STUCK PIPE MITIGATION: FINDINGS, ANALYSIS, AND MACHINE LEARNING SOLUTIONS

Abstract

The occurrence of stuck pipes during drilling presents significant challenges, leading to nonproductive time (NPT) and potential damage to equipment. This paper delves into the reasons behind, effects of, and methods for addressing incidents of stuck pipes in drilling actions. Extensive literature review and analysis reveal the prevalence of stuck pipe events and their diverse underlying causes, ranging from differential sticking to mechanical jamming. Additionally, the study identifies key drilling parameters and signals indicative of potential stuck pipe incidents, emphasizing the importance of early detection and intervention to prevent adverse outcomes. Proposing the utilization of machine learning algorithms and statistical models for predictive analytics as a proactive strategy to mitigate the risk of stuck pipe incidents. The paper discusses the limitations of traditional statistical models and highlights the efficacy of machine learning methods, such as artificial neural networks (ANNs) and support vector machines (SVMs), in accurately modeling drilling processes. Furthermore, practical machine learning models using real-time drilling data are presented as effective tools for early detection and timely intervention in stuck pipe incidents. The study also describes various software tools used to assess and mitigate risks in drilling operations. Overall, this paper presents prospects for improving operational efficiency and safety of drilling operations using advanced analytical methods and software solutions. Keywords: stuck pipe, type of stuck, drillstring immobilization, nonproductive time, machine learning in drilling, risk analysis software.