Adaptive Surrogate Estimation with Spatial Features Using a Deep Convolutional Autoencoder for CO2 Geological Sequestration

Abstract

This paper develops a reliable deep-learning framework to extract latent features from spatial properties and investigates adaptive surrogate estimation to sequester CO2 into heterogeneous deep saline aquifers. Our deep-learning architecture includes a deep convolutional autoencoder (DCAE) and a fully-convolutional network to not only reduce computational costs but also to extract dimensionality-reduced features to conserve spatial characteristics. The workflow integrates two different spatial properties within a single convolutional system, and it also achieves accurate reconstruction performance. This approach significantly reduces the number of parameters to 4.3% of the original number required, e.g., the number of three-dimensional spatial properties needed decreases from 44,460 to 1920. The successful dimensionality reduction is accomplished by the DCAE system regarding all inputs as image channels from the initial stage of learning using the fully-convolutional network instead of fully-connected layers. The DCAE reconstructs spatial parameters such as permeability and porosity while conserving their statistical values, i.e., their mean and standard deviation, achieving R-squared values of over 0.972 with a mean absolute percentage error of their mean values of less than 1.79%. The adaptive surrogate model using the latent features extracted by DCAE, well operations, and modeling parameters is able to accurately estimate CO2 sequestration performances. The model shows R-squared values of over 0.892 for testing data not used in training and validation. The DCAE-based surrogate estimation exploits the reliable integration of various spatial data within the fully-convolutional network and allows us to evaluate flow behavior occurring in a subsurface domain.

Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification

“…DCAE-based surrogate modelling has been implemented in [26,27]. Jo et al [26] developed a DCAE framework for the purpose of extracting latent features from spatial properties and investigating adaptive surrogate estimation to sequester CO 2 into heterogeneous deep saline aquifers.…”

“…DCAE-based surrogate modelling has been implemented in [26,27]. Jo et al [26] developed a DCAE framework for the purpose of extracting latent features from spatial properties and investigating adaptive surrogate estimation to sequester CO 2 into heterogeneous deep saline aquifers. They used a DCAE and a fully convolutional network for reducing the computational costs and extracting dimensionality-reduced features for conserving spatial characteristics.…”