TOWARDS THE DEVELOPMENT OF A FFS TOOL FOR THE INSPECTION OF CORROSION UNDER

INSULATION

Dr Yves Gunaltun, Petroleum Institute, Abu DhabiDyana Ambrose, Petroleum Institute, Abu DhabiPatrick Hivert, PLS, Abu DhabiGary Penney, FTI – Gary@fti-intl.com

OUTLINE

• Introduction• Objectives• Project Phases• Design of Experiment• Test Loop and Simulated Defects (Targets)• Data Collection and Interpretation• Results of the Study• Statistical Analysis• Key Findings• Closing Remarks

INTRODUCTION

• CUI remains a major problem for industry

• LRUT originally developed to detect CUI without extensive insulation removal

• LRUT is a screening technique which can detect corrosion

• Current commercially available systems cannot provide sizing information

OBJECTIVESProject Objectives:

• Independently benchmark performance of LRUT

• Measure POD for typical real-world condition

• Increase understanding of variables affecting performance of LRUT

• Develop LRUT to provide data for FFS

Presentation objectives:

• Share key findings, lessons learned and new insights

Principle

The Long Range Guided Wave Ultrasonic Technique (LRUT) was designed to

inspect long sections of piping from a single location.

• Ultrasonic waves induced into pipe body

and propagated along pipe segment

being inspected

• When these guided waves encounter a

defect or feature signals are reflected

back to the tool (transducer collar)

• These signals digitally captured and

processed on a laptop

• Time-of-flight for each signature is

calculated to determine distance from the

tool and the significance of the anomaly

• Transducer collar wired in octants to

determine position around circumference

OVERVIEW OF LONG RANGE ULTRASONIC TESTING

OVERVIEW OF LONG RANGE ULTRASONIC TESTINGAdvantages of LRUT

• Rapid surveying of long lengths of pipe • Detection of CUI without removing insulation except at the probe collar location • Cost reduction in gaining access to the pipes for inspection • The whole pipe wall (100% circumference) is inspected • Able to inspect inaccessible areas e.g. wall penetrations, road crossings, buried

lines • Test range typically up to 100m• Detection of internal or external metal loss

LRUT technology has been used to screen tubular section for material loss for more than 15 years.

COMMERCIALLY AVAILABLE LRUT SYSTEM

3rd generationTeletest Focus system

from Plant Integrity Ltd

Commercial systems available from:

Guided Ultrasonics LtdOlympusPlant Integrity LtdSouthwest Research Institute

4th generation Teletest Focus+ system from Plant Integrity Ltd

PROJECT APPROACH

• Design and construct test loop to benchmark performance of commercially available LRUT for detection of CUI

• Evaluate effect of insulation

• Repeatability study of LRUT test data

• Study to define target (defect)sizes

• Insertion of numerous simulated CUI targetsin test loop

• Collection of LRUT data from a number of test locations

• Collected data analysed by 9 experienced LRUT Level 2 operators (interpreters)

• Results analysed to determine POD and performance of LRUT system

Tests Loop Features

The test loop was built at Mussafah, UAE and was purpose designed for this study

• Material: Carbon steel• Pipe diameter 12”• Pipe thickness 9.53mm• Spool length of 12m• 120m test loop • 1.5D elbows• Painted with inorganic zinc

primer with epoxy topcoat

Fabricating the test loop

Test loop after construction

TEST LOOP AND SIMULATED CUI TARGETS

TEST LOOP AND SIMULATED CUI TARGETS

• Welds were inspected using PAUT and shown to be free of defects

• Weld cap heights were measured and recorded72m

4m

TEST LOOP AND SIMULATED CUI TARGETS

• Shape of defects was designed to simulate typical CUI defects

• Targets were created using manual grinding for high degree of control and practicality

Shape of simulated CUI targets in test loop

Around 160-200mm

Around50-150mm

SIDE VIEW

TOP VIEW

AXIAL DIRECTION

1.5-7.8mm

DESIGN OF EXPERIMENT

Stage 1 Experiments

• Effect of insulation evaluated• A single target progressively grown in size in >50 increments• LRUT data collected for each size increment• Results used to define sizes for final targets in test loop, from

not likely to be detected to readily detectable

Stage 1Experiments

Stage 2Experiments

LRUT A-scan without insulation –20kHz

LRUT A-scan with insulation –20kHz

EFFECT OF INSULATION ON LRUT RESULTS

Scanning of Artificial CUI Targets

Thickness gauge measurement of theparent pipe material

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Typical mapped target

TEST LOOP AND SIMULATED CUI TARGETS

Pit gauge measurement for mapping the targets

DESIGN OF EXPERIMENT

Stage 2 Experiments

• Numerous simulated CUI targets inserted in test loop• Targets offset around pipe circumference to avoid shadowing• All target sizes were recorded by detailed pit gauging• LRUT data collected from multiple test locations; provided to

