Transitioning a Legacy Reservoir Simulator to Cloud Native Services

Abstract

The digital transformation journey provides new opportunities for running simulations through cloud computing, with flexibility in hardware resources and availability of a wide array of software tools that can enhance decision-making. This work reviews the benefits of running reservoir simulations in a cloud environment and demonstrates the efficiency and cost savings. Additionally, a workflow for uncertainty analysis and history matching that integrates data analysis and machine-learning tools is presented. First, the hardware architecture must be designed to meet parallel reservoir simulation needs: significant message passing occurs between computer nodes, and for satisfactory performance, these nodes must be connected by a low-latency network, rather than be randomly located. Second, to ensure portability and easy replication across multiple cloud sites and platforms, the software performing the simulations must be containerized. Third, to reduce the time required to start a new simulation run, the Kubernetes platform is used to optimize resource allocation. Finally, reservoir simulation in the cloud is no longer merely the running of the simulation model, but it is integrated with data management and data analysis tools for decision-making. The cloud-based simulation services discussed herein exhibit good results during scale up, when a simulation operation requires a larger number of central processing units and/or greater memory, and also during scale out, when thousands of operation scenarios are necessary for history matching. The "pay as you go" pricing model reduces the time and capital costs of acquiring the new computing infrastructure to nearly zero, and the effectively unlimited scale-out capability can reduce the elapsed time for history matching by 80%. The availability of data centers in different regions is good for team collaborations. It serves the data management tool well to track history data, perform data mining, extract more information, and make decisions. Compared to traditional reservoir simulation, the cloud-based reservoir simulation software as a service model simplifies the process and reduces hardware acquisition and maintenance costs. Integrating intelligent data analysis with simulation helps quantify the uncertainty in the model and enables improved decisions.