Casing Run Failure Avoidance Through Automated Assessment of Borehole Quality

Summary Traditionally, evaluating a borehole’s readiness to accept casing has relied on a manual, limited, and often biased analysis of an insufficient amount of data. This approach frequently results in casing run failures, leading to high nonproductive time and cost. In this paper, we propose a digital tool to automate this analysis, providing the drilling team with a comprehensive, timely evaluation of the risk of casing run failure and an interpretation of that risk. The tool developed discretizes the hole section under analysis into small intervals. It then utilizes both time-series data (preferably 1 Hz) and contextual data to derive features associated with casing runnability for each interval. These features include the potential for cuttings accumulation, the presence of borehole undulations or excessive hole curvature, differences in rigidity between the casing and drillstring, and other factors further described in this paper. The tool then uses three models. The first model determines whether the casing run is likely to succeed; the second determines whether each interval is likely to cause severe restrictions during the casing run; and the third explains the predictions of the second model. The tool was constructed with data from 52 hole sections from deepwater wells in the Gulf of Mexico/America (GOM) and subsequently tested on five additional sections, comparing the actual casing runs with the tool’s predictions. This test revealed that the model not only provides an accurate assessment of the risk of casing run failure—capturing actual risky intervals while avoiding spurious predictions—but also offers a meaningful interpretation of this risk. It identifies the location of high-risk intervals along the wellbore and the potential causes of such risks. Implementing this new interpretable tool can help reduce the frequency of casing run failures and the associated costs. Additionally, it can help avoid risks in subsequent hole sections caused by setting the casing above the planned depth, like the risk of getting stuck in the rathole or drilling the next section with a reduced kick tolerance. Ultimately, the drilling team is enabled to make better-informed decisions before and during the casing run. They can decide whether a conditioning trip is required and/or whether to outfit the casing with reaming/drilling features. Furthermore, they can avoid inefficiencies caused by implementing overly conservative measures, like superfluous hole conditioning trips. The novelty of this tool is twofold. First, it is the first tool that can indicate the location of risk along the wellbore in addition to providing a risk evaluation. Second, it integrates data-driven and physics-based models, in a hybrid approach implemented only to a limited extent in past studies. This tool represents a significant advancement over the traditional approach, not only in the volume of data it processes but also in its accuracy and interpretability.