9 experienced interpreters (all min. Level 2 LRUT operators)• A-scans for frequencies determined by LRUT software

Stage 1Experiments

Stage 2Experiments

Defect Detection for POD Study

• Test results were sent to specialists of 9 individuals for “Blind Evaluation” of the results

• Capability of the tools (probability of detection) was finalized

• Human impact on the results was evaluated

DATA COLLECTION AND INTERPRETATION

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‘RA

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Cross-sectional Loss (CSL), %

CUI Test Loop - 12"

RESULTS OF THE STUDY – DETECTABILITY

100% Detection (all interpreters): CUI flaws ≥ 3.49% CSL1.2% CSL flaw with 100% detection (further investigation required to explain)

STATISTICAL ANALYSIS - POD

Model Development

1. A standard binary logistic regression (logit) model (Hosmer & Lemeshow, 1989), with % CSL as a continuous covariate and the interpreter as a categorical factor. Both of which were found to have a very strong influence on the POD, at the 5% significance level.

2. A transformed covariate log (CSL) in place of CSL, as advocated in the Nordtestguidelines (Førli et al, 1998) for POD estimation. This is the same as the Log-Odds Model.

3. The approach suggested by Hosmer and Lemeshow (Hosmer & Lemeshow, 1989) showed that the reason for the poor fit of these standard logit models was non-linearity in the logit as a function of log (CSL).

4. Assuming that the POD increases monotonically with CSL, the LOGIT analysis suggests fitting a model of the following form:

ln 𝑃𝑂𝐷

1− 𝑃𝑂𝐷 = 𝐴𝑖 + 𝐵. log𝐶𝑆𝐿 − log𝐶 3

STATISTICAL ANALYSIS - POD

CSL, %

PO

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Predicted PODs vs % cross-sectional loss (CSL)

Fitted POD model versus % CSL and interpreter

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Mean PO D

Interpreter 6 (worst case PO D)

Predicted PODs vs % cross-sectional loss (CSL)

Mean fitted POD and the ‘worst case’ fitted POD

Variability by interpreter: 1.9 to 4.2% CSL for POD = 90%

STATISTICAL ANALYSIS - POD

Model Development

The main source of variability in the POD model is the interpreter. It is possible to quantify this variability by treating the ‘worst case’ POD in as a non-parametric tolerance limit (Owen, 1962). Using this logic, 4.2% CSL provides an estimate of the flaw size above which the POD exceeds 90% with 93% confidence.

ln 𝑃𝑂𝐷

1− 𝑃𝑂𝐷 = 𝐴𝑖 + 𝐵. log𝐶𝑆𝐿 − log𝐶 3

KEY FINDINGS

1. No detectable effect of insulation on LRUT data

2. Pipe wall temperature affected target detection: Variation in sound velocity and thus axial location calculations

3. The repeatability of LRUT data from multiple data collections from the same location was very good

4. Variability in performance can come from differences in interpretation of LRUT data between operators

KEY FINDINGS

5. Consistent reporting of flaws and features dependent on well prepared technique sheets and specification of reporting (‘call’) levels, with these being followed

6. Variability and scatter in analysis results were observed for targets with CSL <3.5%

7. Targets with CSL ≥3.5% were detected consistently by every operator/interpreter (100% detection)

8. Estimation of POD using statistical methods reported a worse case POD of 90% with 93% Confidence for CSL >4.2%

CLOSING REMARKS

1. Highly useful insights and findings were made which could improve performance of LRUT

2. The test loop in Abu Dhabi will be valuable for further R&D and for LRUT training

3. Development areas were identified that will allow LRUT to be deployed with increased confidence and performance

4. Factors and variables affecting LRUT results require further study to improve on or minimise their effect

5. Performing FFS directly using LRUT data requires improved ability to predict extent of CUI directly from LRUT data

ACKNOWLEDGEMENTSThanks to the following for funding this research project:

• GRC• Petroleum Institute, Abu Dhabi, UAE

• Thanks to Charles Schneider (TWI) for his assistance in the statistical evaluation of the data acquired.

Gary Penney, FTI – Gary@fti-intl.com – +971 50 415 0675