Development of a Framework for Data Integration, Assimilation, and Learning for Geological Carbon Sequestration


Figure 1: DIAL-GCS consists of data management,<br/>complex event processing, coupled modeling/data<br/>assimilation, and decision support modules.
Figure 1: DIAL-GCS consists of data management,
complex event processing, coupled modeling/data
assimilation, and decision support modules.
University of Texas at Austin
Website:  University of Texas at Austin
Award Number:  FE0026515
Project Duration:  10/01/2015 – 09/30/2019
Total Award Value:  $1,662,227
DOE Share:  $1,315,873
Performer Share:  $346,354
Technology Area:  Monitoring, Verification, Accounting, and Assessment
Key Technology:  MVAA: Subsurface Monitoring
Location:  Austin, Texas

Project Description

The project aims to develop and demonstrate a data integration, assimilation, and learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS is an intelligent monitoring system (IMS) for automating GCS closed-loop management. It leverages recent advances in computer programming. Specifically, the project is developing an ontology-driven GCS data management module for storing, querying, and exchanging GCS data (both historic and live sensor data) from multiple heterogeneous formatted sources. It incorporates a complex-event processing engine for detecting abnormal situations. This engine is being developed by combining expert knowledge, rule based reasoning, and machine learning. The IMS is being designed to enable uncertainty quantification and predictive analytics using reduced-order modeling. The IMS capabilities are being integrated and developed with both real and simulated data from the Cranfield, Mississippi carbon storage test site.

Project Benefits

The DIAL-GCS includes a data management module, a complex event processing module, a data assimilation module, and a machine learning module to significantly enhance the real-time knowledge extraction and decision support capability in a multi-sensor environment. A versatile business intelligence platform is being adopted to facilitate module integration and to empower the end user to make data-driven decisions, thus offering a GCS-specific decision support environment (both desktop- and cloud-based) for CO2 risk management and injection optimization. The technology developed in this project contributes to the DOE’s Carbon Storage Program’s goal of developing and validating technologies to measure and account for 99 percent of injected CO2 in the injection zones.

Contact Information

Federal Project Manager 
Bruce Brown:
Technology Manager 
Traci Rodosta:
Principal Investigator 
Alexander Sun:

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