Generation of non-stationary stochastic fields using Generative Adversarial Networks
This work addresses the challenge of generating conditioned geological fields for applications like reservoir modeling, though it appears incremental as it builds on existing GAN methods for stationary cases.
The authors tackled the problem of generating non-stationary geological facies using Generative Adversarial Networks (GANs), developing a spatial-conditioning method that produced geologically-plausible realizations with strong correlation to target maps beyond the training set.
In the context of generating geological facies conditioned on observed data, samples corresponding to all possible conditions are not generally available in the training set and hence the generation of these realizations depends primary on the generalization capability of the trained generative model. The problem becomes more complex when applied on non-stationary fields. In this work, we investigate the problem of using Generative Adversarial Networks (GANs) models to generate non-stationary geological channelized patterns and examine the models generalization capability at new spatial modes that were never seen in the given training set. The developed training method based on spatial-conditioning allowed for effective learning of the correlation between the spatial conditions (i.e. non-stationary maps) and the realizations implicitly without using additional loss terms or solving optimization problems for every new given data after training. In addition, our models can be trained on 2D and 3D samples. The results on real and artificial datasets show that we were able to generate geologically-plausible realizations beyond the training samples and with a strong correlation with the target maps.