Data-Driven Approaches To Pipeline Cleaning
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Recent years have seen the emergence of data-driven approaches in a wide range of industries. The pipeline industry is no exception. All too often, no data is captured on cleaning run conditions with regards to type, volume, or nature of the debris removed during the process. This means that operators may be missing tangible information regarding pipeline conditions that could provide guidance on whether an in-line inspection can be conducted smoothly, or if the cleaning program is effective. This may result in uncertainties and increased risks for the efficient transportation of products and operational cleaning or inspection tool runs. This paper illustrates an approach to specifically address these issues, providing pipeline operators with the opportunity to build up a database of information on their assets from standard cleaning runs. When applied to consecutive runs, such an approach enables the systematic build-up of knowledge about a pipeline’s development over time. A wide range of analytics can be brought to bear upon these databases. Proactive maintenance requires collection and management of data from cleaning programs for future use. Analysis and expert interpretation of this data will ultimately benefit any additional process by offering more information from the beginning, and potentially decreasing the workload of in-line inspections. In a system that can operate in near real-time, status alerts can be provided to give operators critical feedback such as when there is a tool in the line, when a cleaning run was successfully completed, or when a specific problem has occurred. The resulting knowledge of the pipeline conditions offers a greater degree of confidence that a line is ready for further in-line inspection, ultimately increasing first-run success rates while reducing risk.
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