Hydrostatic pressure testing is the most widely accepted approach to verify the integrity of assets used for the transportation of natural gas. It is required by Federal Regulations 49 CFR §192 to substantiate the intended maximum allowable operating pressure (MAOP) of new gas transmission pipelines. The Pipeline and Hazardous Materials Safety Administration (PHMSA) Notice of Proposed Rulemaking (NPRM) with Docket No. PHMSA-2011-0023 [1], proposes an additional requirement for MAOP verification of existing pipelines that: i) do not have reliable, traceable, verifiable, or complete records of a pressure test; or ii) were grandfathered into present service via 49 CFR §192.619(c). To meet this requirement, the NPRM proposes that an Engineering Critical Assessment (ECA) can be considered as an alternative to pressure testing if the operator establishes and develops an inline inspection (ILI) program. The ECA must analyze cracks or crack-like defects remaining or that could remain in the pipe, and must perform both predicted failure pressure (PFP) and crack growth calculations using established fracture mechanics techniques. For assets that cannot be assessed by ILI, however, the implementation of an ECA is hindered by the lack of defect size information.
This work documents a statistical approach to determine the most probable PFP and remaining life for assets that cannot be assessed by ILI. The first step is to infer a distribution of initial defect size accumulated through multiple ILI and in-ditch programs. The initial defect size distribution is established according to the as-identified seam type, e.g. low-frequency electric resistance weld (LF-ERW), high-frequency electric resistance weld (HF-ERW), flash weld (FW), single submerged arc weld (SSAW), or seamless (SMLS). The second step is to perform fracture mechanics assessment to generate a probabilistic distribution of PFPs for the asset. In conjunction with the defect size distribution, inputs into the calculation also include the variations of mechanical strength and toughness properties informed by the operator’s materials verification program. Corresponding to a target reliability level, a nominal PFP is selected through its statistical distribution. Subsequently applying the appropriate class location factor to the nominal PFP gives the operator a basis to verify their current MAOP. The last step is to perform probabilistic fatigue life calculations to derive the remaining life distribution, which drives reassessment intervals and integrity management decisions for the asset. This paper will present some case studies as a demonstration of the methodology developed and details of calculation and establishment of database.
Automatic Welding Control System for Electric-resistance Weld Tube Mill
