Adaptive Surrogate Estimation with Spatial Features Using a Deep Convolutional Autoencoder for CO 2 Geological Sequestration

Suryeom Jo, Changhyup Park, Dong-Woo Ryu and Seongin Ahn
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Suryeom Jo: Geo-ICT Convergence Research Team, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
Changhyup Park: Department of Energy and Resources Engineering, Kangwon National University, Chuncheon 24341, Korea
Dong-Woo Ryu: Geo-ICT Convergence Research Team, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
Seongin Ahn: Geo-ICT Convergence Research Team, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea

Energies, 2021, vol. 14, issue 2, 1-19

Abstract: This paper develops a reliable deep-learning framework to extract latent features from spatial properties and investigates adaptive surrogate estimation to sequester CO 2 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 CO 2 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.

Keywords: deep convolutional autoencoder; deep learning; spatial parameter; latent feature; surrogate model; data integration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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