ut ili riverbottom feature assessment of challenging · pdf fileapproach for defect assessment...

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UT ILI Riverbottom feature assessment of challenging datasets Thomas Hennig, Johannes Palmer, Jan-Eric Teves, Christian Skodzik, Rosen Technology and Research Center, Lingen, Germany Abstract Intelligent inline inspection technologies are an established approach to investigate the condition of a pipeline system. Several technologies are available on the market. Pipeline operators typically would like to use inline inspection results to estimate immediate repair actions or operational boundary conditions for the future lifespan of the transporting system. This paper will deal with ultrasonic data gathered by ROSEN inline inspection tools, RoCorrUT. The authors present an optimized approach to calculate the Riverbottom profile commonly used for RSTRENG case 1 defect assessment. Especially for complex axially oriented internal anomalies this approach will increase the accuracy and reliability of pressure calculations. Case studies are elaborated in this regard on the basis of high resolution Automated Ultrasonic Test (AUT) field verification data. Also pressure calculations are performed to enlighten the different results of several new approaches. 1. Introduction Inline inspections (ILI) are an established option to receive reliable information about the status of a pipeline. Several technologies are available to provide a maximum of information with regard to the expected situation, e.g. history of the pipeline, previous inspection findings and fitness for purpose calculations. The primary outcome of such intelligent pipeline inspections are anomalies, typically characterized by their position and geometry. This information itself may be of interest for the pipeline operator, but does finally not help to draw accurate conclusions about the safety of the transporting system. The majority of pipeline operators are interested in predicted and calculated consequences with regard to operational pipeline safety of every single reported anomaly. The information delivered by the inline inspection company are only one aspect for these predictions. In the past several investigations and burst pressure experiments have been made to determine empiric relations between pipeline anomalies and the consequences for the safety of the pipe system. In the late 1960s first approaches to assess the integrity of pipelines were made and published. Based on investigations and a research project (AGA-NG18) the well known ASME B31G assessment code was developed (Maxey et al. 1971; Kiefner and Duffy, 1971). Here the defect geometry is approximated as an rectangle of length L and maximum depth d. Consequently, an overestimation of metal loss area compared to real defect geometry is typically observed. Several modifications have been developed, with regard to the defect geometry, the so called Folias factor and the yield strength. The details of these developments will not be discussed in the frame of this paper. Bjornoy and Marley (2001) give a comprehensive overview. The overall structure to calculate the maximum allowable pressure for a cylindrical pipe is as follows:


where Psafe is the maximum pressure, the flow stress, t the wall thickness, D

the outer diameter of the pipe, the so called Folias factor and one additional factor containing geometry information of the anomaly. Three of the developed approaches are available within the RSTRENG collection. They are all based on the original ASME B31G formula. 2. RSTRENG The length and maximum depth of the anomaly have major impact on the result of the assessment code B31G. The deviation between the parameterized or approximated anomaly profile (rectangle) and the real defect profile will have direct impact on the reliability of the pressure calculations. Small sized corrosion anomalies often have a typical profile and shape, which can be approximated, e.g. ASME B31G formula result with area d*L. This approach is also called RSTRENG –case 3. The so called case 2 uses a rectangle slightly reduced by 15%.

Figure 1: Riverbottom profile and approximation through rectangle of length L and depth d (white dashed line). For small sized shallow features this approximation might lead to acceptable results.

For anomalies with a more complex shape this approximation may differ significantly from the real defect shape. Consequently, another improvement is the introduction of the real, or at least measured anomaly profile (RSTRENG case 3 or Riverbottom). Therefore an algorithm was defined to collect for each single distance the minimum wall thickness reading of all samples in circumferential direction within the anomaly area (Kiefner and Vieth, 1989). This straight-forward approach is quite easy to apply on ILI UT-data and can also be performed in the ditch. It is a commonly used approach for defect assessment especially for ultrasonic inspection data due to the high sampling rate and direct measurement principle. Figure 2 depicts an example wall thickness dataset from an 8” pipeline. Severe corrosion at the bottom of the pipe (180 degree) and a shallower corrosion band at 12:00 o’clock is clearly visible (360 degree). The classic Riverbottom profile of the anomaly in Figure 2 (black rectangle) is shown below in Figure 3. The approximation using the ASME B31G code / RSTRENG case 3 is indicated through a white dotted rectangle. The differences between the rectangle and the real feature profile becomes obvious.


