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Abstract Mechanical pipe cutters are an increasingly attractive option for intervention and plug and abandonment (P&A) campaigns due to their simplicity, ease of deployment, and reduced risk of stored energy inherent in traditional energized pipe cutters. Limitations of current mechanical cutter designs include small expansion ratios, challenges to cut pipe under compressive load, and limited surface readouts that require expertise to interpret. A mechanical tubing cutter has been developed that seeks to address these challenges and limitations. The robust mechanical design in the new cutter features a high expansion ratio with the ability to cut in tension, neutral weight, or compression, including multiple cuts per descent. Critical surface readouts have been incorporated to provide a fuller understanding of the cut and well response. Fully automated cutting algorithms, which minimize user interaction, have been programmed into the service to provide an active, torque-controlled cut that optimizes penetration and delivers consistency in performance. The cutter is designed as a modular addition to a full intervention solution family to offer combinable, multiobjective descents with a light wellsite footprint. This new cutter has been deployed successfully by several operators to enhance well production and to successfully execute P&A scopes where other solutions were unfavorable or financially untenable. Two case studies illustrate the application of features such as the downhole sensors, automated algorithms, and expansion ratio and show how these features are central to achieving a successful cut. This tubing cutter design expands the utility of mechanical cutters to target pipe that was previously inaccessible due to restrictions or unforeseen tubing deformation. In addition, the cutter reduces the risk of cutting complications stemming from compressive and torsional forces acting on downhole targets. Downhole automation enhances cutting reliability through reactive torque control, programmed stall recovery, and real-time response to changes in cutting conditions.
Cygan et al. (Tue,) studied this question.
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