- Enhanced Analytical Simulation Tool (EASiTool) for CO2 Storage Capacity Estimation and Uncertainty Quantification
- Project PI: Seyyed A. Hosseini
- Main Personnel: Reza Ganjdanesh, Seunghee Kim, Houman Bedayat
- Collaborators: C12 Energy
Please contact project PI Seyyed A. Hosseini to ask for a free copy of the EASiTool V4.0 program and other inquiries.
Here are some new features in EASiTool V4.0:
- A brine extraction model has been coupled with the base model to evaluate the active reservoir management. User has the option to enter the number of extractors.
- Uniform injection/extraction rate has been added as an alternative for the optimal injection/extraction rate.
- User-defined locations for injection and extraction wells.
This project culminated in the publication of two important scientific journal articles. Read them to learn more about the methods and ability of EASiTool to more easily estimate carbon storage capacity:
- Ganjdanesh, Reza, and Seyyed A. Hosseini. 2017. “Geologic Carbon Storage Capacity Estimation Using Enhanced Analytical Simulation Tool (EASiTool).” Energy Procedia 114 (July): 4690–96. https://doi.org/10.1016/j.egypro.2017.03.1601.
- Ganjdanesh, Reza, and Seyyed A. Hosseini. 2018. “Development of an Analytical Simulation Tool for Storage Capacity Estimation of Saline Aquifers.” International Journal of Greenhouse Gas Control 74 (July): 142–54. https://doi.org/10.1016/j.ijggc.2018.04.017.
An analytical-based Enhanced Analytical Simulation Tool (EASiTool) has been developed for technical and non-technical users with minimum engineering knowledge. The purpose of EASiTool is to produce a fast, reliable estimate of storage capacity for any geological formation. EASiTool includes closed-form analytical solutions that can be used as a first step for the screening of geological formations to determine which formation can best accommodate storage needs over a given period of time.
EASiTool has been developed with a highly user-friendly interface, however, the analytical models behind the EASiTool are cutting-edge models that incorporate effects of rock geomechanics, evaporation of brine near the wellbore, as well as deployment of brine extraction in the field to enhance the storage capacity. A net present value (NPV) based analysis has been implemented to devise the best field development strategy to maximize the stakeholder's profit by optimizing the number of injection/extraction wells.
This highly user-friendly tool provides a unique strategy for CO2 injection combined with brine extraction to optimize any CO2 project by maximizing the project's NPV. Benefits of this project include:
- application of the advanced closed-form analytical solutions to estimate CO2 injectivity into geological formations,
- estimation of the number of injection/extraction wells necessary to reach the storage goal, and
- improving current static storage efficiency coefficients by instead using dynamic closed-form analytical solutions.
The EASiTool developed in this project contributes directly to DOE research needs. At four stages of the development, EASiTool were released to the possible end-users (regulators, private and public companies, coal-fired power plants, etc) at this website.
This report 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 study was funded and managed by the U.S. DOE/NETL, under award number DE-FE0009301.
Last Updated: September 26, 2019