The RSTRENG case 1 algorithm does not only take the real anomaly profile into account to calculate the metal loss area. For the calculation of the maximum pressure the algorithm will identify the subset of the anomaly with the minimum safe pressure calculation. Depending on the overall profile only a short and deep subset of the profile may be used for the assessment. The outcome of the RSTRENG optimization process are so called effective area Aeff and effective length Leff values. These values are always smaller or equal to the overall defect length L and the rectangle defect Area A0. This subsection is shown in blue color in Figure 3. Compared to the classical ASME B31G code the RSTRENG case 1 (Riverbottom profile) provides the pipeline operator a more realistic and less conservative result for the assessment of the anomalies.

Figure 2: Wall thickness data of an UT inspection; 8inch line, @ 06:00 o’clock/180 degree deep corrosion and shallower continuously corrosion band @ 12:00 o’clock/360 degree.

Figure 3: Depth profile for boxed pipeline anomaly from Figure 2. White dotted box indicates the overall length and maximum depth (Case 3), whereas the effective Area and length are plotted in blue.

3. Challenging situations with real ILI data Another example of UT ILI data is shown in Figure 4. This anomaly is located in a seamless spool. Due to operational conditions during the inline inspection and corrosion characteristics itself the data quality is not optimal. Some noisy samples are visible despite the real anomaly. Figure 5 displays the standard Riverbottom profile as defined by the RSTRENG case 1 algorithm for the white anomaly box of Figure 4. The upper Figure 5 graph shows clearly the influence of the noise samples in the remaining wall thickness (RWT) data. The noise, often interpreted as reduced remaining wall thickness, will have major impact on the profile. The corresponding profile will have a non plausible shape. Consequently, the calculated effective length and effective area do not


represent the anomaly in a proper way. The overall outcome of the assessment is affected by the reduced quality input data. The different Psafe calculated values for this anomaly are summarized in Table 1. Figure 4: Wall thickness and Stand-Off data of UT inline inspection. Especially the WT data show several noisy samples. Figure 5 depicts the RBP result for the white box.

The bottom part of Figure 5 depicts a profile based on the Stand-off data only. As expected, the profile shows a relatively smooth and plausible shape. This profile is only affected by relatively few outliers. This approach is used since several years to provide river bottom profiles for internal corrosion for anomalies with a limited width, i.e. ensuring the sensor not to dive into the metal loss. Long axial corrosion bands or general thinning are most interesting metal loss geometries to use river bottom profiles. Unfortunately these are not suitable to be analyzed on SO data exclusively, because it is not ensured that the sensor does not dive into the general corrosion by following its contour - even if connected with neighboring carriers. The measured Stand-off values are still correct, but its absolute reference is lost. Only local corrosion within the general thinning band is taken into account. The assessment will consequently lead to optimistic results, by underestimating the absolute depth. Nevertheless the higher plausibility of the Stand-Off Riverbottom profile shape here can be used to crosscheck the RWT shape and remove outliers. I.e. although the SO data cannot be used standalone, the combination with the RWT data has potential to optimizes the combined shape.


Figure 5: (Top) Riverbottom profile according to original definition for white anomaly shown in Figure 4. The effect of the outliers is clearly visible. Maximum anomaly depth and profile shape are not plausible. (Bottom) Profile based on SO data.

