Application of Machine Learning Approach for Intelligent Prediction of Pipe Sticking

Abstract

Abstract Stuck pipe has been recognized as one of the serious problems in drilling operations that has a significant impact on drilling efficiency and well costs. The events related to the stuck pipe can be responsible for losses of hundreds of millions of dollars each year in the drilling industry. This paper presents a study on the application of machine learning methodologies to predict the stuck pipe occurrence which can be utilized to modify drilling variables to minimize the likelihood of sticking. The new models were developed to predict the stuck pipe incidence for vertical and deviated wells using artificial neural networks (ANNs) and a support vector machine (SVM). The proposed models were examined using a few examples of real stick pipe cases from the field. The results of the analysis have revealed that both ANNs and SVM approaches can be of great use, with the SVM results being more promising. The present analysis supplies knowledge that can be used during well pre-planning and developmental phases to make informed decisions that will avoid pipe sticking problems and essentially optimize drilling performance. The risk of pipe sticking can then be minimized and the costs associated with its occurrence will be reduced.