Effective integrity management incorporates tool performance

April 17, 2006
Application of more rigorous engineering assessment methods, such as probability of exceedance, to in-line inspection data provides a more reliable procedure for pipeline integrity management than current alternatives.

Richard McNealy, Blade Energy Partners, Houston

Application of more rigorous engineering assessment methods, such as probability of exceedance, to in-line inspection data provides a more reliable procedure for pipeline integrity management than current alternatives. Establishing tool performance parameters is critical to ensuring effective pipeline integrity.

The total number of field measurements and the number of out-of-tolerance field measurements will establish the confidence level placed in the tool sizing uncertainty, in turn allowing design of a mitigation plan to a cumulative POE consistent with target reliability levels.

In addition to providing increased safety, such an approach will result in a cost-optimized excavation and repair plan. This approach could also provide a technical basis for extending notification regarding reinspection intervals. Establishment of reinspection intervals to the maximum allowed by regulations, however, is not automatic (i.e., 5 years for liquid pipelines).

Tool tolerance and corrosion growth rate are the two primary sources of uncertainty in evaluating the future fitness for service of any pipeline and are key considerations in determining the reinspection interval.

Tools and methods relating to sampling and validation of ILI data, corrosion growth-rate analysis, and structural reliability analysis are readily available to the pipeline industry. A structured POE approach as presented in this article, the first of two, quantifies assessment performance and assessment reliability when viewed as an overall process.

The second article will focus on the importance of tool-sizing performance and corrosion growth-rate data in implementing this approach.

PHMSA guidance

On Dec. 1, 2000, the US Department of Transportation-Pipeline and Hazardous Materials Safety Administration issued a regulation requiring hazardous liquid pipeline operators to identify pipeline segments in their systems affecting high-consequence areas (HCA) and to develop and implement suitable integrity-management programs (IMP).1 Later, PHMSA published a similar IMP rule for natural gas transmission pipelines.2

Comprehensive IMP rule compliance must contain a process to assess and evaluate the segment integrity by periodic inspection, followed by evaluation of all significant defects and execution of appropriate remedial action.

Given that buried pipelines are prone to threats such as external and internal corrosion, cracks, stress corrosion cracking, and third-party damage (dents and gouges), operators often perform inspection by means of instrumented pigs.

Public concern and pressure from regulatory bodies are accelerating the need for pipeline operators to formalize and intensify their evaluations of pipeline in-line inspection (ILI) assessments. PHMSA conducted a public meeting in August 2005 to discuss a range of issues concerning IMP rules, including how pipeline operators are interpreting and evaluating data acquired by in-line inspections.3 Applicable ILI tools include deformation tools to detect dents and gouges, magnetic-flux-leakage tools to detect metal loss and mechanical damage, and ultrasonic tools to detect corrosion, cracks, and crack-like defects.

ILI tools and inspection processes are complex; consequently there are uncertainties in detection, characterization, and identification of defect locations. Use of an inappropriate ILI tool for a given threat further magnifies the potential errors. Proper understanding of the inspection technology, combined with an insight into the mechanism of the pipeline threat, along with a process for ensuring accuracy and reliability of the inspection data, are necessary for a credible integrity assessment.

This article discusses sources of ILI measurement uncertainty, application of probability of exceedence (POE) methodology for integrity assessment, guidelines on appropriate evaluation of tool performance, and their implications on an effective integrity assessment. The effect of actual ILI performance on immediate and future integrity is demonstrated, with the role of such analysis in complying with the liquid and gas IMP rules addressed.

Sources of uncertainty

Knowledge of the ILI measurement uncertainties plays an important role in determining acceptance of an in-line inspection and performing integrity assessment of reported defects and also allows for establishment of a long-term integrity strategy through deterministic or probabilistic assessment methods.

For metal loss (corrosion), sizing tolerance relates to errors in the reported corrosion feature size as compared to its actual size, which can then be quantified statistically with standard deviation or an error band at a certain confidence level. A two-sided confidence interval for a given confidence level quotes sizing errors associated with ILI tools uniquely. The depth-sizing variability of high-resolution MFL tools for corrosion metal loss is typically ±10% WT with 80% certainty (confidence level).4

The following factors typically lead to sizing errors:5

• Type of sensors, sensor mounts, signal acquisition, and processing algorithm and data recording and storage system, etc., as well as human factors, particularly analyst’s experience.

• Running conditions; i.e. tool speed, cleanliness of pipe internal wall, etc.

• Defect type, geometry, aspect ratio and interference between adjacent defects, such as metal loss or cracks adjacent to dents and welds, and pipe WT.

• Metallurgical variations and cold work-induced magnetic anisotropy due to cold expansion or cold bending, which tend to affect magnetization characteristics.

• Inappropriate tool for the threat; i.e. MFL typically better characterizes pitting corrosion than UT, while UT ILI will provide better accuracy and confidence for large-area features or general wall thinning.

• Inaccurate field measurements.

