Assessment of Frost Impact on Cast Iron Pipes

Transcription

1 Assessment of Frost Impact on Cast Iron Pipes GTI Report Project: Principle Investigator: Khalid Farrag, Ph.D., PE Presented by: Paul Armstrong April 10,

2 Company Overview ESTABLISHED 1941 Independent, not-for-profit established by the natural gas industry Providing natural gas research, development and technology deployment services to industry and government clients Performing contract research, program management, consulting, and training Wellhead to the burner tip including energy conversion technologies 2

3 Introduction About 33,600 miles of cast iron mains are estimated to be in service in About 50% of these pipes are located within four states: New Jersey, New York, Massachusetts, and Pennsylvania. Most of the states have replacement programs. However, replacement efforts in urban areas can be technically difficult, extremely expensive, and will take time to complete. Consistent with the 49 CFR federal requirements, LDC s have developed procedures for surveillance of their cast iron to identify leaks, damages, and take appropriate actions. 3

4 Introduction The objective of the study was to enhance LDC s winter surveillance programs, based on an engineeringsupported approach which evaluates the weather, soil, and pipe parameters. The approach consisted of the following: Review leak surveillance data, inspection, and repair records. Correlate CI leaks and damage due to freeze with local site conditions. Statistical modeling and assessment to enhance the winter leak surveillance procedure. 4

5 Introduction The project reviewed the cast iron pipeline inventory in the eastern region of MA. This region included towns in four counties (Essex, Middlesex, Suffolk, and Norfolk) and had a total of 2,258 miles of CI main, which is about 20% of the CI inventory in the gas distribution system in New England and NY. Investigated leak-database records during the winter months in the period from 2001 to

6 CI Inventory Database The area was divided into grids (275 grids of area about 1.2 square miles each). 6

7 CI Inventory Database CI-mileage inventory was added in each grid (red lines) in the study area. CI Inventory Database Attributes of each grid 7

8 CI Leak Database CI leak inventory was added in each grid (yellow dots) in the study area. CI- Leak Database ( ) 8

9 CI Leak Database All Leaks: from Leak Cause: Natural Force = 6 Filter to Filter to Leak Type: Broken Main = 1 Pipe Leaks = 2, 4, and 5 9

10 CI Leaks by: Pipe Size Leak Type Data from Histograms, Program: Statistica Note: Data in graph is not normalized with length of CI for each pipe size 10

11 CI Damage by: Pipe Size No. of Breaks [Normalized] All Data from

12 CI Leak/Damage Monthly Records

13 CI Leak/Damage Monthly Records

14 Weather Records * Weather Stations (in red) Records: 14

15 Weather Records Monthly Freeze Days [DX32] : Number of days per month with the High daily temperature <= 32 o F 15

16 Weather Records Monthly Freeze Days [DX32] : Number of days per month with the High daily temperature <= 32 o F 16

17 Weather Records Example of the annual variation of Freeze Days [DX32] 17

18 Soil Records * AASHTO Guide for Design of Pavement Structures, American Association of State Highway and Transportation Officials, Washington, D.C,

19 Soil Data Example: Essex County 19

20 Soil Records 20

21 ANOVA for Response Surface Linear Model Response-2 : Break Ratio Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob. > F Model < significant A-DT B-DT < C-DX < D-%Sand 1.437E E-%Silt F-%Clay The Model F-value of implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. P-Values less than indicate model terms are significant. Terms DT32 & DX32 were significant model terms. * Statistical Program: Design-Expert, Stat-Soft Inc. 21

22 Data Analysis Pipe size did not correlate to leaks. Pipe size correlated to pipe breaks: Smaller diameters had more breaks. No breaks were recorded for pipes > 18-inch diameter. No correlations with age of pipes. 22

23 Data Analysis Soil properties were obtained from the Web Soil Survey database of the U.S. DOA. Soil with high silt and clayey-silt contents are more susceptible to freeze and heave. However, soil data could not be used as a trigger to predict pipe damage or initiate winter surveillance. Some of the factors are: (a) soil variations make it difficult to estimate soil-frost susceptibility, and (b) backfill around the pipes in the urban areas differs from native soils listed in the database. 23

24 Data Analysis Freeze-Days: Number of days per month with the High-Daily temperature <= 32 o F. Freeze Days relates to the other commonly used term: Frost Degree Days, which is: Number of days with the high-daily temperature T <= 32 o F multiplied by (32- T) o F]. The relationship between Freeze-Days and pipe damage records was plotted in the following slides. 24

25 Monthly Freeze Records Records of Freeze-Days periods (Sample in NW MA) 25

26 Monthly Freeze Records Records of pipe damages in Freeze-Days periods 26

27 Monthly Freeze Records Records of pipe damages in Freeze-Days periods 27

28 Conclusions The correlation between the Freeze-Days and pipe damage was evaluated in 18 towns in the NE. Most of the winter pipe damages (90%) occurred after 5 days accumulation of the Freeze-Days. This correlation can be used to establish a criterion for initiating winter patrols and to optimize the probabilities of detecting winter damage. Soil freeze-depth varies widely and is highly dependent on soil type, moisture condition, and type and thickness of the pavement cover. This makes it difficult to have a representative soil freeze-depth for the whole region. 28

29 Conclusions Freeze-Days provides a better correlation than using Average Daily Temperature to correlate to CI. Utilities may use the weather data in their service areas to establish similar winter surveillance plans. The following slides provide a suggested procedure for the scheduling winter patrol as an illustration of the approach to implement weather data. 29

30 Conclusions Initiation of Winter Patrol: Initiate winter patrols when the Freeze-Days (No. of days with Hi-daily temperature <= 32 O F) reaches 5 days. The 5-day sum may cover several freeze cycles and is not necessarily in consecutive days. Example: A company-approved weather forecast shows subfreezing weather conditions with Freeze-Days of 3 consecutive days, followed by an above-freeze temperature. This condition may not cause an initiation of the winter surveillance. However, a second cycle of 2 Freeze-days or more would result in accumulating 5 days and initiating the surveillance plan. 30

31 Conclusions The thaw period occurring after sustained duration of freeze may causes significant stress variations and, consequently, additional breaks. About 6 percent of the damage records occurred in the months following extended periods of freeze records. The records did not show after-freeze damage when the duration of the freeze season was short (i.e., when all Freeze-Days occurred in less than 3 months of the winter season). *Note: Initial figures, may change with further work in other areas 31

32 Conclusions Termination of Winter Patrol: The survey plan may be suspended when the Freeze-Days cycles occur for a duration less than 3 months and are no longer present. When the Freeze-Days occur for 3 months or longer, the winter survey plan may be extended for one month after the Freeze-Days are no longer present. Example: A company-approved weather forecast shows 8 Freeze-Days in early December, 20 Freeze-Days from January to mid-february, and no Freeze forecasted in the following months. This results in a freeze duration of 2-½ months and the winter surveillance plan may be suspended by the end of February. If Freeze-Days are extended to the end of February, the freeze period is 3 months and the winter surveillance plan continues for one more month (i.e., to the end of March). 32

33 Tackling Important Energy Challenges and Creating Value for Customers in the Global Marketplace Paul Armstrong Director Business Development, GTI