Accelerating ensemble-based well control optimization with ES-MDA data-space inversion framework

Advances in applications of reservoir management workflows have shown the value of closed-loop optimization in leveraging the learning from measurements gathered during operations to improve subsequent operational decisions. Closed-loop workflows rely on the combination of optimization and history matching procedures, which both can involve a very large number of reservoir simulations when employing ensemble-based methods for uncertainty quantification. This renders such approach unfeasible in many real-life applications which involve large-scale models. The problem is amplified in more advanced workflows where the closed-loop calculations must be repeated several times (e.g., value of information approaches to assess and optimize the effectiveness of monitoring strategies). Recent developments in direct forecasting techniques such as data-space inversion (DSI) have shown promising results to alleviate the computational burden associated with the generation of ensemble of simulated forecasts conditioned to measurement data and their use in optimization workflows. In this work, we present an implementation of the DSI framework using the ES-MDA method available within a mature open-source data assimilation tool suitable for large-scale reservoir applications. The developed workflow utilizes machine learning techniques to better handle the presence of non-linearities typical of real-life applications (e.g., well shut-ins) and also accounts for the variability of well controls to enable the use of the forecasts for well control optimization purposes. We demonstrate the workflow with two realistic synthetic case studies. In the first case, we illustrate the forecasting of the extent of the plume of CO2 injected in an aquifer reservoir. In the second example we couple the developed DSI framework to an ensemble-based optimization framework to optimize water injection rates in an oil-water reservoir based on the forecasts of cumulative production conditioned to production data. The outcome achieved with DSI is verified against the response of synthetic truth models to assess the validity of the approach. The results obtained confirm the potential of DSI as a suitable technique to enable the acceleration of closed-loop and monitoring design optimization workflows. Moreover, the coupling with an ensemble-based optimization framework opens up opportunities to extend its use to optimize other types of reservoir management and field development decisions. (C) 2022 European Conference on the Mathematics of Geological Reservoirs 2022, ECMOR 2022. All rights reserved.