illinois - railtec slide 1 2013 university of illinois at urbana-champaign. all rights reserved...

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© 2013 University of Illinois at Urbana-Champaign. All Rights Reserved

Pipeline Data AnalysisIntroduction to Pipeline Transportation Risk Analysis

Jeff LaHucikRail Transportation and Engineering Center – RailTECDepartment of Civil & Environmental EngineeringUniversity of Illinois at Urbana-Champaign, U.S.A.

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© 2013 University of Illinois at Urbana-Champaign. All Rights Reserved

Data Collection• Data was collected from the PHMSA Pipeline Safety Incidents

Database.

• This database was recent until April 30th.

• The database contains all pipeline incidents, without a minimum release size, that occurred within the United States as well as in offshore pipeline.

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Data Refining

• The data used was from January 1, 2002 through April 30, 2013.

• All incidents not involving crude oil were removed from the database.

• All incidents offshore were removed from the database as well.

• Incidents without latitude and longitude were removed from the database.

• These incidents were removed since they were either irrelevant to analysis of crude oil pipeline transportation over land or had incomplete data entries.

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Yearly Analysis of Crude Oil Pipeline Incidents

• Once all unnecessary data fields and entries were removed from the database, an analysis of yearly crude oil pipeline incidents was done.

• The amount of incidents was summed up by year and displayed graphically, as shown on the next slide.

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Historical Trends: Year-to-Year Incidents

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Incident Cause Georeferenced Data

• After the yearly incident analysis was done, the database was split into 7 incident cause categories (other, equipment failure, corrosion failure, incorrect operation, material failure, excavation damage, and external impact).

• Latitude and longitude data entries were converted to decimal degrees from various other units as provided in the original database.

• This data was imported into ArcGIS 10.1 and each incident was mapped as a single point (a dot).

• Each incident was assigned a different color.

• The map of crude oil pipeline incidents can be seen on the next slide.

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Crude Oil Pipeline Incidents Map

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Severity/Frequency Analysis

• In order to obtain a basic intuition of the comparative risk of each incident cause, a severity vs. frequency analysis was done.

• Frequency = number of incidents per year.

• Severity = average cost per incident (U.S. $).

• Costs included in severity include: operator costs, socioeconomic costs, cleanup costs, cost of released crude oil, and environmental damage costs.

• The severity vs. frequency plot can be seen on the next slide.

• The average frequency and severity are weighted averages.

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Severity vs. Frequency

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Factoring in Release Size

• For each incident cause and cost category, the data was split into 10 release size categories; creating a PMF of release size.

• Release size categories (barrels): <=1, 1-5, 5-10, 10-50, 50-100, 100-500, 500-1000, 1000-2000, 2000-5000, and >5000

• Within each incident cause workbook, a separate sheet for each cost category was created.

• IF, SUMIF, and COUNTIF functions in Microsoft Excel were used to create the sheets for each cost category as shown on the next slide.

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Release Size Calculation

• Shown above is the release size and cleanup cost sheet.• Columns on the right use the IF function to determine if the release

size for that incident is within the boundaries of the column header.

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Release Size Calculation Continued

• Shown above is the final result of the release size calculations for the other incident cause.

• The total probability rule was used to calculate average barrels released and average cost per release.

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Final Statistics

• Shown above are the final statistics from all crude oil pipeline incidents from January 1, 2002 through April 30, 2013.

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Limitations of Data Analysis

• Average cost per release size category per incident cause is the only severity metric upon which the analysis is built.

• Average cost does not depend on population density (although average cost is a function of release size and population density).

• Sample sizes for each release size category per incident cause are not sufficiently large to be confident in their averages.

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AcknowledgementsThe development of this presentation is partially supported by the following organizations. The opinions expressed here do not necessarily represent the views of these organizations.

National University Rail (NURail) CenterU.S. DOT RITA University Transportation Center

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Copyright Restrictions and DisclaimerPresentation AuthorJeff LaHucikUndergraduate Research Assistant Rail Transportation & Engineering CenterCivil & Environmental Engineering DepartmentUniversity of Illinois at Urbana-ChampaignB118 Newmark Civil Engineering Lab, MC-250205 N. Mathews Ave.Urbana, IL 61801<lahucik2@illinois.edu>

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