Generative Adversarial Networks to incorporate the Training Image uncertainty in multiple-point statistics simulation

Abstract

Multiple-point geostatistics (MPS) algorithms have been extensively used for geostatistical reservoir modeling. These techniques are known for building geostatistical simulations with curvilinear features, which are crucial for applications in flow simulation. One challenge in using MPS algorithms is obtaining a representative Training Image (TI). The result is that often one TI is used to condition the entire set of geostatistical simulations, and the TI uncertainty is disregarded. If the TI is uncertain, this uncertainty should be considered in the geostatistical simulations. Otherwise, the spatial uncertainty represented by the geostatistical simulations is underestimated. Incorporating the TI uncertainty requires building a data set or catalog of plausible TIs. In this context, we propose to use Generative Adversarial Networks (GANs) to build this catalog of TIs prior to the MPS simulation. Each TI generated by GAN is used to condition one realization built by the chosen MPS algorithm. The proposed workflow combines the strength of GAN to generate TIs with the strength of MPS algorithms to consider several sources of information to condition the simulations. A case study is shown, where the methodology is compared against the traditional MPS workflow, which uses a single TI. The results show that the proposed method successfully incorporated the TI uncertainty into the geostatistical workflow. The proposed method built numerical models with higher uncertainty and variability and, thus, provides a more realistic quantification of the space of uncertainty.