Machine Learning Applied to 3-D Reservoir Simulation

The optimization of subsurface flow processes is important for many applications including oil field operations and the geological storage of carbon dioxide. These optimizations are very demanding computationally due to the large number of flow simulations that must be performed and the typically large dimension of the simulation models. In this work, reduced-order modeling (ROM) techniques are applied to reduce the simulation time of complex large-scale subsurface flow models. The procedures all entail proper orthogonal decomposition (POD), in which a high fidelity training simulation is run, solution snapshots are stored and an eigendecomposition is performed on the resulting data matrix. A clustering procedure to reduce the size of the eigen-decomposition problem and the resulting number of degrees of freedom is also implemented. Extensive flow simulations involving water injection into a geologically complex 3D oil reservoir model containing 60,000 grid blocks are presented.