Well Integrity engineers are commonly challenged with using limited resources, and even more limited data, when trying to identify which wells amongst their diverse well inventory may be prone to damage and failure, the mechanisms and influential factors responsible for the potential damage and failures, and the reason why certain wells may pose the greatest risk. Furthermore, these integrity engineers are often uncertain as to the parameters that should be tracked; what inspection methods should be conducted, in which wells and at what frequency measures should be taken; and how the asset risks can be adequately determined and relayed to management to prioritize near-term and future financial investments into well integrity and decommissioning cost centres.
In this paper, an approach and workflow are described on how the application of a combination of reliability and risk methods, parameter-based damage models and available field data can be used to develop a tool used by asset integrity and operations personnel to risk-rank wells by the probability of failure and associated consequences. Additionally, this paper illustrates how the approach and models developed are adaptable to both the damage mechanisms specific to the application and to the data and parameters that are currently being measured or readily obtained, or other related variables that can used as suitable proxy parameters. As experience and history build (adding to the understanding and prioritization of damage mechanisms and key parameters), and to improve estimated values of the associated probability of failure due to these mechanisms, the knowledge is fed back into the model to improve its predictive capabilities.
This paper also describes how the methodology was applied by a commercial SAGD operator to develop a subsurface isolation risk assessment tool that was tailored to their wells, their application conditions and the parameters that they measure. The types of static and dynamic parameters that this tool considers, including geologic, well design, construction and operational data, are also illustrated, as well as how the tool is being used to prioritize injection and production wells by relative risk. Illustrative examples of how well, pad and asset risks are being identified, rolled-up across the asset and summarized are presented, and how well integrity and risk metrics are being communicated within the company. Ongoing activities to continue to update and advance the risk-ranking model are also noted; in particular, potential opportunities to develop improved mechanistic and data-driven models and predictions of damage and failure likelihoods, based on pooled reliability data and information across the broader thermal recovery sector