Pipeline Safety Seminar Distribution Pipe Segment Risk Assessment and Replacement Panel Discussion Central Hudson Gas & Electric Karl Reer, Director, Gas Distribution Engineering
Central Hudson Gas & Electric About 75,000 Customers 1,200 Miles of Distribution Main 56,500 Gas Services Combination Company 825 Employees
All gas assets recorded in GIS 2008 to 2010
Resulting Benefits Include Pipe Material Inventory Tracking and Location Information Improved Leak Mapping and Attribute Association with Pipe Segments Can Readily Map Leak Prone Pipe Can Implement Feature Specific Inspection and Maintenance programs
Resulting Benefits (continued) Enables use of GIS Asset based inspection and compliance software Instant electronic access to all map records in the field Enables use of risk assessment and pipe replacement software such as MRP.
Pipe Inventory: Plastic: 618 Miles Steel: 495 Miles of which 343 Miles is monitored and protected against corrosion Cast Iron: 64 Miles Wrought Iron: 23 Miles 239 Miles of main that can be considered leak prone
Pipe Material Leak Rates: Plastic: 12/100Miles/Year Steel: 41/100Miles/Year Cast Iron: 137/100Miles/Year Wrought Iron: 105/100Miles/Year
Replacement Program Drivers: Gas fuel prices dropped due to increased production made possible by fracking. Major incidents occurred at a number of companies involving cast iron pipe. DIMP Requirements MAOP Rules Interpretation Changes Positive regulatory support
Drivers (continued): Aging Infrastructure Growing number of below grade leaks found each year Regulatory interest in project management and capital investment decisions
Program Requirements: Audit ready Best use of limited (fixed) resources Provide risk assessment for each pipe segment Can be traced back to asset data base and leak repairs. Flexible enough to address new threats
Program Elements Risk Model We are using MRP, a GL software product, to model and rank pipe segment risk The standard risk model provided in this program utilizes a 5 year leak history, pipe inventory age, operating pressure, diameter, and material. The model is flexible enough to take other factors into consideration
Definitions Condition Score: the predicted number of below grade leaks per mile/per year for the individual pipe segment Risk Score: estimated probability of an incident/mile/year for each pipe segment Banded Score: sum of normalized condition, risk, and ancillary consideration scores
Banded Risk Score Formula BS= CS(WFCS) + RS (WFRS) + A1(10)(WF1) +A2(10)(WF2) + A3(10)(WF3)+..+An(10)(WFn) BS= Banded Score WFXX= Weighting Factor sum of WFs =1.0 CS= Condition score RS=Risk score A1 to An= alternate factor indicator (0 or 1)
Pivot Table Showing Pipe Section Risk Calc.
Alternate Factors Business District CPSystem Status Main Feeder Route Proximity to a district regulator station Inside a flood plain Diameter
Alternate Factors: Anything that can be assigned an attribute value in the Distribution main Pipe feature class in GIS yes or no (0 or 1) value Inclusion in the risk model and at what weighting factor determined by SME committees
Project Selection Steps Input data is provided to the software detailing capital budget availability for each of the next 5 years, pro-forma pipe replacement unit costs, and the like. Software identifies all segments that are candidates for replacement. Adjacent and geographically close segments are grouped together into projects. Cost estimates for each project are verified and corrected with SMEs
Project Ranking and Funding Projects are given a final rank score Score = Total BS*(10,000)/(Total Cost) Essentially results in a number defined as banded score (risk) eliminated per $10,000 invested. Projects are funded in order of highest score to lowest.
Model Output The 2013 model run identified 300 projects to replace 590 of the 10153 sections of leak prone pipe in the GIS data base. 18 of these were funded for construction in 2014 representing the bulk of the 23 projects identified for construction.
Evaluation (continued) We will be doing an analysis of the condition score of segments selected for replacement and comparing them to the average for the material segment pool.
Project List By Priority - Steel
A portion of the 2014 budget sheet
Model and Program Evaluation The model can predict numbers of leaks experienced over time as a function of capital investment dollars The primary measurement of success is a comparison of predicted leak rates against actuals over time.
Measurement of Success
Issues to work on: Model calibration adjusting condition and risk scores to match experienced conditions would improve the output. Tracking leak rates for leak prone steel pipe would allow for better calibration Model does not do a great job of leak or consequence prediction on pounds pressure cast
Below Grade Leak Management 100% of leak prone pipe is on an annual survey cycle Cast Iron frost survey is continuous once started All distribution leak work completed by 9/1 each year
Leak Management (continued) Targets set for workable backlog (Class 2 and 2A) each year at the start of the frost cycle. Targets set for total leak inventories.
Thank you! Questions?