使用机器学习防止卡管和紧孔事件的步骤变化

Salah Bahlany, Mohammed Maharbi, Saud Zakwani, F. Busaidi, Ferrante Benvenuti
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摘要

井眼稳定性问题,如卡钻和紧点,是影响钻井作业的最关键风险之一。多年来,中东地区的油气运营商一直面临着卡钻和紧点事件等问题,这些问题对钻井效率、钻井成本和钻井作业的碳足迹都有重大影响。平均而言,作业者每年因卡钻和相关打捞作业而损失200天(非生产时间)。由于钻井作业条件的变化,如岩性、钻井参数、压力、设备、轮班人员和多井设计等,井筒稳定性问题很难预测。所有这些因素使得仅通过人为干预很难减轻卡管的发生。出于这个原因,作业者决定开发一种人工智能工具,利用作业者数据的广度和深度(报告、传感器数据、井工程数据、岩性数据等)来预测和防止井筒稳定性问题。该工具可以告知工程师和钻井人员在井计划和井执行阶段可能存在的风险,并建议可能的缓解措施,以避免卡钻。由于警报是在钻头之前发出的,在可能发生事故的几个小时之前,井工程师和钻机人员有足够的时间对警报做出反应并防止事故发生。到目前为止,该工具已经在38口井中进行了试验,发出了44次真实警报,召回率为94%。自2021年年中以来,运营商已经将该工具扩展到整个钻井作业(超过40台钻机)。
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STEP Change in Preventing Stuck Pipe and Tight Hole Events Using Machine Learning
Wellbore stability problems, such as stuck pipe and tight spots, are one of the most critical risks that impact drilling operations. Over several years, Oil and Gas Operator in Middle East has been facing problems associated with stuck pipe and tight spot events, which have a major impact on drilling efficiency, well cost, and the carbon footprint of drilling operations. On average, the operator loses 200 days a year (Non-Productive Time) on stuck pipe and associated fishing operations. Wellbore stability problems are hard to predict due to the varying conditions of drilling operations: different lithology, drilling parameters, pressures, equipment, shifting crews, and multiple well designs. All these factors make the occurrence of a stuck pipe quite hard to mitigate only through human intervention. For this reason, The operator decided to develop an artificial intelligence tool that leverages the whole breadth and depth of operator data (reports, sensor data, well engineering data, lithology data, etc.) in order to predict and prevent wellbore stability problems. The tool informs well engineers and rig crews about possible risks both during the well planning and well execution phase, suggesting possible mitigation actions to avoid getting stuck. Since the alarms are given ahead of the bit, several hours before the possible occurrence of the event, the well engineers and rig crews have ample time to react to the alarms and prevent its occurrence. So far, the tool has been deployed in a pilot phase on 38 wells giving 44 true alarms with a recall of 94%. Since mid-2021 operator has been rolling out the tool scaling to the whole drilling operations (over 40 rigs).
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