# Modeling nanoconfinement effects using active learning

arXiv: https://arxiv.org/abs/2005.02587 · HTML: https://mlsanity.com/paper/2005.02587/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 7 of 7)

- [MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep Learning](https://mlsanity.com/paper/2102.03393/) (2021)
- [AdjointNet: Constraining machine learning models with physics-based codes](https://mlsanity.com/paper/2109.03956/) (2021)
- [Computationally Efficient Multiscale Neural Networks Applied To Fluid Flow In Complex 3D Porous Media](https://mlsanity.com/paper/2102.07625/) (2021)
- [Towards Robust Deep Active Learning for Scientific Computing](https://mlsanity.com/paper/2201.12632/) (2022)
- [Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model](https://mlsanity.com/paper/2205.14301/) (2022)
- [Predictive Scale-Bridging Simulations through Active Learning](https://mlsanity.com/paper/2209.09811/) (2022)
- [Neural Network Predicts Ion Concentration Profiles under Nanoconfinement](https://mlsanity.com/paper/2304.04896/) (2023)

## Abstract (arXiv)

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.