- NII as replacement of IVI
- Pressure Vessels
- Heat Exchangers and Tubing
- Data Science
- Equipment integrity and reliability
- Robotic Tank Inspection
Refinement of ILI tool calibration
Pipelines are often inspected by in-line inspection (ILI) using intelligent pigging tools. These tools provide valuable information on pipeline condition but given the challenges of inspection from the inside of pipelines, the accuracy of measurements can be limited. Qualification of the measurement performance of ILI tools is generally required by end clients and this includes provision of tool tolerances based on test loops with artificial defects. Measurement performance is often affected by degradation morphology, however, and test loop data is frequently not representative of performance as achieved on real degradation. The differences can be both systematic, e.g. a bias towards over or under-sizing, and random.
In many cases it is evident that actual tool performance is outside of the stated tolerance. This can lead to inappropriate integrity decisions with substantial consequences, e.g. premature replacement of lines or in-service failures earlier than anticipated. Verification of ILI findings is widely used by pipeline owners/operators as a way to ensure decisions made are aligned to the real condition of pipelines. In cases where the degradation morphology or other factors are such that there is a substantial variation in field performance of the ILI tool by comparison to its specification, there can be further benefits by using the verification inspection results to refine the ILI tool calibration.
Sonomatic can assist clients with refinement of calibration based on accurate external inspection at a limited number of locations. The process allows the full ILI feature list to be updated based on statistical analysis of the results. The update provides an improvement in accuracy for the features not included in the verification and accurate measurements for the features inspected externally. This allows integrity decisions to be made using data that is overall more representative of the actual condition of the pipeline.