2 research outputs found
Exemplar-based synthesis of geology using kernel discrepancies and generative neural networks
We propose a framework for synthesis of geological images based on an
exemplar image. We synthesize new realizations such that the discrepancy in the
patch distribution between the realizations and the exemplar image is
minimized. Such discrepancy is quantified using a kernel method for two-sample
test called maximum mean discrepancy. To enable fast synthesis, we train a
generative neural network in an offline phase to sample realizations
efficiently during deployment, while also providing a parametrization of the
synthesis process. We assess the framework on a classical binary image
representing channelized subsurface reservoirs, finding that the method
reproduces the visual patterns and spatial statistics (image histogram and
two-point probability functions) of the exemplar image
DeepFlow: History Matching in the Space of Deep Generative Models
The calibration of a reservoir model with observed transient data of fluid
pressures and rates is a key task in obtaining a predictive model of the flow
and transport behaviour of the earth's subsurface. The model calibration task,
commonly referred to as "history matching", can be formalised as an ill-posed
inverse problem where we aim to find the underlying spatial distribution of
petrophysical properties that explain the observed dynamic data. We use a
generative adversarial network pretrained on geostatistical object-based models
to represent the distribution of rock properties for a synthetic model of a
hydrocarbon reservoir. The dynamic behaviour of the reservoir fluids is
modelled using a transient two-phase incompressible Darcy formulation. We
invert for the underlying reservoir properties by first modeling property
distributions using the pre-trained generative model then using the adjoint
equations of the forward problem to perform gradient descent on the latent
variables that control the output of the generative model. In addition to the
dynamic observation data, we include well rock-type constraints by introducing
an additional objective function. Our contribution shows that for a synthetic
test case, we are able to obtain solutions to the inverse problem by optimising
in the latent variable space of a deep generative model, given a set of
transient observations of a non-linear forward problem.Comment: 25 pages, 15 figures, fixed typo