The goal of this project is to develop three technology platforms which will be combined as a system to provide remote monitoring of natural gas pipeline conditions, and early detection of factors leading to a potential for unintended methane release. The system components consist of an organic coating formulation intended for interior pipeline application, embedded RF sensors with resonant frequencies correlated to changes in the sensor environment, and in-line interrogation equipment capable of coupling with the sensors and communicating relevant information.
PPG Industries, Inc., Allison Park, PA 15101
Research Triangle Institute, Research Triangle Park, NC 27709
Gas Technology Institute, Des Plaines, IL 60018
A recent paper published in the journal of Science, "Methane Leakage from North American Natural Gas Systems" presented alarming evidence that natural gas emissions from the Nation’s pipeline system are about 50% higher than existing EPA estimates. Given that methane as a greenhouse gas is as much as 30 times more efficient than carbon dioxide, release mitigation is an important issue to address. The authors conclude that “if natural gas is to be a “bridge” to a more sustainable energy future, it is a bridge that must be traversed carefully: Diligence will be required to ensure that leakage rates are low enough to achieve sustainability goals.”
A major part of the problem is the sparsity of data on the conditions of the pipe/coating/soil system. The operator knowledge of these conditions is based on methods that are expensive to apply and provide limited views of the actual conditions. In-line-inspection (ILI) tools can provide indications of internal corrosion, wall loss, and other issues over the length of the pipe. However, ILI tools are run at long intervals, sometime measured in years. External Direct Assessment (DA) can provide excellent data on pipe and coating conditions, and with ultrasonic thickness gauging, can provide some insight into the pipe interior. Again, DA is performed at intervals and are limited to the physical extent of an excavation. The “snapshot” nature of these techniques necessitate that operators extrapolate their data of the pipe over time and location. This has an impact on the confidence level of predictions. The ability to capture some history of pipe parameters over its spatial extent would improve predictions and expose leading indicators of problems while they are developing.
A common trait for many of the available inspection techniques is that leaks are detected only after they occur. Accordingly, a need exists to obtain insights into the condition of the pipeline or natural gas composition prior to a leak occurring. Data providing evidence that conditions within the pipeline pose the potential for a future leak or pipe failure would enable pinpointing of areas to examine more closely and remedial actions to be taken before a methane release or catastrophic failure. Case in point is a very recent pipeline explosion near Salem, PA on April 30th, 2016. A 30 inch transmission line burst shortly after daybreak, shooting an enormous plume of flames into the sky that could be seen for miles. Fortunately, personal injuries and property damage were limited but out of caution additional methane releases where ordered from 4 nearby lines. Most telling is that the pipeline, which was built in 1981, was inspected in 2012 and found to have “no areas requiring repair or remediation before the next inspection.” The next inspection wasn’t required until 2019 when the explosion occurred.
The project team intends to combine sensors, communications, and coatings to enable the monitoring of conditions inside a pipeline. Although the elements required for technical success have been well established in different fields, this project represents an opportunity to bring a diverse range of technologies together to advance our knowledge of conditions within a pipeline. Such knowledge enables more informed decisions about the quality of the gas stream and potential for equipment failures, thus leading to overall reductions in methane release and allowing for required remedial actions to occur before an uncontrolled methane release. The technology innovations envisioned in this project are aimed at multi- modal sensors with an initial focus on the measurement of sensor resonance frequency changes which result from changes in moisture content, temperature and/or dielectric properties of the coating. The relative scale of resonance frequency change will be used to infer on-going coating environmental changes and coating degradation processes and be indicative of the onset of corrosion in the pipeline. Water content information informs utilities about the quality of gas within the pipe but also can be a leading indicator for corrosion activity within the pipe. To achieve project goals the assembled team will build on previous work initiated by PPG by conducting detailed evaluations of RF sensor devices embedded in coatings which have been exposed to a variety of environmental challenges. These evaluations will be conducted using lab scale coated coupons but also larger scale internal pipe coating demonstrations wherein the sensors are embedded within coatings applied to the interior surface of a 24 inch pipe section. The pipe section will be mated to longer pipe lengths to provide an accurate simulation of performance within a pipeline environment.
While extensive diagnostics of the natural gas is the eventual goal, this project focuses on a few measures within the pipe; dielectric changes in the coating, moisture absorbed by the coating from the natural gas stream and temperature. Follow on projects with either public or private funding may be considered which could address additional measures such as vibration. Likewise, remote controlled, in-line interrogation robots will eventually examine these sensors, collect data and communicate conditions within the pipe. However, within the scope of this project the assembled team will perform demonstration within a representative pipe diameter using a tethered interrogation device rather than an autonomous robot.
Research to be performed is divided into seven major tasks that shall be conducted over a three year period and that are designed to achieve the research objectives in the most efficient and cost‐effective way. These include:
The primary benefit of the proposed research is an improvement in operator knowledge of conditions on their systems. The ability to query sensors that are permanently mounted within the pipeline allows the history of pipe conditions to be captured. The other prevalent methods, in-line-inspection tools and external direct assessment, provide limited “snapshots” of pipe conditions.
ILI tools can only record the conditions at the time the run is made. Cleaning pigs are often run in advance of ILI tools and may remove moisture, hydrocarbons, and corrosion products that are early warnings of problems. The ILI tool may only have a view of issues that are advanced enough to not be disturbed by cleaning. Likewise, external direct inspection can give very precise data of conditions but are spatially limited to a single excavation. The operator assumes the data from a DA excavation is typical and extrapolates it over their system. This assumes a level of uniformity in the pipe/coating/soil system over some spatial extent.
The ability of embedded sensors in the coating to capture some parameters of the pipe conditions over time will allow operators to overcome the time dependency of ILI methods. Distributing these sensors over the length of a pipeline overcomes the spatial limitations of DA methods. The intended result of this effort is that having access to this greater span of data will expose leading indicators of problems that are developing more readily than current practices.
The project was awarded October 1, 2016 and is now underway.
Current efforts are focused on selection of a wide range of coating chemistries for initial benchmarking of sensor performance within different coating matrices. The coating formulations will become progressively more complex to incorporate pigments, additives and corrosion inhibitors to assess formulation factors affecting sensor performance. Correlation of sensor response with environmental factors such as moisture ingress and pipeline corrosion will be an important Phase 1 deliverable.