{"id":20731,"date":"2025-03-13T11:36:34","date_gmt":"2025-03-13T16:36:34","guid":{"rendered":"https:\/\/entrustsol.com\/?p=20731"},"modified":"2025-03-13T11:36:34","modified_gmt":"2025-03-13T16:36:34","slug":"enhancing-pipeline-class-location-analysis-with-connected-route-considerations","status":"publish","type":"post","link":"https:\/\/entrustsol.com\/enhancing-pipeline-class-location-analysis-with-connected-route-considerations\/","title":{"rendered":"Enhancing Pipeline Class Location Analysis With Connected Route Considerations"},"content":{"rendered":"

\"\"Kris Kerr <\/strong>\u2013 Project Manager \u2013 Data Solutions<\/i><\/p>\n

Mr. Kerr is responsible for managing Class Location analysis and related services for ENTRUST\u2019s gas pipeline operator clients:<\/i><\/p>\n

When a natural gas pipeline fails the primary risk is to people and property located in the vicinity of the pipeline, due to the potential for product ignition and explosion. Pipeline Class Location is an algorithm used to gauge population density along the pipeline. The greater the population density, the greater the risk. Class Locations values range from Class 1 (low population density, rural) to Class 4 (high population density, urban). Originally used as part of pipeline safety design, Class Location analysis is now integral to federally mandated pipeline integrity management requirements and is a critical activity for pipeline operators.<\/span><\/p>\n

Pipeline Class Location analysis relies on the \u2018sliding mile\u2019 algorithm, which primarily determines the Class Location at any point along a pipeline by counting the number of dwelling units in buildings intended for human occupancy (BIHOs) within a 660-foot radius and along a one-mile span of the pipeline. As the analysis moves along the pipeline, the one-mile window \u2018slides\u2019 with it, giving rise to the term \u2018sliding mile.\u2019<\/span><\/p>\n

Traditionally, gas transmission operators conduct Class Location analysis on pipeline \u2018routes,\u2019 which are continuous, non-branching sections of pipe within the larger system. These routes typically span from one compressor station to the next, isolating them in terms of operating pressure and product flow.<\/span><\/p>\n

However, a challenge arises when operators define routes based on non-physical boundaries, such as operating division borders, district limits, or state lines, even though product flow is continuous across these boundaries. Additionally, operators may split routes on physical pipe changes that have no bearing on Class Location analysis, such as changing pipe diameter. Similarly, some operators split routes at pigging launcher\/receiver stations. Although these stations physically separate the pipeline during pigging operations, they are bypassed during normal operations, allowing uninterrupted product flow between routes. The challenge becomes even more acute in gas gathering systems which are, by nature, highly branching, networked pipeline systems.<\/span><\/p>\n

The Pipeline Hazardous Materials and Safety Administration (PHMSA), the division of the U.S. Department of Transportation (DOT) responsible for pipeline safety oversight, has raised concerns about Class Location results for routes defined by such artificial boundaries and for interconnected routes in highly networked gathering systems. PHMSA argues that when pressure and product flow remain continuous across connected routes, the \u2018sliding mile\u2019 analysis should extend beyond the boundaries of the individual route.<\/span><\/p>\n

This regulatory shift means operators must no longer limit Class Location analysis to isolated routes. Instead, they are required to account for adjacent, physically connected routes, ensuring that the analysis captures the full context of continuous product flow and contiguous pressure regimes. By adopting this broader approach, operators can provide more accurate and comprehensive Class Location results, aligning with PHMSA’s safety standards.<\/span><\/p>\n

Enhanced Class Location analysis with ENTRUST’s networked algorithm<\/span><\/h2>\n

Tracy Thorleifson <\/i><\/strong>\u2013 Vice President, Software<\/i><\/p>\n

Mr. Thorleifson is responsible for ENTRUST\u2019s suite of commercial software solutions, including the Gas HCA Tool, which are used to enable ENTRUST\u2019s advanced analytical service offerings:<\/i><\/p>\n

ENTRUST has upgraded its Gas HCA Tool to incorporate networked Class Location analysis, offering a more comprehensive approach to evaluating natural gas pipeline systems. The enhanced algorithm automatically traces connectivity between pipeline routes, identifying junctions where two routes meet\u2014either end-to-end or end-to-edge. At these junctions, the tool generates an artificial \u2018super route\u2019 that extends at least one mile in either direction beyond the connection point.<\/span><\/p>\n

The sliding mile analysis is then performed on both the original routes and the newly created super routes. The BIHO dwelling unit counts for the super routes are overlaid onto the sliding mile counts for the original routes. The highest BIHO dwelling unit count in any overlapping section determines the class location for the original routes, ensuring that no critical data is overlooked.<\/span><\/p>\n

To maintain clarity, the tool tracks relationships between super routes and their parent routes. This allows operators to easily identify when a Class Location on an original route has been determined by the sliding mile BIHO dwelling unit count from a super route. This automated, network-aware approach streamlines compliance, while providing a more accurate reflection of pipeline safety requirements.<\/span><\/p>\n

How Super Routes enhance pipeline integrity management<\/span><\/h2>\n

Bethany Augustine <\/i><\/strong>\u2013 Senior Project Manager – Integrity<\/i><\/p>\n

Ms. Augustine is responsible for providing pipeline integrity subject matter expertise and oversight for ENTRUST\u2019s Class Location analysis and related services:<\/i><\/p>\n

Performing accurate Class Location determinations on pipeline segments less than one mile in length has traditionally been a challenge. The approaches previously applied could sometimes result in incongruous results across connections between lines. For instance, prorating sliding mile house counts to account for line lengths less than a mile on short laterals could lead to situations where short laterals were assigned a higher (prorated) class location value than the main line to which the lateral connects. Similarly, simply applying the class location value for the main line to a short, connected lateral line could miss actual class location changes within the lateral. The ability to trace from short laterals into connected mainlines, and potentially other nearby laterals, provides a much more accurate method for determining class location values. Additionally, the potential incongruities of results seen with other methods are avoided; class location values across connection points between lines are always consistent.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Kris Kerr \u2013 Project Manager \u2013 Data Solutions Mr. Kerr is responsible for managing Class Location analysis and related services for ENTRUST\u2019s gas pipeline operator clients: When a natural gas pipeline fails the primary risk is to people and property located in the vicinity of the pipeline, due to the potential for product ignition and […]<\/p>\n","protected":false},"author":13,"featured_media":20734,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"wds_primary_category":0,"footnotes":""},"categories":[33,40],"tags":[],"class_list":["post-20731","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-gas-utilities"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t