Natural Gas Midstream
Smart Methane Emission Detection System Development
Last Reviewed March 2017

DE-FE0029020

Goal
The goal of this project is to develop an autonomous, real-time methane leak detection technology, the Smart Methane Emission Detection System, which applies machine learning techniques to passive optical sensing modalities to mitigate emissions through early detection. Phase 1 is leveraging previous SwRI research and experience to develop the prototype methane detection system with integrated optical sensors and the embedded processing unit. Phase 2 will focus on integration and field-testing of the prototype system along with demonstration to the DOE within a representative controlled environment. The system will target the following key features:

Table 1. Key Features of the Smart Methane Emission Detection System

Feature

Details

Low False Alarm Rates

Less than 0.5% (number of events incorrectly classified as leaks).

Autonomous Detection

No need for a human to be in the loop – the system acquires, processes, and makes autonomous decisions on whether or not a hazardous substance was observed, using machine learning algorithms.

Near Real-Time Detection

The time between acquiring data and obtaining an output from the system is only a few minutes.

Non-Intrusive, Passive Technology

No need to retrofit existing equipment and facilities. The proposed technology is passive in nature, thus eliminating safety and operational restrictions.

Target Platform

Ability to deploy technology in a stationary platform for monitoring facilities such as refineries and pump stations.

Extensibility

Ability to nimbly integrate new detection techniques into system for other types of target substances.


Performers
Southwest Research Institute (SwRI), San Antonio, TX 78238
Falcon Inspection (camera in-kind contributor)
IRCameras (camera in-kind contributor)

Background
While much has been accomplished in developing optical sensors for imaging methane leaks, limited work has been accomplished in developing methane emission detection technologies that meet three key critical criteria for effective methane emission mitigation:

  1. Autonomy (no need for a human to be in the loop)
  2. High reliability (low false alarm rates)
  3. Real-time performance (ability to detect emissions within a few minutes once it starts)

This project focuses on these three key aspects and significantly advances the state-of-the-art in methane emission detection using optically-based sensing technologies.

Impact
An autonomous, real-time methane leak detection system facilitates early detection of emissions before they become a larger problem. Compressor station operators will be able to identify failing equipment in aging infrastructure and replace faulty components expediently, resulting in methane emissions being reduced significantly through early detection of non-compliant equipment. This project is expected to produce the following outcomes and/or impacts:

  • Develop a system to reliably, accurately and autonomously identify methane leaks at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions.
  • Add a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility.
  • Assist in the quantification process by providing a means of collecting temporal and spatial image data of a leak event.
  • Reduce operational costs of emissions detection technologies by significantly minimizing the need for operator involvement.Provide a solution that is scalable, cost-effective, and non-intrusive.
  • Reduce methane emissions by early real-time, autonomous detection of methane leaks.

Accomplishments (most recent listed first)

  • Sensor (cameras) setup, configuration, and calibration was completed.
  • Initial test data were labeled for classifier training. Initial convolutional neural network (CNN) was configured for training.
  • The following equipment for the budget period was ordered and received: Midwave Infrared (MWIR) camera from IRCameras, MWIR camera from Falcon Inspection, thermal camera (SwRI in-kind).
  • Tests and data acquisition for budget period 1 are underway.
  • Feature extraction and analysis tasks are underway.
  • Development of leaks and non-leak events detection algorithm is underway.

Current Status (February 2017)
Camera setup, configuration and calibration was completed. The Mid-Wave Infrared (MWIR) cameras are being utilized as the transducers to capture frames in search of methane gases. The cameras are configured and calibrated to effectively capture the environment under varying illumination, weather conditions, and distances. An initial set of tests was performed under representative conditions to establish a baseline database containing methane leaks of various concentrations, distances, and scenarios. These conditions include an initial set of varying ambient temperature conditions, cloud cover, presence, and lack of obstacles (such as piping), and varying wind (including stagnant) conditions. A subsequent test was performed with more representative scenarios of methane leaks as well as leaks of other types of gases that might be present at compression station-type facilities that could trigger false alarms. This was done to increase the accuracy and robustness of the algorithms. An initial set of test data was labeled for classifier training. An initial convolutional neural network (CNN) was also configured for training. Development of a leaks and non-leak events detection algorithm is underway.

Project Start: October 1, 2016
Project End: September 30, 2018

DOE Contribution: $628,396
Performer Contribution: $157,379

Contact Information
NETL – Joseph B. Renk III (joseph.renk@netl.doe.gov or 412-386-6406)
SwRI – Maria S. Araujo (maria.araujo@swri.org or 210-522-3730)

Additional Information:

Quarterly Research Performance Progress Report [PDF] July - September, 2017

Quarterly Research Performance Progress Report [PDF] April - June, 2017

Quarterly Research Performance Progress Report [PDF] January - March, 2017

Quarterly Research Performance Progress Report [PDF] October - December, 2016