LNG

Deep-Learning Image-Processing Model Uses Optical Gas-Imaging Camera To Detect Leaks

This paper describes a deep-learning image-processing model that uses videos captured by a specialized optical gas imaging camera to detect natural gas leaks.

Fig. 1—Processed footage of a correct detection. Distance: 18.6 m; emission rate: 109.5 ± 2.5 scf/hr.
Fig. 1—Processed footage of a correct detection. Distance: 18.6 m; emission rate: 109.5 ± 2.5 scf/hr.
Source: OTC 34756.

The authors have developed a novel deep-learning (DL) image-processing model that uses videos captured by a specialized optical gas imaging (OGI) camera to detect natural gas leaks. The temporal DL algorithm is designed to identify patterns associated with gas leaks and improve its performance through supervised learning.

Modeling

This study was designed to exploit the technical strengths of OGI technology in tandem with temporal DL techniques. The central objective of the study was to engineer a robust model capable of accurate and efficient detection and quantification of methane leaks from video data. This resulted in the inception of the temporal DL image‑processing model (TDLP-NG).

The operational phase commenced with the strategic deployment of OGI cameras at select locations throughout natural gas facilities.

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