Stochastic Reservoir Simulation Using Neural Networks Trained on Outcrop Data

Abstract Extensive outcrop data or photographs of present day depositions or even simple drawings from expert geologists contain precious structural information about spatial continuity that is beyond the present tools of geostatistics essentially limited to two-point statistics (histograms and covariances). A neural net can be learned to collect multiple point statistics from various training images, these statistics are then used to generate stochastic models conditioned to actual data. In petroleum applications, the methodology developed can be a substitute for objects based-algorithms when facies geometry and reservoir continuity are too complex to be modeled by simple object such as channels. The performance of the neural net approach is illustrated using training images of increasing complexity, and attempts at explaining that performance is provided in each case. The neural net builds local probability distributions for the facies types (categorical case) or for petrophysical properties (continuous case). These local probabilities include multiple point statistics learned from training images and are sampled with a Metropolis-Hastings sampler which also ensures reproduction of statistics coming from the actual subsurface data such as locally varying facies proportions. P. 321