Federal rule changes governing natural gas pipelines have made non-destructive techniques, such as instrumented indentation testing (IIT), an attractive alternative to destructive tests for verifying properties of steel pipeline segments that lack traceable records. Ongoing work from Pacific Gas and Electric Company’s (PG&E) materials verification program indicates that IIT measurements may be enhanced by incorporating chemical composition data. This paper presents data from PG&E’s large-scale IIT program that demonstrates the predictive capabilities of IIT and chemical composition data, with particular emphasis given to differences between ultimate tensile strength (UTS) and yield strength (YS). For this study, over 80 segments of line pipe were evaluated through tensile testing, IIT, and compositional testing by optical emission spectroscopy (OES) and laboratory combustion. IIT measurements of UTS were, generally, in better agreement with destructive tensile data than YS and exhibited about half as much variability as YS measurements on the same sample. The root-mean squared error for IIT measurements of UTS and YS, respectively, were 27 MPa (3.9 ksi) and 43 MPa (6.2 ksi). Next, a machine learning model was trained to estimate YS and UTS by combining IIT with chemical composition data. The agreement between the model’s estimated UTS and tensile UTS values was only slightly better than the IIT-only measurements, with an RMSE of 21 MPa (3.1 ksi). However, the YS estimates showed much greater improvement with an improved RMSE of 27 MPa (3.9 ksi). The experimental, mechanical, and metallurgical factors that contributed to IIT’s ability to consistently determine destructive UTS, and the differences in its interaction with composition as compared to YS, are discussed herein.
The Pipeline and Hazardous Materials Safety Administration (PHMSA) Notice of Proposed Rulemaking (NPRM), with Docket No. PHMSA-2011-0023, substantially revises 49 CFR Part 191 and 192. Notable among these changes was the addition of §192.607, verification of pipeline material. This section calls for the verification of material properties of pipe and fittings located in either high consequence areas, class 3, or class 4 locations where traceable, verifiable, and complete records do not exist. Material properties include grade (yield strength, YS, and ultimate tensile strength, UTS) and chemical composition. The proposed regulations include an independent third-party validation for non-destructive testing (NDT) methods to determine material strength and require an accuracy of within ±10% of an actual strength value. Among the NDT technologies currently available to pipeline operators to estimate material strength is instrumented indentation testing (IIT). IIT is based on the principal that there exists a relationship between the indentation response of a material and its stress-strain curve. The indentation response is measured during the IIT process whereby an indenter is sequentially forced into the material during testing. The link between the indentation response and the material stress-strain curve is established often through the use of iterative Finite Element Analysis (FEA). The IIT vendor’s proprietary software performs this calculation, converting force-displacement measurements into an estimate of YS and UTS. In this study we extracted force-displacement data from IIT performed using FEA on an idealized steel. This data was then coupled with literature algorithms developed at Seoul National University (Kwon et al.). Parametric sensitivity analysis was then performed on estimated YS with respect to the algorithm parameters. Preliminary results indicate that while variations in the indenter constant, ω, used to estimate surface deformation do not significantly alter the predicted UTS or YS, the sensitivity to deviations in the empirical constant, Ψ, relating normal load to representative stress was more pronounced due to an effect on the calculated power-law constant, K. PHMSA’s NPRM accuracy requirements for NDT to establish yield and tensile strength should be driven by a rigorous understanding of material inhomogeneities, uncertainties in actual tensile strength determination, experimental uncertainty, and modeling uncertainties. The analysis performed in this paper provides part of this rigorous framework to establish realistic accuracy requirements for NDT that must drive federal rulemaking. In addition, this research highlights the need for pipeline operators to establish controls on the algorithms adopted by commercial NDT vendors.
The United States Pipeline and Hazardous Materials Safety Administration (PHMSA) recently revised the federal rules governing natural gas transport. PHMSA added a new section on the verification of pipeline material properties for pipeline assets with insufficient or incomplete records. This section permits the use of nondestructive examination (NDE) technologies to estimate material properties, which include yield strength (YS) and ultimate tensile strength (UTS), if several conditions are satisfied. These include that NDE measurement accuracy and uncertainty be conservatively accounted for, that the NDE technology be validated by experts, and that proper calibration procedures be implemented. One such NDE technology is Instrumented Indentation Testing (IIT), which can be used to estimate YS and UTS. Precise quantification of any NDE technology’s precision and accuracy requires consistent identification of test errors: if an error occurs during a measurement such that the data should be excluded from subsequent analyses, analysts need to be alerted to the data characteristics prior to including these results. These testing errors are distinct from the inherent measurement uncertainty due to both random error and systematic error. Any NDE measurement will contain some degree of uncertainty; however, faulty measurements exhibiting clearly identifiable errors must be excluded from subsequent analyses to maintain the integrity of the data set. Accordingly, this paper extends Pacific Gas and Electric’s (PG&E’s) previously reported efforts on IIT uncertainty quantification by presenting observations of a specific type of IIT error related to tool fixturing that has occurred during in-situ testing and describing the characteristics of how this error was exhibited in the test data. Once this test error was clearly identified, isolated, and was found repeatable; pre-processing algorithms were adapted to detect and alert NDE technicians to this error during testing, ultimately evolving NDE work procedures. This paper discusses this process from the initial recognition of a test error, to the adaptation of appropriate detection algorithms, and then finally to resulting revisions in operator procedures. Ultimately, these modifications have improved validation data quality and reduced the error rate of IIT measurements collected in the field.
The October 2019 revisions to US federal rules governing natural gas pipelines require Operators to establish vintage and manufacturing process for line-pipe assets with incomplete records. Vintage and manufacturing process are considerations when establishing populations of pipe for maximum allowable operating pressure (MAOP) reconfirmation, materials verification, and integrity management programs. Additionally, the rule changes establish an allowance to utilize in-situ nondestructive examination (NDE) technologies to verify line-pipe material properties including strength, composition, microstructure, and hardness. Economic and market demands have driven changes in steelmaking technologies and pipe-forming approaches. Knowledge of the relationships between processing, microstructure and mechanical properties have been fundamental to the evolution of steel line pipe manufacturing. Product specifications and standards for the manufacture and testing of pipe and tube have crystallized this evolution as performance expectations increased. The resulting manufacturing process changes have left a variety of “fingerprints” observable from in-situ materials verification NDE data, when analyzed holistically. The purpose of this work is to enable operators to begin leveraging these fingerprints to illuminate the vintage and manufacturing process of their line pipe assets using the NDE data. A method is proposed to re-establish line-pipe asset manufacturing and vintage records using historical line pipe manufacturing practices and NDE materials verification data.
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.
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