Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects

25 Pages Posted: 1 Apr 2021

See all articles by William Ampomah

William Ampomah

New Mexico Institute of Mining and Technology

Robert Will

New Mexico Institute of Mining and Technology

Marcia McMillan

New Mexico Institute of Mining and Technology

Tom Bratton

affiliation not provided to SSRN

Lianjie Huang

Government of the United States of America - Los Alamos National Laboratory

George El-Kaseeh

New Mexico Institute of Mining and Technology

Xuejian Liu

Government of the United States of America - Los Alamos National Laboratory

Jiaxuan Li

Government of the United States of America - Los Alamos National Laboratory

Don Lee

affiliation not provided to SSRN

Date Written: March 31, 2021

Abstract

Researchers at New Mexico Tech, Los Alamos National Laboratory (LANL), Sandia National Laboratory (SNL), and other collaborators are developing a methodology to improve characterization of stress in the subsurface by means of a model-based inversion of reservoir engineering data, time lapse seismic measurements, and microseismicity. Historically, various geophysical techniques have been used in efforts to understand the state of stress in the subsurface through direct and indirect imaging of stress sensitive features (faults and fractures) and observations of transient stress related observations (time variant elastic moduli and microseismicity). Standard direct and indirect techniques for seismic fault and fracture imaging suffer from detectability limits due to reliance on high data quality and multiplicity. Variations in effective elastic properties from inversion of high quality seismic (Vertical Seismic Profile (VSP)) data may be used to infer stress near the wellbore through integration with independent experimental characterization of the stress-velocity relationship. Given a sufficiently robust observation network, microseismic emissions may be inverted to characterize focal mechanisms which, together with supporting assumptions and constraints, inform estimates of in-situ stress. While each of these techniques contributes in part to the characterization of stress within limited spatial and temporal domains, no one method provides an unambiguous stress measurement or a predictive capability over a site scale spatial extent. Challenges associated with solution non-uniqueness, measurement ambiguity, and irregular sampling may be greatly minimized through combination of one or more of independent measurements within a common framework in which realistic geological, hydrodynamic, geomechanical, and seismological constituent process models may act as constraints.

Suggested Citation

Ampomah, William and Will, Robert and McMillan, Marcia and Bratton, Tom and Huang, Lianjie and El-Kaseeh, George and Liu, Xuejian and Li, Jiaxuan and Lee, Don, Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects (March 31, 2021). Proceedings of the 15th Greenhouse Gas Control Technologies Conference 15-18 March 2021, Available at SSRN: https://ssrn.com/abstract=3816723 or http://dx.doi.org/10.2139/ssrn.3816723

William Ampomah (Contact Author)

New Mexico Institute of Mining and Technology ( email )

801 Leroy Place
Socorro, NM 87801
United States

Robert Will

New Mexico Institute of Mining and Technology ( email )

801 Leroy Place
Socorro, NM 87801
United States

Marcia McMillan

New Mexico Institute of Mining and Technology ( email )

801 Leroy Place
Socorro, NM 87801
United States

Tom Bratton

affiliation not provided to SSRN

Lianjie Huang

Government of the United States of America - Los Alamos National Laboratory

Los Alamos, NM 87545
United States

George El-Kaseeh

New Mexico Institute of Mining and Technology ( email )

801 Leroy Place
Socorro, NM 87801
United States

Xuejian Liu

Government of the United States of America - Los Alamos National Laboratory ( email )

Los Alamos, NM 87545
United States

Jiaxuan Li

Government of the United States of America - Los Alamos National Laboratory ( email )

Los Alamos, NM 87545
United States

Don Lee

affiliation not provided to SSRN

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
21
Abstract Views
82
PlumX Metrics