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A new coiled tubing (CT) pipe pull test placement optimizer and automation solution is developed and integrated into an autonomous CT unit. This method provides consistency in strategic and automated pull test placement to extend the life of the pipe and consequently reduce total cost of ownership and greenhouse (GHG) emissions. A case study in the North Sea showcases the solution in real-time operations and simulated cases demonstrate the potential impact. Pull tests are a common means of mitigating stuck pipe in CT operations. Traditionally, they are executed at fixed intervals while the CT is run in hole. Risks associated with this rote approach include repeated pull tests at an already fatigued section, which accelerates fatigue of the CT pipe, and pull tests executed in wellbore sections with completion restrictions. The new solution addresses those risks by systematically combining the pipe's fatigue profile, weld points, and completion information to strategically adjust the pull test schedule, reducing the impact of those tests on pipe fatigue and the risks associated with drifting across downhole completion jewelry. The pull test automation and optimization are integrated into an automation-enabled CT unit in the North Sea. The automation eliminates the human factor and the risk of repeating pull tests at the same locations on each run; it also minimizes pull tests at the weakest points, which include the weld points where cycle fatigue is accelerated, and it eliminates pull tests at restrictions, reducing the risk of abrading or damaging completion jewelry. Finally, besides standardizing pull test placement, the combination of the pull test optimization and automation frees the CT operator to focus on other critical elements of the operation such as running and monitoring the unit and downhole tools, managing the crew, and engaging the customer representative. The performance of the pull test automation and optimization solution is tested in a simulated environment to quantify the impact on the CT pipe from new (0% fatigue) to end of life (EOL, 100% fatigue). The simulation uses a CT pipe with 1 3/4-in. outer diameter (OD) and a mission profile from a previous intervention campaign. CT of this OD is common in CT operations worldwide, making it an interesting test subject for the analysis. As compared to the traditional pull test approach, the optimized pull test placement redistributes the fatigue to less-fatigued sections of the pipe and extends the useful life of the pipe from 137 runs to EOL to 176 runs to EOL. Extending the useful life by 28% is equivalent to reducing the pipe's run-on-run cost and GHG manufacturing emission contributions by 22%.
A new coiled tubing (CT) pipe pull test placement optimizer and automation solution is developed and integrated into an autonomous CT unit. This method provides consistency in strategic and automated pull test placement to extend the life of the pipe and consequently reduce total cost of ownership and greenhouse (GHG) emissions. A case study in the North Sea showcases the solution in real-time operations and simulated cases demonstrate the potential impact. Pull tests are a common means of mitigating stuck pipe in CT operations. Traditionally, they are executed at fixed intervals while the CT is run in hole. Risks associated with this rote approach include repeated pull tests at an already fatigued section, which accelerates fatigue of the CT pipe, and pull tests executed in wellbore sections with completion restrictions. The new solution addresses those risks by systematically combining the pipe's fatigue profile, weld points, and completion information to strategically adjust the pull test schedule, reducing the impact of those tests on pipe fatigue and the risks associated with drifting across downhole completion jewelry. The pull test automation and optimization are integrated into an automation-enabled CT unit in the North Sea. The automation eliminates the human factor and the risk of repeating pull tests at the same locations on each run; it also minimizes pull tests at the weakest points, which include the weld points where cycle fatigue is accelerated, and it eliminates pull tests at restrictions, reducing the risk of abrading or damaging completion jewelry. Finally, besides standardizing pull test placement, the combination of the pull test optimization and automation frees the CT operator to focus on other critical elements of the operation such as running and monitoring the unit and downhole tools, managing the crew, and engaging the customer representative. The performance of the pull test automation and optimization solution is tested in a simulated environment to quantify the impact on the CT pipe from new (0% fatigue) to end of life (EOL, 100% fatigue). The simulation uses a CT pipe with 1 3/4-in. outer diameter (OD) and a mission profile from a previous intervention campaign. CT of this OD is common in CT operations worldwide, making it an interesting test subject for the analysis. As compared to the traditional pull test approach, the optimized pull test placement redistributes the fatigue to less-fatigued sections of the pipe and extends the useful life of the pipe from 137 runs to EOL to 176 runs to EOL. Extending the useful life by 28% is equivalent to reducing the pipe's run-on-run cost and GHG manufacturing emission contributions by 22%.
Each year, coiled tubing (CT) well intervention fleets produce terabytes of multimodal data; these are recorded from surface and downhole sensors on each job. Among these data are job type(s) and technologies used on each job; traditionally, a field crew manually supplies this information. Given the diversity of data, many acquisition labels are often missing or inaccurate. A multimodal framework is presented that automatically identifies the job type and technologies used during an acquisition. The proposed framework leverages different types of data depending on the job and technology to identify. Most job types (e.g., CT milling, CT fishing), and general technologies (e.g., downhole telemetry, jetting nozzles), are identified through a natural language processing (NLP) algorithm applied to operational reports. The presence of downhole technologies with greater granularity and certainty (e.g., downhole 2 1/8-in. pressure and temperature sonde) lies in the detection of meaningful information or noise on specific channels and follows a logic mimicking human interpretation. Finally, CT cementing and electronic firing heads are identified through statistical metrics and pattern recognition. The framework leverages several tested methods. Primary job types and general technologies are classified using NLP. Of 366 acquisitions in the cloud archival system, the algorithm labels 97% with job type and 26% with one or more technologies. The second method extends the resolution and number of detected technologies to cover 12 unique real-time telemetry modules such as pressure, temperature, casing collar locator, and gamma ray sondes. For this method, over 50 acquisitions are analyzed with an accuracy of 94%. Electronic firing head signatures for three unique types of firing heads are identified successfully 97% of the time on 44 different acquisitions. This identification is sped up by automatically identifying three major stages of a CT well intervention (i.e., initial run in hole, service delivery, and final pull out of hole) and restricting the search space to the relevant stage. CT cementing job type identification is tested on 52 different jobs, with an accuracy of 92% and less than 30% of false negatives. The innovative framework automatically classifies jobs and technologies. The inherent methods combine domain knowledge with the power of machine learning to enable efficient mining of data that would otherwise remain out of reach. By automating labeling, human error is largely eliminated and the reliability of the contextual metadata is significantly improved to provide crucial insights regarding operations.
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