An Advanced Joint Inversion System for Carbon Dioxide Storage Modeling with Large Date Sets for Characterization and Real-Time Monitoring-Enhancing Storage Performance and Reducing Failure Risks under Uncertainties Email Page
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Performer: Leland Stanford Junior University
Project conceptual framework for developing,<br/>testing, and applying advanced algorithms for high<br/>resolution estimation of subsurface properties and<br/>CO<sub>2</sub> transport and to provide uncertainty estimates
Project conceptual framework for developing,
testing, and applying advanced algorithms for high
resolution estimation of subsurface properties and
CO2 transport and to provide uncertainty estimates
Website: Leland Stanford Junior University
Award Number: FE0009260
Project Duration: 10/01/2012 – 01/31/2016
Total Award Value: $799,865
DOE Share: $600,000
Performer Share: $199,865
Technology Area: Geologic Storage
Key Technology: GS: Fluid Flow, Pressure & Water Management
Location: Stanford, California

Project Description

The objective of this project is to develop, test, and apply an advanced joint data inversion tool to enhance the predictive capability of models regarding the fate of CO2 plumes, and to improve storage performance through better understanding storage systems. The joint inversion tool can be used for decision-making for optimal control of CO2 injection and storage by linking forward simulation, dynamic monitoring and inversion, uncertainty quantification, and risk assessment under a consistent framework.

Project Benefits

This study is focused on development of an advanced joint inversion tool, incorporating modeling and monitoring data, which can be used for decision-making for optimal control of CO2 injection operations. Improved injection control systems allow 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 improves computational efficiency of stochastic joint inversion which links forward simulation, dynamic monitoring and inversion, uncertainty quantification, and risk assessment under a consistent framework.

Contact Information

Federal Project Manager Karen Kluger: karen.kluger@netl.doe.gov
Technology Manager Traci Rodosta: traci.rodosta@netl.doe.gov
Principal Investigator Peter K. Kitanidis: peterk@stanford.edu

 

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