Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Javed Akbar Khan, Muhammad Irfan, Sonny Irawan, Fong Kam Yao, Md Shokor Abdul Rahaman, Ahmad Radzi Shahari, Adam Glowacz and Nazia Zeb
Additional contact information
Javed Akbar Khan: Petroleum Engineering Department and Shale Gas Research Group, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Muhammad Irfan: College of Engineering, Electrical Engineering Department, Najran University, Najran 61441, Saudi Arabia
Sonny Irawan: School of Mining & Geosciences, Nazarbayev University, Nur-Sultan City 010000, Kazakhstan
Fong Kam Yao: Petroleum Engineering Department and Shale Gas Research Group, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Md Shokor Abdul Rahaman: Fundamental and Applied Science Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Ahmad Radzi Shahari: Petroleum Engineering Department and Shale Gas Research Group, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
Adam Glowacz: Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
Nazia Zeb: Department of Computer and Information Sciences, University of Management & Technology, Lahore 55150, Pakistan

Energies, 2020, vol. 13, issue 14, 1-26

Abstract: 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 ( σ ) 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.

Keywords: artificial neural networks; drilling operation; machine learning classifiers; RBF Kernel function; stuck pipe; support vector machines; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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