4. Tested approaches Several ideas to overcome above shortcomings of the Riverbottom data evaluation and how to generate reliable profiles for pressure calculations are tested in this section. 4.1 Filter on Profile Depending on the pattern and origin of the noise in ultrasonic wall thickness readings the generated Riverbottom profile could be filtered. E.g. a median filter with a certain filter length on the Riverbottom profile can be applied to the profile. The Riverbottom profile will be more reliable with regard to scattered wall thickness data. However a classical median filter will decrease the maximum feature depth and therefore the overall assessment of this anomaly may be changed in an not required way. This will lead directly to inconsistencies between reported anomaly depth and Riverbottom profile. The generated Riverbottom profile is shown in Appendix C. The maximum anomaly depth (75%, resp. 9mm) according to the RBP does not reflect the reported depth of approximately 24%, resp. 3mm. 4.2 Maximum depth approach During the analysis of the complex inline inspection data experienced analysts use state of the art algorithms to determine the characteristic values of every single anomaly. Additional to other parameters length, width, type, surface location and depth are determined for each anomaly.


The calculated minimum remaining wall thickness reading for each single anomaly will be used as the lower level for the Riverbottom profile. Only wall thickness values equal or above the determined wall thickness reading are fed to the Riverbottom profile. In detail, the algorithm will check first if the reading is greater or equal to the minimum wall thickness at selected distance. If the condition is not fulfilled a new search for the 2nd deepest wall thickness reading is performed within the anomaly. This procedure is repeated until the initial condition is fulfilled. If no wall thickness reading can be determined which fulfills the condition, the Riverbottom profile will not get a value at that specific distance. Therefore the algorithm does only use measured wall thickness data for Riverbottom profile generation. No interpolation or extrapolation is performed. The effect of this approach is visible in Figure C1 (Appendix). The new Riverbottom profile shows a relatively smooth and plausible profile.

4.3 Combination of Filter and Maximum depth approach Both maximum depth and (median) filter approach can be combined. The order of both steps has essential influence on the result. The different results are shown in Figure C1 (Appendix). The 2nd last plot shows the result of a median filtered wall thickness dataset and afterwards a RBP generation with depth criterion. The last plot depicts the changed order: RBP generation on original wall thickness data with depth criterion and afterwards a median filter on the RBP. Pipeline operators requested several times to apply classical filter on ILI data and profile data. The authors hesitate to recommend the use of classical filter approaches, neither on ILI data nor on profile data. The above examples show the effects of such approaches. The resulting pressure calculations are summarized in Table 1. The above described simple approaches does not fulfill for all cases the expectations of inspection company and pipeline operator. Especially for internal corrosion bands, often observed in offshore pipelines around the bottom of the pipe, this approach might not deliver sufficient results. 4.4 Generalized Riverbottom profile approach Inspection data with internal anomalies can be processed in a different way to achieve reliable Riverbottom profiles. This generalized approach uses wall thickness and Stand-Off data. The overall idea is to use a temporary dataset WS to determine reliability or confidence level of each sample:

The sum of wall thickness and Stand-Off data, WS, is calculated for each sample.

Determination of wide corrosion (bands), e.g. channeling corrosion on Stand-Off and wall thickness data and correction of Stand-Off signal.

Stand-Off data is normalized to consider variations of tool/transducer fabrication tolerances which are directly visible in stand-off data.

A reference WS value for each channel from the undisturbed surrounding of the anomaly is determined.


Comparison of every WS sample with reference value and determination of confidence level.

Generation of optimized dataset, several possibilities are possible: o Use original measured wall thickness o Use Stand-Off information to determine optimized wall thickness o Use plausibility rules and determine optimized WT data (neighborhood) o Define sample as defect and do not use for further steps

The maximum allowable tolerance for the above described comparison between reference WS and measured wall thickness sample depends on several input parameter as wall thickness variation (fabrication tolerance), sensor, inline inspection tool, operating conditions and the general accuracy of measurement system. The allowable tolerance should be adjusted for each inspection dataset. Figure 6 depicts an comprehensive overview about the above described workflow. Figure 6: Schematic workflow for the ‘Generalized Riverbottom profile’ approach. At two different stages external information and plausibility conditions are fed to the algorithm to achieve reliable profiles.

