  Predicting the spatial configuration of gas molecules in nanopores of shale
formations is crucial for fluid flow forecasting and hydrocarbon reserves
estimation. The key challenge in these tight formations is that the majority of
the pore sizes are less than 50 nm. At this scale, the fluid properties are
affected by nanoconfinement effects due to the increased fluid-solid
interactions. For instance, gas adsorption to the pore walls could account for
up to 85% of the total hydrocarbon volume in a tight reservoir. Although there
are analytical solutions that describe this phenomenon for simple geometries,
they are not suitable for describing realistic pores, where surface roughness
and geometric anisotropy play important roles. To describe these, molecular
dynamics (MD) simulations are used since they consider fluid-solid and
fluid-fluid interactions at the molecular level. However, MD simulations are
computationally expensive, and are not able to simulate scales larger than a
few connected nanopores. We present a method for building and training
physics-based deep learning surrogate models to carry out fast and accurate
predictions of molecular configurations of gas inside nanopores. Since training
deep learning models requires extensive databases that are computationally
expensive to create, we employ active learning (AL). AL reduces the overhead of
creating comprehensive sets of high-fidelity data by determining where the
model uncertainty is greatest, and running simulations on the fly to minimize
it. The proposed workflow enables nanoconfinement effects to be rigorously
considered at the mesoscale where complex connected sets of nanopores control
key applications such as hydrocarbon recovery and CO2 sequestration.
