3D-GAN to Model Uncertainty and to Perform Effective History Matching of a Complex Turbidite Field Case

Abstract

Summary Modelling uncertainty of complex 3D geological fields can require several sophisticated geostatistical methods. Such methods include process like methods that mimic the physics of deposition to create more realistic geological models. Performing a full field history matching with such generative tools can be extremely challenging due to the large number of parameters that are used to generate each stochastic realization, the static data conditioning, and the complex and non-linear input-output relation. Ensemble data assimilation methods such as ESMDA (ensemble smoother with multiple data assimilation) are not directly applicable or can perform poorly due to the strong input parameter dependence and non-linearity. In this work we present some recent advances on the usage of GAN (Generative Adversarial Networks) to help solving the history matching problem when process like methods are used to generate multi-realizations to model the uncertainty of turbidite fields. GAN are deep learning generative models that are used in many AI applications. In history matching, using GAN to generate new geological models can help reducing drastically the input parameter space used to solve the inverse problem. In fact, to generate a new realization with a GAN we sample a random vector from a low dimensional independent Gaussian distribution (called the latent space). As a result, once the GAN is trained (using few thousands realizations generated with the process like methods), the inverse problem consists in having to invert only a few dozen independent parameters respect to a few millions dependent parameters in the original space. Usage of GAN for history matching of simple synthetic field cases has been discussed in previous works. However, to apply GAN on real field cases one needs to address several issues such as: - high number of cells of the input space (typical GAN architectures are build for 2D 128x128 images) - highly dependent input and non-linear input output relationship - inverse problem with multiple solutions To obtain accurate results in our real dataset we use state-of-the-art machine learning, optimization/inversion technique and HPC. More particularly we use an advanced architecture for the 3D GAN trained on multi-GPU and a very recent Bayesian optimization technique to solve the inverse problem and to find several possible solutions. A comparison with a more industry standard approach using ESMDA is also presented and discussed.