Abstract
History matching large, complex fields has remained time consuming, and valid probabilistic uncertainty quantification has been a distant goal. Significant advances have been made recently in the development of improved sampling algorithms, efficient and accurate proxy models, and extremely high-performance forward simulation using GPUs and scalable algorithms. Combining these, we present a highly efficient workflow for robust probabilistic forecasts of a real field model with detailed reservoir characterization and over thirty years of production history.
The model used here describes a highly faulted offshore field produced from two platforms with 11 distinct sand sub-intervals and 22 major faults. Geostatistical methods were used to estimate each reservoir's static property distribution based on well logs. This static 3D model was upscaled and combined with reservoir engineering data and historical-interpretations to provide initial fluid distribution for use in flow simulation history matching and forecasts. Hamiltonian Markov Chain Monte Carlo techniques coupled with an efficient implementation of proxy models are used for uncertainty quantifications. A fast, highly efficient and scalable GPU-based reservoir simulator is employed for all forward simulations.
The complexity of the geological properties of the reservoir requires a large number of cells to adequately describe the dynamics of production, and many adjustable parameters are needed to characterize the uncertainties in the model. While the use of advanced sampling and proxies vastly reduces the number of detailed flow simulations needed, hundreds of forward simulations are still required to calibrate the proxies and production forecasts. The bandwidth and computational throughput provided by GPUs allow such simulations to be performed extremely fast using only a modest amount of hardware. Through this case study, we show that by coupling an advanced history matching, prediction and optimization tool with a fast GPU reservoir simulator, accurate probabilistic uncertainty quantification can be made practical even for large models.
The workflow presented here can potentially transform the way probabilistic uncertainty quantifications are performed on large real field models. It will lay the foundation for the next generation of fast and accurate uncertainty tools and workflows.
