Using big data and smart field technology for detecting leakage in a CO2 storage project

Using big data and smart field technology for detecting leakage in a CO2 storage project

Artificial intelligence

Carbon dioxide

Big data

Domes

Pattern recognition

Digital storage

Learning systems

Reservoir management

Petroleum reservoir evaluation

Smart Fields are distinguished with two characteristics: Big Data and Real-Time access. A small smart field with only ten wells can generate more than a billion data points every year. This data is streamed in real-time while being stored in data historians. The challenge for operating a smart field is to be able to process this massive amount of information in ways that can be useful in reservoir management and relevant operations. In this paper we introduce a technology for processing and utilization of data generated in a smart field. The project is a CO2 storage demonstration at Citronelle Dome, Alabama and the objective is to use smart field technology to build a real-time, long-term, CO2 Intelligent Leakage Detection System (ILDS). The main concern for geologic CO2 sequestration is the capability of the underground carbon dioxide storage to confine and sustain the injected CO2 for very long time. If a leakage from a geological sink occurs, it is crucial to find the approximate location and amount of the leak in order to take on proper remediation activity. To help accommodate CO 2 leak detection, two PDGs (Permanent Down-hole Gauges) have been installed in the observation well. A reservoir simulation model for CO 2 sequestration at Citronelle Dome was developed. Multiple scenarios of CO2 leakage are modeled and high frequency pressure data from the PDGs in the observation well are collected. The complexity of the pressure signal behavior and the reservoir model makes the use of inverse solution of analytical models impractical. Therefore an alternate solution is developed for the ILDS, based on Machine Learning. High Frequency Data Streams are processed in real-time, summarized (by Descriptive Statistics) and transformed into a format appropriate for pattern recognition technology. Successful detection of location and amount of CO2 leaking from the reservoir using the real-time data streams demonstrates the power of pattern recognition and machine learning as a reservoir and operational management tool for smart fields. Copyright 2013, Society of Petroleum Engineers.

815-821

1

Haghighat, S. Alireza

Mohaghegh, Shahab D.

Gholami, Vida

Shahkarami, Alireza

Moreno, Daniel

SPE Annual Technical Conference and Exhibition, ATCE 2013, September 30, 2013 - October 2, 2013

2013

conferencePaper

10.2118/166137-ms

http://dx.doi.org/10.2118/166137-ms