A methodology for quantifying risk and likelihood of failure for carbon dioxide injection into deep saline reservoirs

Justin Wriedt, Milind Deo, Weon Shik Han, Jim Lepinski

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


Tectonically stable deep saline reservoirs are considered to be the most abundant carbon dioxide (CO2) sequestration sites. Pressure, temperature, salinity and other characteristics of these geologic formations vary at each injection site. It is essential to understand the roles of geologic and engineering factors to optimize the CO2 injection conditions and to quantify the associated risks. Factors such as the magnitude of injection-induced reservoir pressure, quantity of supercritical phase CO2 that comes in contact with caprock, and the amount of residually trapped CO2 govern the fate of CO2, and provide quantitative assessment of the storage integrity.A streamlined protocol was developed using response surface methodology to quantify the risks related to CO2 injection. The proposed methodology includes the design of simulation scenarios, selection and screening of parameters, multiple-linear regression of outcomes, and the development of probability density functions (PDF) of various potential risk factors. Multiphase numerical simulations were performed to understand the behavior of the injected CO2 and associated parameters in deep saline reservoirs with prescribed geometries and petrophysical properties. Formation thickness, formation depth, porosity, horizontal permeability, brine density, and the end-point residual CO2 saturation were the six critical parameters identified that affected important outcomes. A six-factor Box-Behnken experimental design procedure was used to establish an understanding of the sensitivity of the parameters on the important factors, and for subsequently establishing response surfaces. Closed boundary domains with different operational constraints were employed. A stepwise, sequential regression method was used to determine statistically significant coefficients of a response surface model. Monte Carlo simulations with logical distributions of input parameters were performed using the response surface coefficients. Uncorrelated and correlated porosity-permeability distributions were used to generate two types of probability density functions (PDF). PDFs of CO2 plume extent under the caprock and average reservoir overpressure after injection were generated given all of the variability in the input parameters. These results will allow initial screening of a large number of potential injection sites without detailed simulations of each.

Original languageEnglish
Pages (from-to)196-211
Number of pages16
JournalInternational Journal of Greenhouse Gas Control
Publication statusPublished - 2014 Jan

Bibliographical note

Funding Information:
This material is based upon work supported by the Department of Energy Award Number DE-FE0001112. This paper was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. This project is managed and administered by Headwaters Clean Carbon Services LLC (HCCS) and funded by DOE/NETL and cost-sharing partners. The authors gratefully acknowledge the academic license to CMG products from Computer Modeling Group, Calgary, Canada.

All Science Journal Classification (ASJC) codes

  • Pollution
  • General Energy
  • Management, Monitoring, Policy and Law
  • Industrial and Manufacturing Engineering


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