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.

Summary Underground gas storage (UGS) wells are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity. The digital twin used for this application presents advantages compared with other solutions present on the market with regard to the simplified configuration; that is, with minimal input data, the system can produce an accurate and useful output, which is then used in the well integrity decision-making process. UGS wells present additional criticalities with respect to normal production wells due to their longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for the safe management of the well barriers. The proposed digital twin simulates the trends of pressure and temperature within each annulus and compares results with data from the field, allowing the identification of the position and size of leaks. A genetic algorithm is applied to optimize the placement of leaks on their specific barriers. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned. The studies presented show how the digital twin has been used on two wells with similar problems. At first, it has confirmed the necessity to put the well out of service as opposed to planning maintenance, thereby saving both time and cost. In the second case, it allowed the validation of a solution that led to a 60% reduction in failure consequence, allowing the well to continue operating without major costs or risks. The errors of the resulting simulations were always confined within the 0.5 bar limit highlighting its accuracy. The system has been in use for over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcases a clear readiness for predictive capabilities aimed to select, plan, and design fit for purpose mitigating actions.

Underground gas storage (UGS) are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. Today, with the advancement of continuous remote monitoring and digitalization, evaluating the integrity of UGS wells has become quicker and more efficient. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity. UGS wells present additional criticalities with respect to normal production wells due to its longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for a safe management of the well barriers. The proposed digital twin has been developed using material and energy balances and considering each annulus as a separate control volume. Each control volume can exchange heat and mass through predesigned barriers. Simulating evolution in time of pressure and temperature in the control volumes., and comparing results with data from field, allows the identification of position and size of leaks. A genetic algorithm is applied to optimize placement of leaks on their specific barriers. The system aims to identify the position and dimension of possible leaks by matching historical pressure, temperature, and flow data. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned. The system has been in use for little over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcased a clear readiness for predictive capabilities aimed to select, plan and design fit for purpose mitigating actions. This paper highlights the power that a digital twin can present leveraging field data with advanced algorithms. The paper also showcases workflows that allow convenient, efficient, and timely evaluation of well integrity, which leads to safer operating conditions and lower operational costs.