Nearly one third of the drilling time lost is caused by stuck drill pipe. In many cases, stuck pipe is preventable if early signs are detected and timely measures are taken, particularly for stuck pipe events caused by solids (primarily drilled cuttings) in the wellbore. This paper presents a physics-based model and data analytics combined approach to predict stuck pipe caused during drilling.

The new proposed method combines the physics-based first principle models, including transient solid transport model, drill string model (torque and drag model) and the data-driven models. The proposed models will be worked based on the analysis of both field and experimental data. The physics-based models capture the basic rules of fluid mechanics, drill string mechanics, and multiphase flow during the drilling operations.

By analyzing field data from historical wells and experimental data, an EnKF based data-driven model is applied to provide parameters and coefficients needed by the physics-based models. The data-driven model improves the reliability of the results predicted by the first-principle based model and allows it to continuously improve itself.

Based on a transient approach, this development can use real-time drilling operational data as inputs, predict the stuck pipe risks, and provide warnings when a high risk for stuck pipe scenarios are encountered. Comparing to existing stuck pipe prediction approaches, the new proposed approach can distinguish the hole cleaning related stuck pipe risk and other reasons to create stuck pipe. This hybrid method in build technology will use as a supporting tool in decision make. Resulting to bring an opportunity for the drillers to avoid the potential stuck pipe incidents by taking a proper action in time.

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