PR-670-183826-R03 Extended Evaluation of LSM-Magnetostrictive Pipe Models
Integrity assessment of pipelines is vital to ensure that oil and gas pipes have adequate strength to prevent leaks and ruptures. Regular inspections are conducted to confirm safe operation conditions of pipelines. The industry's principal method for assessing pipelines is in-line inspection (ILI), involving the passing of a device along the inside of a pipe to assess the condition of the pipeline. ILI devices can be fitted with a number of sensors allowing various measurement parameters to be obtained simultaneously.
Not all pipelines are suited to ILI for reasons such as small diameter, obstructions within the pipe, or insufficient access to deploy or retrieve the ILI tools. These pipelines are sometimes referred to as 'difficult to inspect'. Alternative methods for examining pipeline condition are required with a range of technologies collectively known as Large Standoff Magnetometry (LSM) offering a promising solution for detection of pipe defects from a distance, reducing the need for excavation.
LSM utilizes coupling between defects and changes in the magnetic properties of the pipeline material as a method for evaluation of pipe walls to identify the location of areas that require repair or further monitoring. Trials of existing commercial instruments by the pipeline industry have shown sufficient promise to investigate these technologies further. However, the vendors have supplied limited information on the underpinning physics of both the materials being tested and the instrument technology, meaning that further study is required in order to build confidence in the technique.
The purpose of the project is to establish the ability of LSM to detect corrosion in API 5L pipe grades B to X70. The aim of the modelling work has been to use quantitative physics-based modelling techniques to predict the performance of LSM as a defect detection technique in situations that cannot easily be recreated in a laboratory environment, after validating the modelling approach by comparing measurements made in the laboratory to model results.