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Tuesday, October 26 • 12:00pm - 1:00pm
ML for Energy Transition

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Talk 1:End-to-End Approaches to Enhance the CO2 Capture - Cécile Pereira
Nanoporous materials can be used as solid adsorbents to capture CO2 from combustion flue gases or directly from the air using what is called a temperature swing adsorption (TSA) process. In this process, the gas containing the CO2 is injected into a gas (CO2 source) / solid (solid-sorbents material) contactor of the material where the pores of the material selectively adsorb the CO2. Once the adsorbent is saturated, a highly enriched CO2 gas stream is recovered by purging the contactor with a combination of heat and steam. If the right nanoporous material can be found, a cost-effective approach to CO2 capture may be achievable. ACO2RDS (Adsorptive CO2 removal from dilute sources) is a multi-year project to develop transformative solid-sorbent-based technologies for CO2 capture from dilute sources, specifically natural gas combined cycle (NGCC) power plant flue gas and atmospheric CO2 with direct air capture (DAC). In this presentation, we introduce the ACO2RD project and we review key state-of-the-art publications on the topic.


Talk 2: A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage - Hewei Tang
Fast assimilation of monitoring data to forecast the transport of materials in heterogeneous media has many important applications. Such applications include the management of CO2 migration in geologic carbon storage reservoirs. It is often critical to assimilate emerging data and make forecast in a timely manner. However, the high computational cost of data assimilation with a high-dimensional parameter space undermines our ability to achieve this goal.

In the context of geologic carbon storage, we propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast history matching -reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from observed pressure and CO2 plumes. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. Intelligent treatments are applied to bridge between quantities in a true 3D reservoir and a single-layer model underlying the workflow. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.


Talk 3: Monitoring of Microseismic for CO2 Sequestration - Bob Clapp
Monitoring of microseismic events is going to play an important role in evaluating CO2 reservoirs during injection. Since DAS fibers are installed down wells and are thus close to the microseismic events, they hold vast potential for high-resolution analysis of their continuously-recorded data.


However, accurately detecting microseismic signals in continuous data is challenging and time-consuming. DAS acquisitions generate substantial data volumes, and microseismic events have a low signal-to-noise ratio in individual DAS channels.


Herein we design, train, and deploy a machine learning model to automatically detect microseismic events in DAS data acquired inside a proxy to an injection well, an unconventional reservoir. We create a curated dataset of 6,786 manually-picked microseismic events. The machine learning model achieves an accuracy of 98.6\% on the benchmark dataset and even detects low-amplitude events missed during manual picking. Our methodology detects over 100,000 events allowing us to reconstruct the spatio-temporal fracture development accurately.

Speakers
avatar for Cécile Pereira

Cécile Pereira

Data Science & AI Research Scientist, TotalEnergies
Cécile Pereira is a research scientist in the digital domain, working for Total CSE, Data Science & AI team. Her current research focuses on the development of new products and materials. She is strongly involved in the computational chemistry project, and she is co-supervising the... Read More →
avatar for Hewei Tang

Hewei Tang

Postdoctoral Staff Member, Lawrence Livermore National Laboratory (LLNL)
Dr. Hewei Tang is currently a postdoctoral staff member in Lawrence Livermore National Laboratory’s Atmospheric, Earth, and Energy Division. She holds a Ph.D. degree in Petroleum Engineering from Texas A&M University. Dr. Tang serves as an Associate Editor of Journal of Petroleum... Read More →
avatar for Bob Clapp

Bob Clapp

Technical Director, Stanford Center for Computational Earth and Environmental Science
Dr. Robert “Bob” Clapp is Technical Director of the Stanford Center for Computational Earth and Environmental Science. He has been at Stanford University for two decades, during which time he has published dozens of articles and presented talks on a wide range of geophysical and... Read More →
avatar for Mauricio Araya

Mauricio Araya

Senior R&D Manager HPC & ML, TotalEnergies
Mauricio Araya is a Senior Computer Scientist and lead researcher working at TotalEnergies EP R&T USA. He is also lecturer with the Professional Science Master’s Program at  the  Weiss  School  of  Natural  Science  of  Rice University,  where  he  teaches  computational... Read More →


Tuesday October 26, 2021 12:00pm - 1:00pm CDT

Attendees (3)