Enhanced Analytical Simulation Tool for CO2 Storage Capacity Estimation and Uncertainty Quantification


Screenshot of the EASiTool User Interface
Screenshot of the EASiTool User Interface
University of Texas at Austin
Website:  University of Texas at Austin
Award Number:  FE0009301
Project Duration:  05/01/2013 – 04/30/2018
Total Award Value:  $994,942
DOE Share:  $795,896
Performer Share:  $199,046
Technology Area:  Geologic Storage
Key Technology:  GS: Fluid Flow, Pressure & Water Management
Location:  Austin, Texas

Project Description

This project has the primary objective of developing an Enhanced Analytical Simulation Tool (EASiTool) for the development of simplified reservoir models to predict pressure impact on CO2 injectivity and reservoir-storage capacity of geologic formations. The EASiTool will include three major features: (1) an advanced, closed-form, analytical solution for pressure-buildup calculations that is used to estimate both injectivity and reservoir-scale pressure elevation, in both closed- and open-boundary aquifers; (2) a simple geomechanical model coupled with a base model to evaluate and avoid the possibility of fracturing reservoir rocks during CO2 injection operations, which can account for rock deformation; and (3) a net-present-value based optimization algorithm to integrate the brine-management process so as to maximize stakeholders’ profits, assuming carbon-storage credits.

Project Benefits

This project is focused on development of an analytical simulation tool (EASiTool) for CO2 storage capacity estimation and uncertainty quantification. Development of improved reservoir modeling tools will enable project developers to more confidently predict storage capacity and ensure storage efficiency and permanence, contributing to better storage technology and thus reducing CO2 emissions to the atmosphere. Specifically, this project will develop EASiTool, which includes a solution for pressure-buildup calculations, a simple geomechanical model coupled with a base model for rock deformation, and a net-present-value (NPV)-based optimization algorithm that each serve as a part of a methodology for selecting the optimal number of required injection and extraction wells and calculating new capacity and injectivity estimates under the brine-extraction process.

Contact Information

Federal Project Manager 
Andrea McNemar: andrea.mcnemar@netl.doe.gov
Technology Manager 
Traci Rodosta: traci.rodosta@netl.doe.gov
Principal Investigator 
JP Nicot: jp.nicot@beg.utexas.edu

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