Methane Leak Attribution with LiDAR Gas Imaging
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Solution Overview
Problem
Existing gas imaging systems for detecting fugitive emissions, such as methane, suffer from false positives, false negatives, and inaccuracies in attributing leaks to the correct source due to limitations in scanning patterns, human operator dependence, and environmental complexities, which affect the accuracy of leak rate quantification and source identification.
Innovation Solution
An adaptive gas imaging system using LiDAR and supervised machine learning to automatically scan for gas plumes, define attribution subspaces, and calculate leak attribution confidence levels based on camera orientation and site geometry, employing differential absorption spectroscopy and single photon detection to enhance precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a predetermined scanning pattern is used to scan for gas plumes, then the scanning process is systematic and covers the field of view, but false positives from noise occur and large plumes are spread across multiple frames reducing detection accuracy
Solution Approach 1:
The patent combines multiple scattered plume detections across different frames and scanning cycles into a single unified plume event. By merging detections that are spatially and temporally correlated, the system distinguishes real plumes from random noise, reducing false positives while maintaining comprehensive coverage of the field of view.
Solution Approach 2:
The system continuously scans the field of view across multiple cycles and frames, maintaining continuous monitoring rather than isolated detections. This continuous action allows the system to track plume movement and persistence over time, improving detection reliability by distinguishing continuous plume signals from transient noise.
2Measurement precision
If the imager recenter and zoom on detected plumes, then additional detail is captured, but the scan cycle is prolonged and attribution accuracy is reduced due to restricted field of view
Solution Approach 1:
The system performs preliminary scanning at a wider field of view to detect plumes and identify their general location before performing detailed examination. By preparing the context of the overall scene in advance, the system can quickly locate and attribute plumes without prolonged scanning cycles, as the preliminary wide-angle scan already establishes the spatial context.
3Measurement precision
If zoom level is optimized to focus on one equipment unit, then leak rate quantification accuracy is improved, but surrounding context is cut off making source attribution error-prone
Solution Approach 1:
The system transitions from a two-dimensional trade-off between zoom level and field of view to a three-dimensional solution by incorporating temporal information. By analyzing plume characteristics across multiple time points and scanning cycles, the system can maintain accurate leak rate quantification while using temporal context to correctly attribute plumes to their sources, effectively adding the time dimension to compensate for restricted spatial view.
4Ease of operation
If human operators manually identify leak sources from images, then flexible interpretation is possible, but operator fatigue and cognitive biases reduce accuracy and scale operation becomes bottlenecked
Solution Approach 1:
The system enables automatic self-identification of leak sources by processing images and detecting plumes without human intervention. The automated algorithm identifies plume characteristics, tracks their movement, and attributes them to specific equipment, eliminating operator fatigue and cognitive biases while maintaining consistent accuracy across large numbers of images and enabling scalable operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate and efficient detection and attribution of methane leaks by minimizing false positives and negatives, ensuring prompt repair actions and enhancing the reliability of leak rate quantification.
Implementation Method 1
The gas monitoring system can detect methane emissions at oil and gas facilities using a combination of differential absorption spectroscopy and single photon detection of the scattered laser light
Implementation Method 2
The light detection and ranging ('LiDAR') based gas monitoring system provides images of the integrated methane concentration, LiDAR range, and scattered light intensity within its field of view
Data Source
AI summary
Systems and methods are described for determining leak attribution of a fugitive gas. In an example, a computing device receives a gas density image of a fugitive gas from a camera. The computing device identifies, based on the camera orientation and the estimated leak location within the camera's field of view, along with information about the camera installation and site geometry, the equipment unit or group of equipment units where the emission occurred.