  Oilfield development related decisions are made using reservoir
simulation-based optimization study in which different production scenarios and
well controls are compared. Such simulations are computationally expensive and
so surrogate models are used to accelerate studies. Deep learning has been used
in past to generate surrogates, but such models often fail to quantify
prediction uncertainty and are not interpretable. In this work, beta-VAE based
regression is proposed to generate simulation surrogates for use in
optimization workflow. beta-VAE enables interpretable, factorized
representation of decision variables in latent space, which is then further
used for regression. Probabilistic dense layers are used to quantify prediction
uncertainty and enable approximate Bayesian inference. Surrogate model
developed using beta-VAE based regression finds interpretable and relevant
latent representation. A reasonable value of beta ensures a good balance
between factor disentanglement and reconstruction. Probabilistic dense layer
helps in quantifying predicted uncertainty for objective function, which is
then used to decide whether full-physics simulation is required for a case.