These factors may cause inconsistencies in the actual accuracy of determining defect size and location as compared to stated ILI specifications. As part of any detailed integrity assessment, it is therefore important to consider the effects of sizing and discrimination errors and account for these in the integrity strategy.

POE method

One commonly used structural reliability assessment methodology is the load-and-resistance-factor-design (LRFD) serviceability assessment. The overlap between the load and resistance defines probability of failure (Fig. 1a). Structural and pipeline reliability and serviceability analyses use the LRFD technique.6 7 8

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A loading variation should consider pressure distribution along the pipeline, geotechnical loads, cyclic loading, and other miscellaneous loading conditions. The resistance to load would include mechanical property variation, defect size and shape variation, ILI tool error, and other measurement variability. Such an analysis, however, requires extensive load and resistance data.

Fig. 1b shows a burst pressure variation established with deterministic material properties, recognizing variability of defect size established with ILI tools and field measurements. The IMP rules prescribe resistance limits for corrosion depth for liquid pipelines and the burst pressure for both liquid and gas pipelines. Multiple factors influence the degree of load and resistance uncertainty making a calculation of true probability of failure difficult.

Considering defect sizing tolerance and corrosion growth can, however, readily determine the probability of exceeding a prescribed load or measurement. Probability of exceedance analysis provides a more realistic integrity and serviceability assessment, as well as providing for reliable remedial action.

There is significant published work demonstrating the value of structural reliability-based evaluation techniques applied to pipeline assessment data.9 10 A common consideration is minimizing the number of excavations and maximizing cost savings. These same techniques and considerations ensure safety of pipelines and gauge the effectiveness of the assessment processes of inspection, remediation, and mitigation.

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A standard ILI tool performance specification for depth sizing is certain 80% of the time within ±10% WT, statistically equivalent to 95% certainty within ±15% WT metal loss, assuming a normal distribution. This performance parameter has significant influence on the effectiveness of the integrity assessment.

Two ILI tools with the same sizing tolerance (±15%) but different certainty levels will have different resulting probabilities of exceeding depth limit and-or allowable pressure criteria (Fig. 2). Actual MFL sizing accuracy could be either better or worse than expected.

The 80% certainty, ±10% WT sizing specification represents an average value generally determined by ILI tool vendors with laboratory validation tests and field validations. The physical configurations of MFL inspection tools are generally designed to cover a range of possible service conditions, such as pipe ID, WT, and tool speed.

For example, the fixed magnetic field capability of any given tool will cause the magnetic flux density to change slightly across a range of WT and may be optimal for only a portion of its operating range. The resulting defect sizing performance therefore could vary with WT. Similarly, increasing the velocity of a tool reduces the applied field strength by inducing eddy currents near the pole pieces, which can also affect sizing tolerance.5

Finally, for a threat such as corrosion, the characteristics of corrosion (small pitting vs. large localized and aspect ratio) will substantially affect percent confidence. Characterizing the confidence level for a specific pipeline therefore is critical.

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Within the context of the IMP rules, Fig. 3 illustrates a typical ASME B31G modified failure plot and the role for structural reliability in addressing ILI tolerance and corrosion growth. The IMP liquid rule specifies the repair criteria for features detected by ILI. For example, defects exhibiting a safety factor less than 1.39 × MAOP require repair within 180 days after inspection.

For a hypothetical defect with reported dimensions (Defect A in Fig. 3), the requirement for repair under the IMP rule is obvious. Justifying repair decisions for hypothetical features with nominal dimensions below the repair line requires some knowledge of tool performance and assessment. Adding the upper limit of confidence interval to the reported dimensions for conservatism provides one commonly used approach of accounting for tool tolerance. This approach, however, may not fulfill the IMP rule requirement. This limitation can be demonstrated as follows.

Application of the upper limit of tool tolerances at 80% confidence interval as shown by the error bar (Point B’) for Feature B in Fig. 3 would indicate no repair is required. Although this appears conservative, a potential for not satisfying the IMP repair rule becomes clear when an assumed probability distribution of tool sizing is taken into account. Combining measurement uncertainty with a probability distribution for corrosion growth could result in a significant potential for Feature B growing to a critical size Defect B’’ before the next scheduled inspection.

Using POE-based reliability evaluation, with a given reliability target level, allows the effect of tool tolerance on immediate integrity and the effect of potential corrosion growth on future integrity to be quantified and used to identify and schedule repairs and establish reinspection intervals recognizing the maximum allowed intervals.

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The area of the size probability density function curve, exceeding the upper limit of the confidence interval given by the tool tolerance, provides a practical graphic expression of the POE for any given in-line inspection tool. Equation 1 (see accompanying equation box) describes this mathematically. For a POE of 10-3, a long-term probability of 1 out of 1,000 measurement opportunities could be expected to exceed the specification upper limit.

The value in the quantification of pipeline condition by POE lies in the ability to evaluate risk by comparing relative levels of improvement in the condition of pipe segments as a result of quantifiable mitigative actions, such as improving cathodic protection, repair, or replacement of pipe with metal-loss features. This dynamic POE approach fully complies with the requirements of IMP rules. The conventional method, i.e., the commonly used deterministic approach, cannot achieve the same level of compliance as the POE approach.

