A Reliability Model Repository for Real-Time Well Integrity Management in Oil and Gas Operations

Quantitative Risk Analysis (QRA) is essential for well integrity management, providing data-driven insights into current and future well conditions. This paper presents the Reliability Model Repository, a framework integrating multiple reliability data sources with real-time well monitoring to enhance operational safety and optimize performance. The repository employs statistical distributions, accelerated life-test models, structural reliability models, and machine learning to assess failure rates, track component degradation, and incorporate well characteristics, environmental conditions, and operational parameters. It also accounts for failure dependencies, such as cascading effects and common-cause failures (CCF), while supporting incomplete testing scenarios. Additionally, two tools are introduced: ReliaWell and WellRAMS. ReliaWell optimizes well design by balancing reliability, production potential, and cost.WellRAMS focuses on the production phase, enabling real-time reliability monitoring, failure tracking, and predictive maintenance planning. Together, these tools help minimize unplanned downtime, reduce operational risks, and optimize maintenance strategies.
A case study demonstrates the framework's application by analyzing the impact of real-time well conditions on the failure rates of critical components: the Downhole Safety Valve (DHV), Production Tubing, and Production Casing. Results show how the Reliability Model Repository and its tools improve well integrity predictions, ensure compliance with Brazil's ANP-SGIP regulations, and enhance operational safety. The study further explores advanced modeling techniques, such as estimating Remaining Useful Life (RUL) and analyzing degradation effects, offering a data-driven approach to well integrity management across the entire well lifecycle.

Keywords: Reliability model, Condition-based, Well integrity, Real-time monitoring.