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Big Data Analytics for the Analysis and Visualization of Distributed Temperature Sensor Data
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 80th EAGE Conference and Exhibition 2018, Jun 2018, Volume 2018, p.1 - 5
Abstract
Permanent installations of Fiber Optics distributed sensing technologies are increasingly used for well performance surveillance. In particular, Distributed Temperature Sensors (DTS) measurements are a common tool to monitor the effectiveness of production (or injection) in wells with multiple completions, with the capability of highlighting warmer or colder zones along the well that do not behave as expected from the reservoir models. However, the continuous monitoring of DTS data from the wells poses some challenges, related to: the huge size of the data collected from the Fiber, the capability to apply algorithms that automatically identify anomalies in the temperature profile along the time, the possibility to quickly and flexibly visualize the anomalies for further investigation and drilldown.
The paper will describe how a Big Data Analytics platform based on open source technologies, such as Hadoop, can effectively deal with the above mentioned challenges, by providing an environment where algorithms can be easily applied to large amounts of data, and the reservoir specialists can easily visualize and drill into the data through the concept of “Data Science Lab” environment.