictive modeling; Radial Basis Function(RBF); Sensitivity and specificity, Learning systems