Description of the Research Opportunity
In 2013, the USGS produced an assessment of carbon dioxide storage resources throughout the United States [1]. This assessment was a static assessment, which did not account for injection rates, number and configuration of injection wells, pressure increase, or existing pressure of the storage formations. Over the past several years, the USGS has begun to work on multiphase fluid flow modeling to account for these parameters and to build a methodology for a dynamic carbon dioxide storage assessment. Several papers [2-4] cover this effort, the latest of which used the modeling software iTOUGH2 [5]. However, we need a model that will work in the newer version, TOUGH3 [6], and one that is flexible enough to allow for assessing not just carbon dioxide injection, but also methane, hydrogen, methane-hydrogen blends, and potentially compressed air. These other storage options are part of current research to assess the energy storage capability in the subsurface [7]. We would like to be able to use a similar model to assess using the same formation for both carbon and energy storage, as well as to evaluate and compare the relative storage potential of different fluids in the same formation.
There are several paths forward with the research after creating such a malleable model, but two potential areas of interest include probabilistic functionality and/or machine learning evaluations of the output to identify sweet spots for specific storage types. USGS resource assessments are typically probabilistic, which honors the natural heterogeneity of geology in the subsurface. Creating a multiphase fluid flow model that can produce outputs that represent the potential storage ranges throughout the subsurface could be done by performing multiple realizations with parameters chosen for inputs via Monte Carlo simulation. If machine learning can be incorporated into the model to help evaluate the sweet spots in the formation for optimizing energy storage versus carbon dioxide storage, that would be beneficial to land management stakeholders to determine the best usage of the pore space resources. The applicant is encouraged to add in these or other relevant concepts in how their model can be used to answer questions that are crucial to meeting national energy transition goals.
Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.
References
[1] U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resources—Results (ver. 1.1, September 2013): U.S. Geological Survey Circular 1386, 41 p., https://pubs.usgs.gov/circ/1386/. (Supersedes ver. 1.0 released June 26, 2013.)
[2] Jahediesfanjani, H., Warwick, P.D., and Anderson, S.T., 2017, 3D Pressure‐limited approach to model and estimate CO2 injection and storage capacity: Saline Mount Simon Formation: Greenhouse Gases—Science and Technology, v. 7, no. 6, p. 1080–1096. https://doi.org/10.1002/ghg.1701.
[3] Jahediesfanjani, H., Anderson, S.T., and Warwick, P.D., 2019, Improving pressure-limited CO2 storage capacity in saline formations by means of brine extraction: International Journal of Greenhouse Gas Control, v. 88, p. 299–310. https://doi.org/10.1016/j.ijggc.2019.06.009.
[4] Plampin, M.R., Anderson, S.T., Finsterle, S., and Cahan, S.M., 2023, Dynamic estimates of geologic CO2 storage resources in the Illinois Basin constrained by reinjectivity of brine extracted for pressure management: Greenhouse. Gas. Sci. Technol. v. 13, n. 1, p. 31–47. https://doi.org/10.1002/ghg.2189.
[5] Finsterle, S., Commer, M., Edmiston, J.K., Jung, Y., Kowalsky, M.B., Pau, G.S.H., Wainwright, H.M., and Zhang, Y., 2017, iTOUGH2: A Multiphysics simulation-optimization framework for analyzing subsurface systems: Computers & Geosciences, v. 108, p. 8–20. http://dx.doi.org/10.1016/j.cageo.2016.09.005.
[6] Jung, Y., Pau, G.S.H., Finsterle, S., and Pollyea, R.M., 2017, TOUGH3: A new efficient version of the TOUGH suite of multiphase flow and transport simulators: Computers & Geosciences, v. 108, p. 2–7. http://dx.doi.org/10.1016/j.cageo.2016.09.009.
[7] Buursink, M.L., Anderson, S.T., Brennan, S.T., Burns, E.R., Freeman, P.A., Gallotti, J.S., Lohr, C.D., Merrill, M. D., Morrissey, E.A., Plampin, M.R., and Warwick, P.D., 2023, Geologic energy storage: U.S. Geological Survey Fact Sheet 2022–3082, 4 p., https://doi.org/10.3133/fs20223082.
Proposed Duty Station(s)
Reston, Virginia
Areas of PhD
Geology, geophysics, reservoir engineering, carbon storage, or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).
Qualifications
Applicants must meet one of the following qualifications: Research Geologist, Research Hydrologist, Research Engineer, Research Computer Scientist, or Research Physical Scientist.
(This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)
