The goal of this project is to leverage advances in machine learning and predictive analytics to advance the state of the art in pipeline infrastructure integrity management using forecasted (predictive) pipeline condition, using large sets of pipeline integrity data (periodic nondestructive inspection, NDI) and continuous operational data (e.g., sensor data used to monitor flow rate and temperature) generated by oil and gas (O&G) transmission pipeline operators.
Pacific Northwest National Laboratory (PNNL)
Natural gas and oil pipeline networks are critical infrastructures whose reliability is essential to sustaining energy sector operations and the U.S. economy. Establishing the reliability of transmission pipelines requires NDI after fabrication of the piping, and periodic inspection during operation. A range of existing and emerging sensor and instrumentation technologies are available for operational control, integrity assessment, and mitigation responses to off-normal events. At the same time, new sensors are being designed and proposed for many of the same measurement needs as well as for monitoring other parameters that could support and enhance operational decisions.
NDI techniques are typically used to detect the presence of degradation such as axial cracking, mechanical damage, or corrosion in oil and gas transmission pipelines. Typically, the detection of degradation triggers other analysis techniques, often based on structural mechanics, for assessing the structural integrity of pipelines. NDI techniques that have been applied for pipeline integrity assessment include magnetic flux leakage and ultrasound, both of which are used for in-line and offshore piping. Alternative UT (AUT) methods have been applied for inspecting girth welds in onshore and offshore piping. Alternative inspection methods include localized inspection techniques using visual, ultrasonic, guided wave ultrasound, eddy current, or enhanced visual inspection (such as dye penetrant inspection and magnetic particle inspection); however, these techniques often require physical access to the surface of the pipeline. Recent advancements have also resulted in 3D scanners for the rapid inspection of the external surface of the pipeline. Again, these require access to the surface of the pipe. Physical access may be a challenge if the pipe is buried or otherwise jacketed. Alternative emission techniques, visual observations, thermal imaging, chemical sensing, or process monitoring. Millimeter-wave and spectroscopic methods have also been proposed for detecting the presence of gaseous vapor due to a leak; however, these techniques are still in various stages of research and not commercially available.
The analytics to be developed will enable earlier detection of degradation in operating pipelines to support decisions about where to focus inspections, enhance situational awareness about the scope of inspections to be performed, allow monitor trending between inspections, and inform the timing of preventative maintenance.
Performing peer review of draft industry outreach information packet. The information packet covers data requirements, the hybrid data-driven physics model of pipeline corrosion to support risk analysis and prognostics, and the implementation of technology transfer options. In addition, bilateral partnerships with major natural gas pipeline operators and pipeline inspection service providers are being pursued.
Federal Project Manager – Eric Smistad (firstname.lastname@example.org or 281-494-2619)
HQ Program Manager – Christopher Freitas (email@example.com or 202-586-1657)
Principal Investigator – Casie Davidson (Casie.Davidson@pnnl.gov or 509-372-6259)
Team Supervisor – John Duda (firstname.lastname@example.org)
Technology Manager – Jared Ciferno (email@example.com or 412-386-5862)