The most critical component of external corrosion prevention on pipeline is the protective coating system. The coating selection process can be extremely challenging due to the sheer number of manufacturers and products/options that are offered — often with limited performance data available. Relying solely on manufacturer’s recommendations or information can be problematic when the anticipated service environment has not been adequately characterized, application parameters not completely understood, and/or when there is a misunderstanding of the product’s capabilities. Although there are many test methods for evaluation of pipe coatings, there are no commonly accepted test protocols or acceptance criteria for selecting coatings. Moreover, laboratory based testing is often complicated, expensive, and rarely provides an accurate simulation of field conditions. Although in-house subject matter experts (SMEs) and/or independent coating specialists provide some confidence in coating selection, the diversity of background and experience between these experts frequently creates inconsistency in coating evaluations and can produce divergent or conflicting recommendations.

In this paper, an innovative approach is proposed to address these coating selection process challenges. The proposed approach incorporates a systematic analysis of critical material attributes within an expert environment, and applies established decision making techniques to the evaluation. Priorities are developed by structuring a hierarchy of criteria and eliciting technical judgment of company’s SMEs, stakeholders, and unbiased industry specialists. The Deterministic Analytic Hierarchy Process (d-AHP) is applied using pairwise comparisons for prioritizing coating products/options and achieving an optimal selection. The experts’ opinions can then be updated by technical lab-based results for a smaller selection of top ranked products. Laboratory tests would be expected to be completed annually based on smart selection of certain products and to ensure year-over-year consistency. This paper also presents a probabilistic approach that improves d-AHP in order to capture uncertainties in experts’ opinions and/or lab results through probabilistic AHP (p-AHP). Although this approach is not widely used within the pipeline industry, there is a potential opportunity to improve conventional approaches for selecting and approving coatings for pipelines based on a systematic/quantitative approach.

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