ChristoHouston Energy Inc. (CHE INC.) Pipeline Anomaly Analysis By Liquid Green Technologies Corporation

Transcription

1 ChristoHouston Energy Inc. () Pipeline Anomaly Analysis By Liquid Green Technologies Corporation

2 CHE INC. Overview: Review of Scope of Work Wall thickness analysis - Pipeline and sectional statistics Feature Analysis and cross-modality comparisons Conclusions

3 CHE-LGT Advantages: Able to identify new at risk corrosion features in collected pipeline data using novel statistical methods to characterize depth, size, severity Utilize all multi-year data to obtain the highest accuracy and confidence using proprietary auto-alignment software and feature registration. Advanced statistical analysis and multi-year feature registration enables large scale spatial-temporal processing for 1000 s of km of pipeline Fine feature analysis provides predictive tool for issue tracking and corrosion classification

4 Typical Scope of Work: 1. The automatic alignment of pipeline sections across multiple data collection events from different time periods to within 1-2 samples (2.5-5mm) to enable highly accurate multiple year comparisons in selected areas 2. Review of previous analysis performed by other vendors within these areas of interest 3. Additional physical and statistical analyses for corrosion events within these area of interest that will provide new insights into the nature and dynamics of the corrosion events 4. Analyze Multi-year Pipeline Inspection data from Smart PIG Data Collection for Selected Areas in Pipeline FW2 5. Quality control of datasets, identification of changes in data collection and corrections as necessary to those datasets 6. Precision alignment of pipeline across multi-year datasets 7. Multi-year audit GE pipeline integrity reports change analysis and risk classification of features in selected areas. Confirmation of statistics to identify various corrosion event including pitting and scaling 8. Review of Rosen dataset 9. Analysis of selected corrosion events from Pipeline. 10. Analysis to provide delineation of the event its dimensions including depth, volume, and roughness along with selected statistical parameters 11. Analysis will provide classification of the corrosion event and potential mechanisms of growth.

5 Uncertainty of ID erosion and wall thickness using UT analysis Small pit identification and depth biases with both UT and MFL Determination of absolute depths of pits with uncertain wall thickness

6 Use Advanced Statistics to Reduce WT Uncertainty: LGT defines dropouts are unreliable measurements Dropouts are identified as statistically bad measurements that are well outside the mean well thickness or spike spuriously LGT calculated the location and percentage of dropouts across the pipelines by year Local dropouts patterns correlate with pipeline size, distance along the pipe, and roughness Incorporating dropout analysis will reduce uncertainty in wall thickness measurements as well as predictions and thus improve depth estimation

7 Smaller pipes have high dropout rates than larger pipes Smaller pipes dropout rates also tend to grow as one moves toward the end of the pipeline

8 GE measurements show high variance and varying trends in wall thickness, raising questions: What are the true variances due to measurement versus pipeline features? What is the predicted wall thickness? LGT identified statistically bad data (dropouts) and eliminated them from the wall thickness calculations Results in more statistically reasonable estimate of wall thickness (LGT)

9 LGT further applies a bias correction per-year to remove variations due to measurement tool setup in each year, and then estimates the average WT loss rate via a linear fit Results are (i) a corrected wall thickness (black line) and (ii) a linear fit to corrected line for annual WT loss estimation Estimated WT loss rate on average in Section 1100 from 2006 to 2011 was 3.74 mpy Actual measurements of removed section of pipe confirms the accuracy of

10 Residuals error have a Gaussian distribution, which is expected from random errors in the measurements Small number of outliers on the high end of the distribution likely due to local areas of excessive scaling

11 1. Model average WT across entire pipe for each year from 2006 to Develop bias corrections to average WT for each year. 3. Apply corrections to each individual pipe section for Fit a line to WT to predict 2012 average WT and compare to actual (corrected) WT for each section Result: Small residual (about +/- 3.2 mils)

12 WT prediction accuracy is approx mm (3.2 mils) and shows a much more consistent behavior in pipe sections that are near each other In this example, LGT analysis predicts a less severe loss rate than GE s in Section 1090, and it predicts similar loss rates to those of nearby sections.

13 Hypothesis: Dropout data is indicative of where measurements are hard (likely roughness LGT looked at the data immediately around the dropout data as well as the data away from the dropout data

14 The amplitude distribution of the entire pipe is compared to the amplitude distribution around the dropout data and away from the dropout data. The distribution around the dropouts have larger variance and a higher mean, likely indicative of increased roughness The distribution away from the dropouts have a smaller distribution and lower mean, indicating a more constant wall thickness measurement

15 Analysis of Feature A

16 Stand Off Distance Wall Thickness: (Red = Thin Profile:

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18 Yellow pixels represent dropout data. Median- Infilled image shows thinnest point at the end of the pipe Feature A

19 Yellow pixels represent dropout data around Feature A. Year 2009 exhibits the fewest dropouts in the data while Year 2012 exhibits the most dropouts in the data.

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21 Surface computed using Alignment procedure of all 7 years of data using proprietary method Local median by year and by 3X3 window to reduce variance and loss of data due to dropouts RESULT: A measure of the feature shape and profile assuming that it is static This profile is used to identify the deepest point for depth analysis.

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24 DEPTH ANALYSIS OF FEATURE A 1. Align data from Years 2006 to 2012 using proprietary method 2. Compensate for depth variations due to bias (previously described) 3. Compute median profile of Feature A across multiple years 4. Use median profile to identify deepest point 5. In the vicinity of the deepest point, a. Calculate local median (3 x 3) depth profile for each year b. Identify depth variation at deepest point as percentage of WT for each year c. Identify average of neighborhood around deepest point RESULT: Two feature depths as a function of (accurate) WT per year

25 DEPTH ANALYSIS OF FEATURE A Year WT Loss (GE) WT Loss Local (LGT) WT Loss, Deepest (LGT) 2006 (Unknown) 7.6% 8.2% 2007 Not Detectable 8.7% 16.8% % 36.9% 39.2% % 19.5% 29.0% % 33.3% 34.5% % 32.4% 34.9% % 33.7% 38.6% *Percentage computed from average WT for each year, which is decreasing at a rate of 3.5 mpy Additional 2012 Results: 26% (GE MFL), 26% / 41% (Rosen) 25

26 Confirms Feature Shape < Color Linescan > < Embossed

27 < Color Linescan > < Embossed

28 Overall shape may be biased by the way the tool combines MFL/UWD data May not be as critical as sentencing is not affected by circumferential width

29 FEATURE A: SUMMARY Used bias correction to accurately estimate average wall thickness over pipe Multi-year alignment of UT data to both locate deepest corrosion point and accurately estimate WT loss year-over-year (2012: 38.6%) Shape of feature consistently seen every year since 2006 from dropout characterization Both feature depth and shape show biases of other analyses (GE UT/Rosen CDP). LGT results are unbiased with tighter tolerances (e.g. higher confidence)

30 ESTIMATED RISK FACTOR (ERF) ASSESSMENT

31 ESTIMATED ERF DISTRIBUTIONS Normally, ERFs are calculated as point values. Measured data has noise; hence, ERF is actually a distribution We used the B31G sentencing equations to generate this distribution via Monte Carlo simulations given means and standard deviation values for Average Wall Thickness known from LGT analysis Remaining Wall Thickness known from LGT analysis Feature Length (weak dependence) Result: An assessment of the ERF error

32 LGT ERFs: Feature A: / Feature B: / The width of the ERF distributions provides an estimate of the confidence in the calculation

33 When can we start working for you? Phone: (877)