Safe and efficient carbon capture and storage

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Left: Underground CO2 storage. Right: CO2 migration model in a digitized rock sample obtained from a pore-scale two-phase flow simulation. The simulation was performed on the Frontera supercomputer. Credit: Sahar Bakhshian, University of Texas at Austin

University of Texas researcher identifies safe and efficient carbon capture and storage factors.

The road to a stabilized climate is difficult and controversial. A number of solutions will be needed to enable a quick and equitable transition away from fossil fuels: among them the development of sustainable energy sources, greener materials and methods to remove CO₂ from the atmosphere.

One of the removal methods scientists are exploring is known as carbon capture and storage (CCS). In carbon capture and storage, CO₂ is captured from industrial sources and injected into deep underground geological reservoirs, theoretically for thousands of years, much like water is stored in aquifers.

Sahar Bakhshian, a researcher at the University of Texas at the Austin Bureau of Economic Geology, recently used supercomputers at the Texas Advanced Computing Center (TACC) to fundamentally understand how CO₂ storage works at microscale pores in rock, and to determine characteristics and factors that can help optimize the amount of CO₂ that can be stored.


CO2 flows inside the pore space of a millimeter-sized rock sample, which is initially filled with brine. This high-resolution fluid dynamics simulation demonstrates the migration pathway of CO2 when injected into saline reservoirs. Credit: Sahar Bakhshian, University of Texas at Austin

Write in the International Journal of Greenhouse Gas Monitoring in December 2021, she explored CO₂ scavenging efficiency by dissolving the gas in resident brine in saline aquifers.

“We tried different scenarios – using different injection rates and fluid rock properties – to determine how the properties affect the percentage of injected CO₂ that can ideally be trapped by the dissolution mechanism,” she explained.

She found that two factors had a big impact on the amount of CO₂ that could be stored in the spaces inside rocks: wettability (or how well CO₂ molecules stick to the surface of the rock); and injection rate (the rate at which supercritical CO₂ is pushed into the tank).

Deep Learning Geological CO2 storage sites

A proposed physics-guided deep learning framework for anomaly detection in soil gas data at geological CO2 storage sites. Credit: Bakhshian, S., & Romanak, K. (2021). Environmental Science and Technology, 55(22), 15531-15541

Another effective process that provides safe storage of CO₂ is capillary entrapment, which occurs when CO₂ pinches and becomes immobilized in the pore space by capillary forces. In a study published in Progress in water resources in April 2019, Bakhshian presented the results of pore-scale biphasic flow simulations that used digital versions of real rocks from a CO₂ storage test site in Cranfield, Mississippi, to explore how CO₂ migrated through the porous structure of the rock during the injection step and how it can be trapped as immobilized drops in the porous space during post-injection.

Bakhshian’s work is carried out under the auspices of the Gulf Coast Carbon Center (GCCC), which has been working to understand the potential, risks and best methods of geological carbon storage since 1998.

According to Bakhshian, supercomputers are one of the main tools available to geoscientists to study processes related to carbon capture and storage. “Computational fluid dynamics techniques are essential for this field, to better select suitable target reservoirs for CO₂ storage and to predict the behavior of CO₂ plumes in these reservoirs,” she said.

Sahar Bahkshian

Sahar Bahkshian, Research Associate in the Office of Economic Geology, Jackson School of Geosciences, Credit: University of Texas at Austin

Understanding the dynamics of pore-level storage capacity through high-performance computer simulations provides insight into how carbon capture and storage could be achieved at scale.

“Our research is essentially trying to characterize the geological parameters suitable for storage and exploring how we inject CO₂ to ensure it is safe, efficient and poses no threat to people or groundwater resources,” Bakhshian said.

Another aspect of Bakhshian’s research involves using machine learning techniques to develop fast computational models that can estimate reservoir storage capacity and aid in environmental CO₂ monitoring.

write in Environmental science and technology in October 2021, Bakhshian proposed a deep learning framework for detecting anomalies in data from soil gas concentration sensors. The model was trained on data acquired from sensors used for environmental characterization at a potential CO₂ storage site in Queensland, Australia.

Bakhshian’s method, which integrates natural soil respiration-based processes into a deep learning framework, was able to detect anomalies in sensor data that, in future applications, could represent either sensor errors, or leaks.

“Having a reliable real-time anomaly detection framework that is trained using streaming sensor data and guided by a process-based methodology could help facilitate environmental monitoring in future projects,” said Bakhshian.

According to the Global CCS Institute, the United States is one of the countries with the greatest potential for geological CO₂ storage. Although some environmentalists argue that CCS is simply a way for energy companies to continue extracting fossil fuels, others, including the Intergovernmental Panel on Climate Change, include CCS as one ways in which the global community can achieve net zero emissions by mid-century. .

“It’s safe and effective,” Bakhshian said. “And computing will help us find cost-effective ways to achieve this goal.”

Reference: “Dynamics of dissolution of sequestration in geological carbon storage” by SaharBakhshian, November 16, 2021, International Journal of Greenhouse Gas Monitoring.
DOI: 10.1016/j.ijggc.2021.103520

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