Digital Twin of an Intelligent Completion System for Anomaly Diagnostic/Prognostic

Intelligent completion systems (ICS) aggregate value to the oil extraction process, as they aim to increase the reservoir recovery factor. Formed by a set of valves, control and monitoring components, the system is subject to failures during adjustment manoeuvres, due to the high complexity of equipment's number and drive mechanisms, or extreme operational environmental conditions of temperature, pressure, flow and natural contaminants. For this reason, intervention actions to maintain the operational assurance of the ICSI are necessary, especially when advanced information and data about the health condition of critical components are provided through Prognostic Health Management (PHM), a maintenance approach that allows predictive analysis based on the operating conditions and health of the assets. From this perspective, the adoption of the Digital Twin (DT) technology proposed in this paper allows modelling the dynamic behaviour of the ICS in detecting interval control valve (ICV) anomalies. The approach involved the collection of operational data and parameters of the physical asset, followed by the creation of the Digital Model (MD) in dynamic/modular software for the hydraulic control of a producing well in the Brazilian pre-salt with three zones. Numerical validation, development of the diagnostic/prognostic Machine Learning algorithm and training were established in the off-Board Diagnostic (off-BD) structure, with integration into a database to capture the normal and failure states of the ICV. The implementation of the DT off-BD phase demonstrated promising results by enabling the identification of anomalies in the pressure profile correlated to the gradual opening of the ICV in three operating scenarios. In this sense, the DT will support decisions-based on system behaviour to predict failures and health management of undesirable events. Furthermore, it is expected to improve the analysis of deviation diagnosis, system integrity and symptom investigation, reliability, system health management and decision making.

Keywords: Intelligent completion, PHM, Digital model, Digital twin, Failure diagnostic.