Operations Coupled Virtual Learning for Reservoir Evaluation and Performance Analysis

Abstract

Abstract The quick and accurate evaluation of reservoir behaviors and responses is essential to achieve successful field development and operations. An emerging technology for field development, physics informed advanced artificial intelligence/machine learning (AI/ML) benefits from both physics-based principles and AI/ML's learning capabilities. The capacitance and resistance model (CRM) method, based on the material balance principle, can provide rapid insights for optimal operations. Its flexible time-window selection and testing capability are especially useful for operation planning and development. Advanced AI/ML models developed for virtual learning environment (VLE) can be coupled to extend and enhance the capability for reservoir evolution evaluation. The objective of this study is to synergize the CRM with the VLE to provide a comprehensive toolset for field operations and reservoir management. The proposed approach has an organic integration of the CRM with the VLE; after completing a rapid reservoir study, the CRM first performs rapid forecasting of the well responses and inter-well connectivity for any given injection situation. The forecasted results from the CRM are then supplied as the inputs to the VLE, which utilizes its ML models to predict the corresponding three-dimensional distributions of key reservoir parameters such as detailed pressure transient and fluid movement for the entire field. This information, together with the field data streams, can be used for decision-making by providing a holistic view of the field operations and reservoir management regarding the injection and production enhancement in a real-time fashion. A simulated reservoir test case based on the SACROC CO2 flooding dataset from West Texas was used to demonstrate the concept and workflow. The test case has shown that the CRM can accurately capture the variations of the production rates and bottom-hole pressures with injection and production plan changes. The responses obtained from the CRM enable the VLE to correctly predict the three-dimensional distributions of the pressure and fluid saturation. The joint force from the CRM and the VLE enable them to capture the effects due to the injection and production changes in the field. Capable of tuning the injection plan, production design, and optimizing reservoir response, this integrated toolset can also assist field design with optimal well location selection/placement as extended benefits. As demonstrated with the preliminary results from above, a comprehensive and integrated toolset that couples the physics with the AI/ML can provide dynamic and real-time decision support for field operations and optimization for de-risked operation support, enhance oil recovery, and CO2 storage/monitoring design. Successful development of such a toolset makes it possible to integrate what-if scenarios and multiple-realizations to the workflow for static and dynamic uncertainty quantification. The toolset shows value and potential for emerging "SMART" field operations and reservoir management with three to four orders of magnitude speedup.