In-Situ MVA of CO<sub>2 </sub>Sequestration Using Smart Field Technology

doi:10.2172/1238333

GSTDTAP > 地球科学

DOI	10.2172/1238333
来源ID	OSTI ID: 1238333
	In-Situ MVA of CO₂Sequestration Using Smart Field Technology
	Mohaghegh, Shahab D.
	2014-09-01
出版年	2014
页数	323
语种	英语
国家	美国
领域	地球科学
英文摘要	Capability of underground carbon dioxide storage to confine and sustain injected CO₂ for a long period of time is the main concern for geologic CO₂ sequestration. If a leakage from a geological CO₂ 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 CO₂. This study aims to monitor the integrity of CO₂storage at the reservoir level. This work proposes developing in-situ CO₂ Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or âSmart Wellsâ along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO₂ 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 CO₂ 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 CO₂leakage 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.
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来源平台	US Department of Energy (DOE)
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/6588
专题	地球科学
推荐引用方式 GB/T 7714	Mohaghegh, Shahab D.. In-Situ MVA of CO₂Sequestration Using Smart Field Technology,2014.