Secure and efficient capture and storage of carbon by supercomputer

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

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

The road to a stable climate is challenging and controversial. A number of solutions will be needed to enable a rapid and equitable transition from fossil fuels, including the development of sustainable energy sources, greener materials and methods of removing CO₂ from the atmosphere.

One of the removal methods that 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 thousands of years old, largely in the way water is stored in aquifers.

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

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

Writing in International Journal of Greenhouse Gas Control In December 2021, it explored the efficiency of capturing CO₂ by dissolving gas in resident brine in saline aquifers.

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

She found that two factors greatly influenced the amount of CO₂ that could be stored in rock spaces: wetting (or how well the 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 sites for CO2 storage

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

Another effective process that ensures the security of CO₂ storage is capillary capture, which occurs when CO₂ is trapped and becomes immobilized in the pore space by capillary forces. In a study published in Advances in water resources In April 2019, Bakhshian presented the results of two-phase pore-scale 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 pore structure of the rock during the injection stage. and how it can be caught as a blob immobilized in the pore space during post-injection.

Bakhshian’s work is done under the auspices Gulf Coast Carbon Center (GCCC)working to understand the potential, risks and best methods for geological carbon storage since 1998.

Supercomputers are one of the key tools that geoscience has at its disposal to study the relevant processes for capturing and storing carbon, according to Bakhshian. “Computational fluid dynamics techniques are essential for this area, to better analyze the appropriate target tanks for CO₂ storage and to predict the behavior of CO₂ feathers in these tanks,” she said.

Sahar Bahkshian

Sahar Bahkshian, Associate Researcher in Bureau of Economic Geology, Jackson School of Geosciences, Credit: University of Texas at Austin

Understanding the dynamics of pore storage capacity through high-performance computational simulations provides a lens on how carbon capture and storage could be performed on a large scale.

“Our research is basically trying to characterize the right geological settings for storage and to explore how we inject CO₂ to make sure it is safe, efficient and does not pose a threat to humans or groundwater resources,” Bakhshian said.

Another aspect of Bakhshian’s research involves the use of machine learning techniques to develop computationally fast models that can estimate the storage capacity of tanks and help monitor the CO₂ environment.

Writing in Environmental Science and Technology In October 2021, Bakhshian proposed an in-depth learning framework to detect anomalies in the data of soil gas concentration sensors. The model was trained on data from sensors used to characterize the environment at a possible CO₂ storage site in Queensland, Australia.

Bakhshian’s method, which incorporates processes based on the natural respiration of the soil into a deep learning environment, was able to detect anomalies in the sensor data which, in future applications, could represent either sensor errors or leaks.

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

According to the Global CCS Institute, the United States is one of the nations with the greatest potential for geological storage of CO₂. While some environmentalists believe that CCS is simply a way for energy companies to continue to extract fossil fuels, others, including International Panel on Climate Changeinclude CCS as one of the ways the global community can achieve zero net emissions by the middle of the century.

“It’s safe and effective,” Bakhshian said. “And computers will help us find economical ways to achieve this.”

Reference: “Dynamics of carbon capture in geological storage of carbon” by SaharBakhshian, 16 November 2021, International Journal of Greenhouse Gas Control.
DOI: 10.1016 / j.ijggc.2021.103520 Secure and efficient capture and storage of carbon by supercomputer

Back to top button