Paper 1

Third-party interference is widely documented as being a major cause of damage to buried pipelines. In addition to routine surveillance, maintaining a minimum depth of cover is recognized as a key means of mitigation against third-party interference. We know that the depth of cover over pipelines can change with time. Current techniques available for measuring depth of cover on buried pipes require significant effort to produce a high-resolution survey for an entire pipeline.

A UK Innovation project completed for National Grid Gas Transmission has successfully demonstrated a methodology to identify reduced depth of cover over an entire pipeline. This methodology combines ground elevation data with high-resolution inertial measurement unit (IMU) data collected during inline inspection to calculate the pipeline depth of cover.

GPS and pipe depth measurements have been used to verify the accuracy of this method. Using the pipe centerline derived from the IMU data, and ground elevation data collected using light detection and ranging (LiDAR) techniques, depth of cover has been calculated to an accuracy of ±0.149 m root mean square error.

This paper describes  the key project steps associated with planning, data collection, data processing, and the validation of results to demonstrate that pipeline depth of cover over an entire pipeline can be accurately determined using the proposed approach

Paper 2

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.

Paper 3

How pipeline operators benefit from the highest resolution available in inline inspection.
Every pipeline operator is looking for a means to improve on OPEX in order to streamline his operations to the varying oil & gas market. To this end, less conservative assessment approaches for corroded pipelines, detecting even the smallest pinhole features, are an optimum solution. Such approaches provide detection and assessment precision that will ultimately save cost by minimizing field verifications while ensuring maximum pipeline safety.
With RoCorr MFL-A Ultra, ROSEN has made a solution available that features

• a circumferential MFL-A resolution of 1.6mm, matching the resolution of state-of-the-art in-the-ditch validation equipment such as Matrix UT and 3D Laser scanners;
• ILI tools with twin-suspension sensor housings and a double MFL sensor ring, eliminating the ‘blind spot’ around girth welds while running in common MFL operating conditions; and
• machine-learning algorithms based on 3D contours of real defects, delivering unparalleled feature-detection/sizing capabilities and reducing ‘human factor’ impact

Using data examples from recent pipeline inspections and subsequent field verifications, the authors of this paper demonstrate how the RoCorr MFL-A Ultra service is instrumental in the assessment of

• small pinhole features,
• complex corrosion, and
• girth-weld related defects

by delivering more realistic ERF calculations with the goal of reducing field verification costs.