A GPU-Based, Industrial Grade Compositional Reservoir Simulator

Abstract

Recently, graphics processing units (GPUs) have been demonstrated to provide a significant performance benefit for black-oil reservoir simulation, as well as flash calculations that serve an important role in compositional simulation. A comprehensive approach to compositional simulation based on GPUs had yet to emerge, and some questions remained as to whether the benefits observed in black-oil simulation would persist with a more complex fluid description. We present our positive answer to this question through the extension of a commercial GPU-based black-oil simulator to include a compositional description based on standard cubic equations of state. We describe the motivations for the formulation we select to make optimal use of GPU characteristics, including choice of primary variables and iteration scheme. We then describe performance results on an example sector model and simplified synthetic case designed to allow a detailed examination of scaling with respect to the number of hydrocarbon components and model size, as well as number of processors. We finally show results from two complex asset models (synthetic and real) and examine performance scaling with respect to GPU generation, demonstrating that performance correlates strongly with GPU memory bandwidth.