2021 Ken Kennedy AI and Data Science Conference

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.