Stuck Pipe Detection in Geothermal Operation with Support Vector Machine

S. Sarwono, Lukáš, M. Kartawidjaja, R. Wardana
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Abstract

One of the biggest problems during drilling operation is a stuck pipe in which the drill string would stick or freeze in the well. This challenge leads to a significant amount of remedial costs and time. Many researchers have investigated different factors regarding the stuck pipe. These factors include poor hole cleaning, improper mud design, key seating, balling up of bit, accumulation of cutting and caving, poor bottom hole assembly configuration, differential pressure, etc. (Chamkalani, Pordel Shahri, and Poordad 2013). Since geothermal drilling targets lost circulation zones at reservoir depth, the chance of getting stuck pipe events becomes higher. Many publications reported that lost circulation events that lead to stuck pipe events have become the top non-productive time (NPT) contributor to costs in many geothermal drilling projects. The consequences of a stuck pipe are very costly, that include lost time when releasing the pipe, time and cost of fishing out the parted Bottom Hole Assembly (BHA), and efforts to abandon the tool(s) in the hole. Despite many observations that have been done to develop a system in avoiding stuck pipe incidents in oil and gas drilling operations using artificial intelligence (AI), few works have been developed for geothermal drilling operations. In this research, we propose a method to build an early warning system model for stuck pipe conditions based on a Support Vector Machine. Based on the experiment result Support Vector Machine Algorithm showed good performance with 89% accuracy and 81% recall for limited training dataset.
基于支持向量机的地热作业卡管检测
钻井作业中最大的问题之一是钻杆卡钻,钻柱会卡在井中或冻结。这一挑战导致了大量的补救成本和时间。许多研究人员研究了与卡管有关的不同因素。这些因素包括井眼清洁不良、泥浆设计不当、关键座垫、钻头成球、切削和落斜堆积、底部钻具组合配置不良、压差等(Chamkalani, Pordel Shahri, and Poordad 2013)。由于地热钻井的目标是储层深度的失循环区,因此发生卡钻事故的可能性更高。许多出版物报道,在许多地热钻井项目中,导致卡钻的漏失事件已成为造成非生产时间(NPT)成本最高的因素。卡钻的后果是非常昂贵的,包括释放管柱时的时间损失、打捞分离的底部钻具组合(BHA)的时间和成本,以及在井中放弃工具的努力。尽管已经进行了许多观察,以开发一种利用人工智能(AI)避免油气钻井作业中卡管事故的系统,但用于地热钻井作业的工作却很少。在本研究中,我们提出了一种基于支持向量机的卡钻工况预警系统模型的构建方法。实验结果表明,在有限的训练数据集上,支持向量机算法具有89%的准确率和81%的召回率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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10
审稿时长
8 weeks
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