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Novel Signatures from Deployed Sensors for Natural Gas Transmission Pipelines
Project Number
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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.

Accomplishments (most recent listed first)
  • Enhanced machine learning (ML) analytical methods by exploring applicability to hazardous liquid data.
  • Identified the technical basis for using the ML methods for assessing corrosion in pipelines carrying gas and liquid.
  • Shared draft data requirements with industry, improved the data requirements based on industry feedback.
  • Performed statistical analysis and machine learning on Pipeline and Hazardous Materials Safety Administration pipeline incident data. 
  • Conducted a review of existing ML methods, identified data associated with the methods, developed draft data requirements and engaged with potential partners and the sponsor to explore collaboration opportunities for evaluating data requirements and identifying data access for project use. 
Current Status

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. 

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DOE Contribution


Performer Contribution


Contact Information

Federal Project Manager –  Eric Smistad ( or 281-494-2619)
HQ Program Manager –  Christopher Freitas ( or 202-586-1657)
Principal Investigator –  Casie Davidson  ( or 509-372-6259)
Team Supervisor – John Duda (
Technology Manager – Jared Ciferno ( or 412-386-5862)