Data Modeling Techniques for Pipeline Integrity Assessment: A State-of-the-Art Survey

Pipelines are economical and efficient modes of transporting oil and gas. Pipelines will inevitably confront various risk factors throughout their lifespan, which could lead to defects. Defects in pipelines can compromise the integrity of the pipeline systems and may result in catastrophic accidents. Thus, it is vital to conduct the integrity assessment of pipelines so that the safe operation of the pipelines can be ensured. Up to the present, widely used approaches for pipeline integrity assessment include defect characterization, growth rate prediction, and failure pressure evaluation. Although the theoretical developments of pipeline integrity assessment methods have yielded fruitful achievements and significantly benefitted industry practices, there is still a lack of a systematic review covering the whole process from data collection to model establishment of the pipeline integrity assessment. Therefore, a comprehensive review is conducted in this article on the pipeline defect integrity assessment from the data modeling perspective. First, the description of data required to construct pipeline defect integrity assessment models is presented, where the required data for modeling can be obtained from pipeline inspection measurements, monitoring sensors, testing experiments, etc. Then, different modeling techniques applied to pipeline integrity assessment are reviewed, which are classified into physics-based models, data-driven models, and multimodel fusion. Also, the advantages and limitations of these techniques are discussed. Finally, the possibility of applying the existing models to a digital twin of pipeline defect is explored. This article aims to provide a guideline for researchers to select optimal models according to data availability and research requirements, which can benefit the research community, as well as, the industry.