Equation 2 calculates the overall probability of leak and rupture for the entire pipeline or individual segments, particularly important for developing a risk-based pipeline management plan.

Pipeline codes, such as DNV RP-F101, incorporate LFRD methodology with defined target reliability levels according to safety class (related to human activity). Pipelines in areas of frequent human activity are classed to anticipate a target probability of 10-5. Areas of infrequent activity are classed with a 10-4 target probability.11 Class of location determines recommended target reliability levels for the entire pipeline (Table 1).12

The PHMSA IMP rules do not address the subject of target or prescribed levels of risk for pipelines affecting high-consequence areas. The IMP rules, however, require an operator to evaluate risk factors periodically and consider decisions about remediation, prevention, and mitigation. This provides latitude for applying reliability methodology for quantifying the improvement to pipeline segments and allows for quantification of target risks within each operator’s written integrity-management program. Utilizing the probability of failure guidelines for the LRFD methodology can define POE.

Integrity-management strategies embracing target reliability levels can apply these criteria to ILI features below the 1.39 MAOP repair line illustrated in Fig. 3 (Feature B), thereby considering tool tolerance for immediate integrity together with predictions of future distribution of defect size to justify the reinspection interval.

The extension of this approach to full probabilistic methods provides a quantitative measurement of safety and cost effectiveness when planning future integrity programs. Setting appropriate target probability of failure levels for various consequence classes, optimization of excavations-repairs, and reinspection intervals helps operators to make cost-effective repair decisions,13 forming the engineering basis for justification and possible extension of reinspection intervals allowed by the IMP rules.

Limited experience has shown the conventional deterministic approach may require a significant amount of unnecessary repairs for future integrity due to additional conservatism being imposed by the repair criteria,13 that is, the repair decisions for future integrity based on B31G 1.39 MAOP assessment curve (Fig. 3), instead of MAOP.

References

1. Code of Federal Regulations Title 49, Subpart F, Sections 195.450 and 195.452, “Pipeline Integrity Management.”

2. Code of Federal Regulations Title 49, Pt. 192, Subpart O, “Pipeline Integrity Management.”

3. Pipeline and Gas Journal, “Gas transmission integrity rule open for discussion,” August 2005, p. 48.

4. Westwood, S., and Cholowsky, S., “Independent Verification of the Sizing Accuracy of Magnetic Flux Leakage Tools,” International Pipeline Conference, Puebla, Mexico, Nov. 12-14, 2003.

5. Nestleroth, J.B., and Bubenik, T.A., “BattelleMagnetic Flux Leakage (MFL) Technology For Natural Gas Pipeline Inspection,” GRI Report GRI-00/0180, September 2000.

6. Lorenz, Robert F., “Load and Resistance Factor Design (LRFD) for Structural Steel,” American Institute of Steel Construction Specifications, 1990.

7. Lewis, D.B., Brand, P.R., and Whitney, W.S., “Load and Resistance Design for Oil Country Tubular Goods,” Offshore Technology Conference, Houston, May 1-4, 1995.

8. Bai, Y., and Song, R., “Reliability-Based Limit-State Design and Requalification of Pipelines,” International Offshore Mechanics & Arctic Engineering Conference, Lisbon, July 5-9 1998.

9. Turley, R., Johnston, D., and Kolovich, C., “Probability approach promises enhanced maintenance program,” Pipeline & Gas Industry, Vol. 84, No.1, January 2001, pp. 69-74.

10. Gao, M., and McNealy, R., “Progressive Management and Engineering Evaluation of Pipeline Integrity,” 21st Century Pipeline Symposium, Canadian Institute of Mining, Metallurgy and Petroleum, Calgary, Aug. 21-24, 2005.

11. RP-F101, Recommended Practice, Corroded Pipelines, Det Norske Veritas, April 1999.

12. Zimmerman, T.J., Chen, Q., and Pandey, M.D., “Target Reliability Levels for Pipeline Limit States Design,” International Pipeline Conference, Calgary, June 7-11, 1996.

13. Gu, B., Kania, R., and Gao, M.,“Probabilistic Based Corrosion Assessment for Pipeline Integrity,” NACE Corrosion 2004, New Orleans, Mar. 28-Apr. 1, 2004.

The authors

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The authors Richard McNealy (rmcnealy @blade-energy.com) is a senior engineer with Blade Energy Partners Ltd. He has also served as engineering manager with GE Pipeline Solutions (PII North America), holds a BS in metallurgical engineering from Colorado School of Mines, and is a licensed professional engineer.

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Gopala Vinjamuri (vinjamurig @verizon.net) is a consultant for Blade Energy Partners Ltd. in Woodbridge, Va. He has also served as senior materials engineer for the US Department of Transportation—Office of Pipeline Safety and Hazardous Materials and holds an MS in applied mechanics from Lehigh University, as well as a DMIT in aeronautical engineering and BS in mathematics from Madras University, India.