June 2017, Vol. 244, No. 6


Cutting Pipeline Operational Costs Without Sacrificing Safety

By Bruce A. Young and Jennifer O’Brian, Battelle, Columbus, OH

Pipeline operators are increasingly turning to software-based tools to predict pipeline failure and reduce the need for frequent manual field inspection. But how well do these models really predict the development of axial cracks and other pipeline flaws?

In order to reduce field inspection costs without increasing the risk of an unexpected pipeline failure, operators need to have confidence that software models provide accurate and reliable predictions. Battelle has developed a new software solution that uses empirically derived and physics-based modeling to provide more complete and accurate life prediction than is possible with traditional software solutions. Pipeline operators can use the predictive software to make more effective decisions for repair and replacement and reduce the frequency of costly inline inspections or hydrostatic tests.

There are few software tools available to model anomaly failure and growth for oil and gas pipelines. Of the software solutions on the market, only small set claim to be able to probabilistically predict anomaly failure or growth of any type. However, none of the tools in common use provide robust prediction of crack development under different operating conditions based on physics and empirical data.

Battelle’s software, called PipeAssess PI™, was developed as part of a Department of Transportation (DOT) Pipeline and Hazardous Materials Safety Administration (PHMSA) project, “Comprehensive Study to Understand Longitudinal ERW Seam Failures.” The DOT wanted a better integrity management tool to understand how axial cracks effect pipelines and help pipeline operators provide more effective recommendations for pipeline management and risk reduction.

The goals of the study were to determine the characteristics of ERW seams that make them susceptible to failure and identify the factors that pipeline operators should consider in order to assure that the operation of ERW pipelines is safe. To facilitate these goals, a modeling program was developed to analyze the collected data and build predictive models of pipeline failure.

Researchers first gathered empirical data using Battelle’s extensive “library” of oil and gas distribution and transmission pipes. Pipes with known defects were evaluated using inline inspection (ILI) and in-the-ditch technologies. The pipe sections were then pressurized to failure. This data was used to provide a more accurate model of axial crack development for various pipe types – including electric resistance-welded (ERW) and flash-welded (FW) pipes made of brittle, quasi-brittle and ductile steels – under different operating conditions.

The current version of PipeAssess PI™ allows operators to model stress due to internal pressure with various crack geometries, including cold welds, hooks cracks and selective seam weld cracks. Additionally, these cracks can be analyzed, based on uncertainty in input data via probabilistic techniques using Monte Carlo simulation (Figure 1). Further refinements are planned that will make PipeAssess P.I. capable of analyzing a large set of anomalies between pump and compressor station, including corrosion, cracking and dents.



Figure 1: Example of a probabilistic failure assessment diagram.

The software incorporates user-defined hydrostatic test conditions, operating pressure profiles and attribute inputs such as pipe geometry, material properties and crack geometry (from inline inspection and in-the-ditch non-destructive examination). The model considers multiple mechanisms for growth, including time-dependent cracking during hydrotests as well as fatigue cracking due to pressure cycles (Figure 2).

The crack growth physics use J-tearing theory for elastic-plastic material behavior and Paris Law behavior accounting for stress-ratio variations and overloads (i.e., hydrotests) for fatigue crack growth analyses. The material constitutive model for time-dependent crack growth under simulated hydrostatic test conditions is the Ramberg-Osgood stress-strain model. These material and fracture mechanics models, along with state-of-the-art stress-intensity factor solutions, provide highly accurate prediction of future crack development and remaining pipeline life.



Figure 2: Results from simulated hydrostatic test and subsequent pressure cycling.

Reducing Costs

Hydrostatic testing can help detect near-critical axial cracks and other near-critical defects. However, this testing is not predictive; it is not always clear whether a detected crack warrants immediate repair or replacement or simply requires continued monitoring. It is also expensive. Many companies monitor crack development through repeated inline inspections, requiring costly operational shutdowns.

With better models, pipeline operators can reduce the frequency of ILI and hydrotesting and optimize re-inspection intervals and still meet DOT requirements for assessment of liquid and gas pipelines. PipeAssess PI™ equips operators with critical data prior to digs or lab verification for early decision-making. As fluctuations in operating conditions occur, operators can run the analysis as often as needed to determine how pipeline failure risk changes with time.

Better prediction has the potential to save a significant amount of money for pipeline operators. Most operators rely on conservative assumptions to make prioritization decisions for pipeline management. A study by Battelle and T.D. Williamson compared pipeline management decisions made using data from traditional ILI technologies to decisions made using PipeAssess PI™ to model and predict future crack development.

The study showed using more sophisticated modeling and inspection tools allows pipeline operators to better prioritize inspection intervals and remedial actions, significantly reducing costs. In fact, using data from 34 pipes with known flaws, operators using traditional inspection methods and conservative assumptions would have initiated immediate remediation for 97% of identified flaws. More accurate predictive modeling flagged just one of the flaws for immediate action. With estimated costs for each dig ranging from $25,000-50,000, the financial impact of these decisions is substantial.

Predictive modeling also improves pipeline safety by allowing companies to better prioritize replacement and repair decisions to address pipeline flaws that present the biggest risks first. Pipeline operators have finite resources for repair and replacement; if everything is flagged as equally urgent, it is difficult to know which pipelines need the fastest attention. More refined analysis makes these decisions easier and ensures that resources are spent where they are most critically needed to prevent an imminent failure.

Modeling also improves safety by allowing operators to better understand the risk implications of re-rating the maximum allowable operating pressure (MOAP) for existing pipelines. The physics-based model allows operators to see how pipeline flaws develop under different operating conditions. This will help them make informed decisions for MOAP ratings that will ensure pipeline safety until repairs or replacements can be made.

DOT Requirements

New software solutions are increasingly important for pipeline operators as they prepare to meet new rules for pipeline integrity management. The DOT PHMSA Notice of Proposed Rule Making (NPRM) Part 192 – §192.607 proposes that all transmission lines have material records that are “verifiable, traceable and complete” in designated high-consequence areas (HCAs). Predictive modeling could help operators meet this requirement and increase pipeline safety while minimizing costs of ILI and digs.

PipeAssess PI™ is already in use by the DOT and is available commercially for pipeline operators. The predictive modeling software could soon become the adopted standard by pipeline owners and operators to complete an engineering critical assessment (ECA) consistent with the requirements of current and pending PHMSA rules, including Code of Federal Regulations (CFR) 49, Parts 192 (gas “mega-rule”) and 195 (liquid rule).

As the software is refined and validated with additional crack and material characterization data, it has the potential to greatly advance ECAs by improving the accuracy of failure predictions. Better predictive modeling will help operators more efficiently navigate and meet existing and proposed DOT compliance rules.

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