Implementing artificial neural networks and support vector machines in stuck pipe prediction

Islam Al-Baiyat, Lloyd Heinze

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

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 by 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, one database that included mud properties, directional characteristics, and drilling parameters has been assembled for training and testing processes. The study involved classifying stuck pipe incidents into two groups - stuck and non-stuck - and also into three subgroups: differentially stuck, mechanically stuck, and non-stuck. This research has also gone through an optimization process which is vital in machine learning techniques to construct the most practical models. This study demonstrated that both ANNs and SVMs are able to predict stuck pipe occurrences with reasonable accuracy, over 85%. The competitive SVM technique is able to generate generally reliable stuck pipe prediction. Besides, it can be found that SVMs are more convenient than ANNs since they need fewer parameters 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.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - Kuwait International Petroleum Conference and Exhibition 2012, KIPCE 2012
Subtitle of host publicationPeople and Innovative Technologies to Unleash Challenging Hydrocarbon Resources
Pages880-892
Number of pages13
StatePublished - 2012
EventKuwait International Petroleum Conference and Exhibition 2012: People and Innovative Technologies to Unleash Challenging Hydrocarbon Resources, KIPCE 2012 - Kuwait City, Kuwait
Duration: Dec 10 2012Dec 12 2012

Publication series

NameSociety of Petroleum Engineers - Kuwait International Petroleum Conference and Exhibition 2012, KIPCE 2012: People and Innovative Technologies to Unleash Challenging Hydrocarbon Resources
Volume2

Conference

ConferenceKuwait International Petroleum Conference and Exhibition 2012: People and Innovative Technologies to Unleash Challenging Hydrocarbon Resources, KIPCE 2012
CountryKuwait
CityKuwait City
Period12/10/1212/12/12

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    Al-Baiyat, I., & Heinze, L. (2012). Implementing artificial neural networks and support vector machines in stuck pipe prediction. In Society of Petroleum Engineers - Kuwait International Petroleum Conference and Exhibition 2012, KIPCE 2012: People and Innovative Technologies to Unleash Challenging Hydrocarbon Resources (pp. 880-892). (Society of Petroleum Engineers - Kuwait International Petroleum Conference and Exhibition 2012, KIPCE 2012: People and Innovative Technologies to Unleash Challenging Hydrocarbon Resources; Vol. 2).