The emphasis on pipeline safety through reliability analysis strategies in the pipeline industry has pushed the process of uncertainty quantification towards advanced statistical techniques. Probabilistic modeling of pipeline threats and inline inspection (ILI) validation/tool performance assessment are areas that have all benefited from advances in uncertainty quantification. More recently, the statistical calibration of measurement uncertainty has developed as a promising approach for reducing the effect of measurement errors that exist in both ILI and field non-destructive examination (NDE) measurement readings. Using traditional methods for estimating measurement error quantities in ILI data, calibration provides a robust and simple approach towards incorporating the error information towards obtaining an improved estimate of the true value. Since calibration is performed at the individual feature-level, its combination with probabilistic modeling using statistical distributions at the parameter-level can potentially provide an opportunity to understand its effect on the probabilistic analysis (leak or burst limit state). In this work, the impact of applying statistical calibration towards identifying/utilizing ILI and NDE measurement uncertainty in conjunction with probabilistic modelling is explored through an investigated problem.

This content is only available via PDF.
You do not currently have access to this content.