Implementing artificial neural networks and support vector machines in stuck pipe prediction
Stuck pipe has been recognized as one of the most challenging and costly problems in the oil and gas industry. However, this problem can be treated proactively through predicting it before it occurs. The purpose of this study is to implement the two most powerful machine learning methods, Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), to predict stuck pipe occurrences. Two developed models for ANNs and SVMs with different scenarios were implemented for prediction purposes. The models were designed and constructed by the MATLAB language. The MATLAB built-in functions of ANNs and SVMs, and the MATLAB interface from the library of support vector machines were applied to compare the results. Furthermore, two databases that include mud properties, directional characteristics, and drilling parameters have been assembled for the training and testing processes. The study involved classifying stuck pipe incidents into two groups - stuck and non-stuck - and also into three groups: differentially stuck, mechanically stuck, and non-stuck. This research also has gone through optimization process which is vital in machine learning techniques to construct the most practical models. It showed that both ANNs and SVMs are able to predict stuck pipe occurrences with a reasonable accuracy which is over 83%. It has been shown in this study that the competitive SVM technique is able to generate promising and reasonable results of stuck pipe prediction. Besides, it can be found that SVMs are more convenient than ANNs since they need one or two parameters at most to be optimized. The constructed models generally apply very well in the areas for which they are built but may not work for other areas. However, they are important especially when it comes to probability measures. Thus, they can be utilized with real-time data and would represent the results on a log viewer.