Application of Deep Learning for Characterization of CO2 Leakage Based on Above Zone Monitoring Interval (AZMI) Pressure Data

5 Pages Posted: 5 Apr 2021

See all articles by Kristian Gundersen

Kristian Gundersen

University of Bergen - Department of Mathematics

Seyyed Hosseini

University of Texas at Austin - Gulf Coast Carbon Center

Anna Oleynik

University of Bergen - Department of Mathematics

Guttorm Alendal

University of Bergen - Department of Mathematics

Date Written: April 2, 2021

Abstract

CCS is an important component of worldwide strategy to tackle climate change. The CO2 will be captured and stored in subsurface reservoirs for a near future to help reducing the greenhouse gas emissions to the atmosphere. To ensure that the CO2 is retained within the reservoir, proper monitoring of CCS sites is necessary. Such monitoring is important for regulators, public perception, the environment and safety. Here we present a framework that uses deep learning methodology, based on reservoir simulations and information from Above Zone Monitoring Interval (AZMI) wells, to detect, localize and quantify potential CCS-leakages trough a Multi-Task-Learning (MTL) framework. To simulate subsurface CO2 movement and its effects on the pressure in the AZMI, we use a high fidelity reservoir model. In particular, in this study we use a heterogeneous 2D spatial reservoir model to simulate different leak fluxes and locations. The work presented here is a short summary of the work presented in the paper A Variational Auto-Encoder for Reservoir Monitoring available on arxiv.

Keywords: AZMI; Pressure Reconstruction; Classification; Bayesian Variational Methods, Semi Conditional Variational Auto-Encoders; Multi tasks Learning; CCS Monitoring

Suggested Citation

Gundersen, Kristian and Hosseini, Seyyed and Oleynik, Anna and Alendal, Guttorm, Application of Deep Learning for Characterization of CO2 Leakage Based on Above Zone Monitoring Interval (AZMI) Pressure Data (April 2, 2021). Proceedings of the 15th Greenhouse Gas Control Technologies Conference 15-18 March 2021, Available at SSRN: https://ssrn.com/abstract=3818244 or http://dx.doi.org/10.2139/ssrn.3818244

Kristian Gundersen (Contact Author)

University of Bergen - Department of Mathematics ( email )

P. O. Box 7803
Realfagbygget, Allégt. 41
Bergen, N-5020
Norway

Seyyed Hosseini

University of Texas at Austin - Gulf Coast Carbon Center ( email )

University Station, Box X
Austin, TX 78713
United States

Anna Oleynik

University of Bergen - Department of Mathematics ( email )

P. O. Box 7803
Realfagbygget, Allégt. 41
Bergen, N-5020
Norway

HOME PAGE: http://https://www.researchgate.net/profile/Anna_Oleynik

Guttorm Alendal

University of Bergen - Department of Mathematics

P. O. Box 7803
Bergen, N-5020
Germany

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