Wall thickness

data

Stand-Off

data

WS (WT + SO)

Additional

information

Pipe, ILI tool,

accuracies

Confidence

Matrix

Optimized

dataset

Plausibility

assessment

(Maximum Depth,

shape)

Riverbottom

profile

Table 1: Safety pressure and ERF values for anomaly of Figure 4.

Classic RBP

(section 3)

SO RBP

(section 3)

Classic & Depth

(section 4.2)

Median (RBP)

(section 4.2)

Opt. RBP

(section 4.4)

RSTRENG 1 Psafe

(MPa)

12.87 15.86 15.91 12.66 15.86

ERF 1.27 1.03 1.02 1.29 1.03


As a conclusion for the feature of Figure 4 we can state that the median filter approaches do not provide plausible results. Whereas the approach of plausibility, (1) the maximum depth criterion and (2) the optimized dataset based on wall thickness and stand-off information of neighborhood of every single sample yields to plausible results. 5. Results Figure 7 depicts the wall thickness data of internal corrosion anomalies from a 16” spool. The data was gathered by a third party company using a high resolution AUT device. The data shows several small sized corrosion anomalies. The following investigation is focused on a small subset of this dataset indicated by the white rectangle in Figure 7. Figure 7: Overview of wall thickness AUT measurements

Figure 8 depicts the corresponding ILI data. Wall thickness on left side, Stand-Off on right side. Both datasets show some noisy samples. As described in chapter 3 the classic Riverbottom algorithm will not generate a plausible profile. Figure 8: ILI data of 16” spool, wall thickness and Stand-Off data


According to the above described procedure, Figure 9 depicts the calculated WS dataset (left) and the optimized dataset (right). Compared with the original wall thickness data in Figure 8 the data shows a reduced noise level. The Riverbottom profile generated on the optimized wall thickness dataset is shown in Figure 10 on the top. In contrary to the ILI based Riverbottom profile the high resolution AUT data will result in the RBP shown in Figure 10 at the bottom. The differences between both Riverbottom profiles are small. Corresponding pressure calculations are summarized in Table 2. Figure 9: ILI data of 16” spool: WS data and optimized dataset

Table 2: Calculated pressures based on different RBPs for ILI and AUT data

Classic RBP Opt. RBP Classic AUT

RSTRENG 1 Psafe (MPa) 10.47 12.84 12.84

ERF 1.17 0.96 0.96

Table 2 gives an impression of the generated pressure values for different optimization approaches in comparison to the assessment performed on external AUT data. The optimized approach yields to identical pressure values as the high sampled AUT based assessment. Figure 10: RBP data for 16” spool: profile from generalized approach (top) and classical profile based on high resolution AUT measurements (bottom)


In addition to the 16” spool data, Figure 11 depicts the optimized Riverbottom profile for the anomaly from Figure 4 and 5. Figure 11: RBP according to generalized approach for wall thickness data shown in Figure 4 and 5.

Two further examples with pressure calculations are available in the appendix A1. For these two anomalies high resolution lab measurements are available. The corresponding Riverbottom profile as well as the saftey pressures based on the lab profile are calculated. All values are summarized in Figure 12. The deviation between the classic appraoch on AUT/lab data and both classic ILI and the optimized ILI approach is shown. The classic approach lead to the most conservative values. The optimized approach yields to values between the classic and the AUT assessment . 6. Conclusion The authors presented an approach to generate Riverbottom profiles for RSTRENG case 1 pressure calculation, especially for extended internal anomalies. The general shortcommings of RBP generation based solely on Stand-Off data is compensated with this procedure. Additionaly effects of datasets with reduced quality, typically caused by operational conditions or corrosion characteristics can be minimized. The described approach uses a combinationof wall thickness and stand-off data and plausibility aspects to prepare a temporary dataset for the Riverbottom generation. Additional information, as pipeline tolerances, tool specification and operating conditions are taken into account. Compared to high resolution AUT/lab measuremnts the authors could show that the optimized approach yields to similar but still conservative values for the assessment.


