Understanding In-line Inspection (ILI) Accuracy: Key Factors & Challenges

Introduction

In-line inspection (ILI) is a critical tool for ensuring the integrity of pipelines, helping operators detect and assess defects such as corrosion, dents, cracks, and weld anomalies. However, the effectiveness of ILI depends heavily on inspection accuracy—how closely the reported measurements match the actual pipeline conditions.

Understanding ILI accuracy, the factors affecting it, and how to improve assessment reliability is key to making informed integrity management decisions.

Key Metrics of ILI Accuracy

In-line inspection tools are evaluated based on three primary accuracy metrics:

1. Detection Accuracy – The ability of an ILI tool to correctly identify defects.

2. Sizing Accuracy – How closely the reported defect dimensions (depth, length, width) match the actual defect size.

3. Location Accuracy – The precision of defect location relative to pipeline features (welds, girth welds, and other markers).

These metrics are typically expressed in terms of tolerance bands, such as:

• Corrosion Sizing Accuracy: ±10% of wall thickness

• Crack Depth Sizing: ±1 mm or ±10% of depth

• Axial Location Accuracy: ±0.2% of distance traveled

Factors Affecting ILI Accuracy

Several variables impact the accuracy of in-line inspection results:

1. Tool Technology & Sensor Type

Different ILI technologies offer varying levels of accuracy:

• Magnetic Flux Leakage (MFL) – Best for corrosion but has limitations in depth sizing.

• Ultrasonic Testing (UT) – More precise in depth sizing but requires liquid coupling.

• Electromagnetic Acoustic Transducers (EMAT) – Used for detecting cracks and stress corrosion cracking (SCC).

• Caliper & Geometry Tools – Measure dents, ovalities, and mechanical damage.

2. Pipeline Condition & Operational Factors

• Wall thickness variations can affect tool readings.

• Speed excursions (too fast or too slow) can cause data distortion.

• Debris and deposits may interfere with sensor readings.

• Pipeline bends and diameter changes impact data quality.

3. Calibration & Validation

• Pre-run calibration ensures sensor accuracy before deployment.

• Post-run validation through direct examinations (excavations, NDE) helps confirm ILI findings.

• Use of dig verification data to refine tool performance.

Improving ILI Accuracy in Pipeline Integrity Programs

1. Selecting the Right ILI Tool

Match the technology to the pipeline threat:

• For corrosion: MFL or UT

• For cracks: EMAT or UT

• For dents & mechanical damage: Geometry tools & strain analysis

2. Enhancing Tool Performance

• Ensure proper tool speed to avoid data dropouts.

• Use combination tools (e.g., MFL + caliper) for comprehensive assessments.

• Implement cleaning programs to remove debris before inspection.

3. Correlation with Field Verification

• Conduct direct examinations (excavations) for high-risk anomalies.

• Use probabilistic models to refine predictions.

• Compare multiple ILI runs to assess tool repeatability.

4. Continuous Improvement & Machine Learning

• Use machine learning algorithms to refine defect sizing.

• Incorporate historical ILI data to improve future assessments.

• Develop statistical confidence intervals for ILI predictions.

Conclusion

In-line inspection accuracy is critical for effective pipeline integrity management. By understanding the limitations of ILI tools, validating results with field data, and continuously refining assessment techniques, pipeline operators can make data-driven decisions to ensure pipeline safety and reliability.

As technology advances, machine learning, artificial intelligence, and enhanced sensor technologies will further improve ILI accuracy, reducing uncertainties and enhancing pipeline safety for the future.

Bibliography

• American Society of Mechanical Engineers (ASME). (2020). Impact Analysis of Inline Inspection Accuracy on Pipeline Integrity Management Programs. ASME Digital Collection. Retrieved from https://asmedigitalcollection.asme.org

• DNV GL. (2022). Inline Inspection & Integrity Management - Questions Answered. DNV. Retrieved from https://www.dnv.com

• Pipeline and Hazardous Materials Safety Administration (PHMSA). (2016). Report on the Use of In-Line Inspection Tools for the Integrity Management of Hazardous Liquid Pipelines. U.S. Department of Transportation. Retrieved from https://www.phmsa.dot.gov

• Office of Scientific and Technical Information (OSTI). (2005). Basic Performance Metrics of In-Line Inspection Tools. U.S. Department of Energy. Retrieved from https://www.osti.gov

• U.S. Department of Energy (DOE). (2022). Challenges for In-Line Inspection. Retrieved from https://www.energy.gov

The information provided in this blog is for educational and informational purposes only. While every effort has been made to ensure accuracy, this content should not be used as a substitute for professional engineering or integrity management advice. Pipeline operators should consult qualified professionals and verify data through field validation before making any integrity-related decisions.