Abstract This paper introduces a novel predictive, non-intrusive methodology for tubing integrity management that combines corrosion analytics with caliper simulation. By integrating real-time data on lift performance, nodal pressure, and fluid properties, the system estimates localized degradation rates and ranks tubing segments based on their failure probability. This approach enables early risk detection, providing operators with data-driven insights for targeted interventions and more efficient use of caliper logging. The primary outcome of this methodology is optimized intervention planning. By shifting from broad, periodic caliper logging campaigns to exception-based, data-informed monitoring, the frequency of site campaigns is significantly reduced, leading to enhanced operational efficiency and reduced production interruptions. Additionally, this approach supports the extension of tubing service life, while improving safety performance by minimizing the risk of unexpected well failures. The methodology has been successfully trialled by a major operator, with documented case study demonstrating its effectiveness and providing a robust validation of its predictive capabilities. The methods employed include the collection of real-time pressure and fluid data, as well as production parameters, to feed into a mechanistic corrosion-erosion model. This model simulates wall loss progression and generates virtual caliper profiles to predict the internal condition of the tubing. The ranking system, which scores tubing segments based on their failure risk, allows for focused surveillance and resource allocation. In turn, this optimization reduces the need for broad caliper logging efforts, resulting in considerable cost savings. In the results and observations section, it is demonstrated that the ranking system accurately identifies vulnerable tubulars, allowing for proactive intervention before catastrophic failures occur. The non-intrusive prediction method reduces the frequency of interventions and production interruptions, while continuous monitoring ensures early detection and supports better decision-making. The use of predictive modelling has shown to extend tubing service life and minimize late-stage integrity failures. Furthermore, a successful field trial validated the approach, which has been fully deployed with real-world case studies. This paper introduces a digital-first approach to well integrity management, blending corrosion forecasting with virtual caliper simulation. The predictive ranking system enables focused inspections, optimizing resource use while improving safety and reliability.
Onukwu et al. (Mon,) studied this question.