A conditional GAN-based approach to build 3D facies models sequentially upwards
by Chao Sun, Vasily Demyanov, Daniel Arnold published in Computers & Geosciences Volume 181, December 2023, 105460
extends the earlier GAN papers and presents an alternative way of simulating 3D facies models using conditional generative adversarial networks (GANs) by reconstructing the 3D volume upward with customised thickness from generated 2D slices. To mimic natural fluvial reservoirs, GANs need to learn the complicated facies’ lateral and vertical sequences, connectivity patterns and spatial correlation, e.g. the distribution of channel deposits and lateral accretion packages, including channel lags and point bars in a meandering fluvial system. 3D GAN modelling remains challenging, despite some success in 3D, due to the increased data and GAN model complexity caused by the 3D training data. For example, a 3D facies model with a larger number of voxels introduces more complex geobody shapes and requires 3D convolutions that increases the computational burden compared to 2D convolutions.
Our proposed conditional GAN-based framework predicts 2D upper layers slice by slice to build 3D facies models, mimicking the sedimentary process in a purely aggrading system, which is less computationally costly than generating the facies models used as training data and generating facies models directly in 3D from a GAN. Each upper layer is predicted conditionally to a lower layer that contains the facies distributions in all layers below the target using several unique training enhancements. All those methods help prevent the simulated 3D models from gradually losing realism and the preset sedimentary settings vertically while inferring the plausible evolution of the channel system. The results demonstrate that the trained conditional generator, a SPADE generator, can work as a simulator to produce unconditional 3D meandering fluvial facies models by inputting a user-defined spatial correlation strength between layers and the number of layers needed (thickness in metres) without post-processing or retraining. This framework’s performance was evaluated using a connectivity metric towards the range of avulsion scenarios that represent a range of depositional uncertainty.