Prediction of drilling pipe sticking by active learning method (ALM)

Abstract

Stuck piping is a common problem with tremendous impact on drilling efficiency and costs in oil industry. Generally, the stuck pipe troubles are solved after their occurrences by using some standard techniques; here we attempt to predict the causes of occurrence of such problems to eschew risks and excessive drilling costs. If these risks are identified in advance, better solutions can be provided to reduce the associated consequences. Based on the literature, this problem is caused by numerous parameters, such as drilling fluid properties and the characteristics of the mud cake that is formed while drilling. In this study, an attempt is made to develop a model for stuck pipe prediction. To consider all aspects of pipe sticking and behavior of the involved variables, the fuzzy logic and active learning method (ALM) can be used as a primary predictive tool. Active Learning Method is a robust recursive fuzzy modeling without computational complexity. These methods are broadly used in many industries; including oil and gas. This paper proposes a systematic approach for pipe stuck prediction based on ALM. The results of this method are more accurate than other methods and prediction accuracy is close to perfect either in stuck or non-stuck cases. This study presents a case study in which the ALM is used successfully to estimate pipe sticking. Thus, the proposed method possesses reliable results for prediction of pipe stuck, and can be used in order to minimize the risk of pipe sticking.