Pipeline network forms the backbone of oil and gas industry by providing the infrastructure for transporting hydrocarbons and, in some cases, water. Its integrity is a must to maintain to ensure safe and smooth operation, and avoid downtimes in case of disastrous events. Following this line, it is required to inspect the pipelines periodically to know their condition and make timely decisions for replacement or repair. One of the common problems faced in a pipeline network and targeted by this research is internal coating disbondment, whereby the internal protective coating of a pipeline is damaged. This type of defect is a precursor for corrosion of pipe wall material. Current industry practice is to replace entire set of pipelines or its large segment, due to the lack of knowledge of specific location of disbondment. Inspection methods used in industry employ non-destructive testing (NDT) of pipelines to find corrosion and other defects. One of the technologies used for NDT is electromagnetic acoustic transducer (EMAT), whereby the magnetic sensors produce electromagnetic waves to generate Lorentz forces resulting in guided acoustic waves in pipelines metallic wall. These acoustic waves are echoed and sensed to give information about the damage in pipeline. However, it is a challenging task to extract internal coating damage information from the received acoustic signal as it requires optimization of sensor settings. Moreover, due to the physics of guided waves, background noise and inspected materials properties, the received signal is highly non-linear, noisy and non-stationary, warranting the use of sophisticated signal processing techniques for extraction of defect information. This project proposes to address these problems by achieving following objectives: 1. To collect data using EMAT scanning device for pipe samples with various internal coating defect profiles. These pipe samples will be prepared by introducing artificial defects. 2. To optimize EMAT sensor configuration and settings to extract maximum information of the defects. 3. To employ various advance signal processing techniques and algorithms to extract defect information from the collected data. The project team has processed the Purchase Request for EMAT device, which is now with the universitys purchase department and it is expected to take 3 to 4 months from date of submitting this proposal. The solution to above defined problem would contribute significantly to oil and gas service sector and the project is anticipated to produce high impact IPs.
|Effective start/end date
|1/02/20 → 1/01/21
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