Figure 12: Comparison of deviation between Lab data assessment , classical and optimized Riverbottom approach

5. Abbreviation A width (circumferential extent) AUT Automated Ultrasonic Testing flow stress

Psafe Pressure until which the system is safe Aeff effective area (RRSTRENG case 1) Leff effective length (RRSTRENG case 1) t wall thickness of pipe D outer diameter of pipe MAOP Maximum operable pressure SMYS specified minimum yield strength ERF estimated repair factor WS Wall thickness plus Stand off WT wall thickness SO Stand-Off RBP Riverbottom profile RWT Remaining wall thickness 6. Acknowledgments This work would not have been possible without the enormous engagement of the European pipeline operator, contributing with valuable and determined discussions and the presented thorough, beneficial and costly field verification data. Also the engaged cooperation and contribution of our colleagues in our Dutch Operational company, specifically the data evaluation department was an essential prerequisite to enable this paper.

90,0

95,0

100,0

105,0

110,0

115,0

120,0

125,0

16" spool 6" - 1 6" - 2

ER

F /E

RF

(Lab

) (%

) ClassicApproach

optimizedapproach

Lab data


7. References Bjornoy O.H. and Marley M.J. (2001): “Assessment of Corroded Pipelines: Past, Present and Future”, Proc. Of the Eleventh International Offsohre and Polar Engeneeing Conference, Norway, June 17-22, 2001 Kiefner J.F. and DuffyA.R. (1971): “Summary of Research to Determine the Strength of Corroded Areas in Line Pipe”, Presented to a Public Hearing at the U.S. Department of Transportation, July 20, 1971 Kiefner J.F. and ViethP.H. (1989): “A modified Criterion for Evaluating the Remaining Strength of Corroded Pipe , RTSTRENG”, Project PR 3-805 Pipeline Research Committee, American Gas Association, Dec 22 1989 Maxey W.A, Kiefner J.F., Eiber R.J and Duffy A.R. (1971): “Ductile Fracture Initiation, Propagation and Arrest in Cylindrical Vessels”, Fracture Toughness, Proc. Of the 1971 National Symp. on Fracture Mechanics, ASTM STP 514


Appendix Appendix A – 6” pipeline Figure A1: Original and optimized wall thickness data for RBP generation according to generalized RBP approach

Figure A2: AUT wall thickness data

Figure A3: top: original RBP from ILI data, bottom: RBP according to generalized approach for wall thickness data shown in Figure 4.


Figure A4: classic RBP on AUT data

Table A1: Calculated pressures based on different RBPs for ILI and AUT data

Classic RBP

Classic &

Depth

Opt. RBP Classic AUT

RSTRENG 1 Psafe (MPa) 19.87 20.93 20.93 21.94

ERF 1.12 1.06 1.06 1.02

Appendix B – 6” pipeline Figure B1: Original and optimized wall thickness data for RBP generation according to generalized RBP approach

Figure B2: AUT wall thickness data


Figure B3: Riverbottom profiles. Top: classic on ILI data, mid: optimized approach, bottom: classic approach on AUT data

Table B1: Calculated pressures based on different RBPs for ILI and AUT data for anomaly shown in Figure

Classic RBP

Classic &

Depth

Opt. RBP Classic AUT

RSTRENG 1 Psafe (MPa) 18.67 19.31 19.34 20.21

ERF 1.19 1.15 1.15 1.10


Appendix C Figure C1: (a) Depth criterion; (b) Classic RBP with median filter of length 3; (c) median(WT) & depth criterion; (d) median filter on RBP including depth criterion

ut ili riverbottom feature assessment of challenging · pdf fileapproach for defect assessment...

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