Enhanced Analytical Simulation Tool for CO2 Storage Capacity Estimation and Uncertainty Quantification Email Page
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Performer:  University of Texas at Austin Location:  Austin, Texas
Project Duration:  05/01/2013 – 04/30/2018 Award Number:  FE0009301
Technology Area:  Geologic Storage Total Award Value:  $994,942
Key Technology:  GS: Fluid Flow, Pressure & Water Management DOE Share:  $795,896
Performer Share:  $199,046

Screenshot of the EASiTool User Interface
Screenshot of the EASiTool User Interface

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.

Presentations, Papers, and Publications

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