Abstract

Summary Production-liner cementation in deepwater wells is critical to contain formation pressures and provide zonal isolation. This is especially challenging in the Gulf of Mexico (GOM) because of depleted zones and small drilling margins. Failure to achieve zonal isolation with the primary-cement job leads to added cost and time for remedial operations. A recent primary-cement-job failure in the GOM prompted the use of an established tool, root-cause-failure analysis (RCFA), in a novel way to identify the causes of the incident and avoid repeating the same mistakes in future wells. A team of operations and engineering personnel was assembled to investigate the cause of the primary-cement-job failure that resulted in nonproductive time and cost of approximately 7 days. RCFAs have typically been performed on equipment failures; however, this RCFA was conducted by the operator on a nonequipment-related failure. The RCFA team initially collected data available from both the planning and operation phases of the job to identify which elements of the cementing process contributed to the cement-job failure. After identifying the root cause, the team communicated their learnings to office and rig-based personnel; developed a new tool for real-time monitoring of primary cementation; and adjusted plans for upcoming primary-cement jobs. The findings from the RCFA were applied to a well on another asset owned by the operator, with which the production liner was successfully rotated throughout cementation for the first time. The RCFA identified that the cement-job design was not a contributing factor; instead, the root causes were operational and started during the circulation of drilling fluid, before the cementing operations. The learnings from the RCFA were transferred to both office and rig-based personnel, influencing engineering and operations plans for subsequent wells. Furthermore, the investigation identified previously unknown issues and showed that the use of the RCFA process was an effective approach that can be standardized and scaled to determine root causes of future operational failures.

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