Utilizing Partial Safety Factors to Better Manage Stress Corrosion Cracking on Pipelines

Abstract Fitness for service assessments of stress corrosion cracking (SCC) crack fields must account for in-line inspection (ILI) tool measurement error. In deterministic assessments, the vendor-specified tool tolerance is typically added to the crack field depth and sometimes to the overall length. This methodology is very conservative and leads to a large number of false positives (resource outliers) when evaluated in the field. The resource outliers are defined as cases where the safety factor on the failure pressure (Pf) assessed using the ILI measurements is below 1.25 (Pf / MOP), but the field safety factor did not indicate a need for repair at this level. In the present study, a semi-probabilistic model was calibrated to match lower percentiles (0.1th and 0.5th percentiles) drawn from distributions of predicted burst pressures generated using a full probabilistic model that is solved using Monte Carlo simulation. The Monte Carlo simulations account for measurement error in the ILI called wall thickness (WT), crack field depth, and crack field length, using probability distributions. The distributions are calibrated to reflect the difference between ILI measurements and field non-destructive examination (NDE) measurements. The calibration for length incorporates either the ratio between the total ILI-called total SCC field length and the NDE measurement of longest interacting length (LIL), or the ratio between the ILI-called LIL and the NDE LIL. The semi-probabilistic model approximates the lower percentiles (0.1th and 0.5th percentiles) of the probabilistic burst pressure distributions using a deterministic evaluation of CorLAS™, incorporating partial safety factor (PSF) adjustments on the feature’s length and depth. The PSFs are calibrated through an optimization which is run against probabilistic results generated for a comprehensive set of feature sizes and pipe attributes. The numerical optimization is done in two steps to ensure that a global optimum solution has been found. First, constrained global search is performed using simulated annealing and then steepest descent is used to fine tune to the best solution local to the outcome from the first step. The performance of the model was evaluated to ensure a proper balance is achieved between the level of conservatism in the predicted failure pressure and the dig efficiency. When evaluated against historical digs, the results showed a more than 80% reduction in the number of recommended digs that were ultimately found to be false positives (resource outliers). Based on these historical field measurements, the expected probability of a false negative call (near miss) is less than 1 × 10−8.