GANs improve 3D petrophysical model generation.
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5 results for “Petrophysics”
problem Generating accurate 3D petrophysical models for reservoir studies.
method Differentiable neural networks, content and perceptual losses.
result GANs effectively generate conditioned 3D pore and reservoir-scale models.
Unsupervised clustering of series using dynamic programming.
problem Clustering coherent segments in multi-variate series.
method Dynamic programming algorithm with constraints.
result Clusters of coherent segments identified using Waxman-Smits equation.
DeepFlow uses deep generative models to solve history matching problems.
problem Calibrating reservoir models with transient fluid data.
method Generative adversarial network for property distribution, gradient descent on latent variables, and additional well constraints.
result Solves history matching problems for synthetic reservoir models.
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Datasetcs.LG
Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one cl…
Stochastic seismic waveform inversion using generative adversarial networks as a geological priorphysics.geo-ph
Generative models improve seismic wave inversion for subsurface structure.
problem Inaccurate subsurface geological structures in seismic inversion.
method Combining GAN for geological priors with PDE solution for wave propagation, using approximate MALA sampling.
result Efficient Bayesian inversion yields diverse realizations of subsurface structures matching seismic observations.