Intelligent Leak Detection System

Software · Mon Oct 27 00:00:00 EDT 2014

OSTI ID:1231917

Mohaghegh, Shahab D.

apability of underground carbon dioxide storage to confine and sustain injected CO2 for a very long 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 order to implement proper remediation activity. 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 CO2 storage 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 “Smart Wells” 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 for this study. A reservoir simulation model for CO2 sequestration in the Citronelle field was developed and history matched. The presence of the PDGs were considered in the reservoir model at the injection well and an observation well. High frequency pressure data from sensors were collected based on different synthetic CO2 leakage scenarios in the model. Due to complexity of the pressure signal behaviors, a Machine Learning-based technology was introduced to build an Intelligent Leakage Detection System (ILDS). The ILDS was able to detect leakage characteristics in a short period of time (less than a day) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS was 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.

• Short Name / Acronym: ILDS; 003176IBMPC00
• Version: 00
• Programming Language(s): Medium: X; OS: Windows 7 and up; Compatibility: IBM PC
• Research Organization: West Virginia Univ., Morgantown, WV (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: FE0001163
• OSTI ID: 1231917
• Country of Origin: United States