In-Situ MVA of CO<sub>2 </sub>Sequestration Using Smart Field Technology [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2014.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- 323 pages : digital, PDF file
- Additional Creators:
- West Virginia University Research Corporation
United States. Department of Energy
United States. Department of Energy. Office of Scientific and Technical Information
- Capability of underground carbon dioxide storage to confine and sustain injected CO<sub>2 </sub> for a long period of time is the main concern for geologic CO<sub>2 </sub> sequestration. If a leakage from a geological CO<sub>2 </sub> sequestration site occurs, it is crucial to find the approximate amount and the location of the leak, in a timely manner, in order to implement proper remediation activities. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or near surface monitoring of the sequestered CO<sub>2 </sub>. This study aims to monitor the integrity of CO<sub>2 </sub>storage at the reservoir level. This work proposes developing in-situ CO<sub>2 </sub> Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or “Smart Wells” along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO<sub>2 </sub> leakage by de-convolving the pressure signals collected from Permanent Down-hole Gauges (PDG). Citronelle field, a saline aquifer reservoir, located in the U.S. was considered as the basis for this study. A reservoir simulation model for CO<sub>2 </sub> sequestration in the Citronelle field was developed and history matched. PDGs were installed, and therefore were considered in the numerical model, at the injection well and an observation well. Upon completion of the history matching process, high frequency pressure data from PDGs were generated using the history matched numerical model using different CO<sub>2 </sub>leakage scenarios. Since pressure signal behaviors were too complicated to de-convolute using any existing mathematical formulations, a Machine Learning-based technology was introduced for this purpose. An Intelligent Leakage Detection System (ILDS) was developed as the result of this effort using the machine learning and pattern recognition technologies. The ILDS is able to detect leakage characteristics in a short period of time (less than a day from its occurrence) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS is examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift and noise in the pressure sensor and uncertainty in the reservoir model.
- Published through SciTech Connect.
Shahab D. Mohaghegh.
- Type of Report and Period Covered Note:
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