Magnetic Flux Leakage (MFL) is a robust technology with high data coverage. Decades of continuous sizing improvement allowed for industry-accepted sizing reliability. The continuous optimization of sizing processes ensures useful results in categorizing metal-loss features. However, the identified selection of critical anomalies is not always optimal; sometimes containing anomalies are dug up too early or unnecessarily and, in rare cases, false under-calls can take place.

 

Today, complex empirical formulas together with multifaceted lookup tables, fed by pull tests, synthetic data and dig verifications, machine learning, artificial intelligence, and, not least, human expertise are translating MFL signals into metal-loss assessments with high levels of success. Nevertheless, two important principal elements are limiting the possible MFL sizing optimization. One is the empirical character of the signal interpretation. The other is the implicitly induced data and result simplification.

 

The reason to go this principal route for many years is simple: it is methodologically impossible to directly calculate the metal-source geometry from the signals. In addition, the pure number of possible relevant geometries is so large that simplification is necessary and inevitable. Moreover, the second methodological reason is the ambiguity of the signal, defining the metal-loss-sizing target as the most probable solution. However, even under the best conditions, the most probable one is not necessarily the correct one.

 

The paper describes a novel, fundamentally different approach as a basic alternative to the common MFL‑analysis approach described above. A calculation process is presented, which overcomes the empirical nature of traditional approaches by using a result optimization method that relies on intense computing and avoids any simplification. Additionally, the strategy to overcome MFL ambiguity will be shown. Together with the operator, detailed blind-test examples demonstrate the enormous level of detail, repeatability and accuracy of this groundbreaking technological method with the potential to reduce the need for field verification of ILI-identified corrosion anomalies.


Concerted, Computing-Intense Novel Mfl Approach Ensuring Reliability and Reducing the Need for Dig Verification

Division / Sponsors: 
Pipeline Systems Division

Paper Proceedings: https://asmedigitalcollection.asme.org/IPC/IPC2020/volume/84447

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