Development of a Digital Twin for Well Integrity Management in Underground Gas Storage Fields

Objective Digitalization is offering several chances to improve performance and reliability of Underground Gas Storage (UGS) infrastructures, especially in those sites where ageing would require investment improvement for maintenance and monitoring. In that context, well integrity management can benefit from the implementation of a well digital twin, integrated with real time monitoring. The work proposes a digital model of the well that can provide a valuable tool to analyse its non stationary states in order to evaluate the integrity of the barriers and its health state. Methods, Procedures, Process The key points on well integrity management are barriers testing/qualification and annular pressure monitoring, and in UGS operations it’s crucial the selection of the timing of barrier assessment and of diagnostic test execution to correctly evaluates the results. The digital model can provide a tool to help the well engineer to understand the health state of the well and to plan maintenance activities. It considers a physical model of the well composed by gas and liquid filled chambers in the annuluses and in the tubing case and all the potential leak paths that could connect the annuluses, the tubing case, and the reservoir to the external environment. Each chamber is modelled considering its mass and energy balance, while fluid resistances describe fluid leakage across the barriers. Appropriate models, selected according to the geometry and type of each well barrier, describe each fluid resistance. The input parameters are the well architecture, flowing tubing temperature and pressure and gas flow rate. The model provides pressure and temperatures trends and estimates of leak rates trends or annular liquid level movements during the observation time window. The fine tuning of the model of each well is carried out by seeking for the values of the parameters that best describe each single leak path, such as size and position of the leaking point, with a genetic algorithm. Results, Observations, Conclusions The model has been customised and validated over several wells, some of which with perfect integrity status and others with some integrity issues. Results showed a very good fit with field data, as well as high precision in identifying leak position and size. The tool can also be applied to forecast well behaviour after the application of mitigating action or to simulate the evolution of the leak. Example applications are the evaluation of the correct time to top up a casing with liquid or nitrogen or the effect on annular pressure of limiting withdrawal or injection flow rate.