Quantitative Optical Gas Imaging (QOGI) has moved from promising innovation to proven field technology thanks to a series of independent, peer-reviewed, and industry-sponsored studies. These investigations—spanning controlled lab releases, blind field trials, and real-world oil and gas operations—demonstrate that QOGI is a robust, field-ready solution for detecting and quantifying fugitive hydrocarbon emissions.
Why These Studies Matter
The evidence shows QOGI can deliver reliable emission estimates, outperform traditional methods like Method 21 in many scenarios, and provide actionable data for LDAR, MRV, and compliance workflows—when used within its defined operating envelope.
These independent, peer-reviewed studies:
- Confirm QOGI’s reliability and accuracy.
- Validate QOGI in both controlled and real-world field conditions.
- Show that best results are achieved with proper setup, environmental awareness, and operator training.
- Establish QOGI as a proven tool for LDAR, MRV, and compliance workflows.
Study summaries
Benchmarking QOGI Against Method 21 (Concawe, June 2025)
Performance Evaluation of Methane Emissions Quantification Technologies Under Controlled and Field Conditions
Location: VITO, Belgium
Sponsor: Concawe (European oil industry association)
This foundational study put an early QOGI system head-to-head with Method 21, using 61 controlled hydrocarbon releases. The results were striking: QOGI achieved 100% leak detection and delivered quantification errors ranging from -23% to 69%, with an average error of just 6%. In contrast, Method 21 errors ranged from -92% to 667%, averaging 31%. The study concluded that QOGI provides more accurate and robust leak-rate estimates than sniffer-based quantification, especially when there is a temperature difference (ΔT) of at least 5°C between the gas and background.
Key takeaways:
- QOGI outperformed Method 21 in quantification accuracy.
- Was most accurate when ΔT ≥ 5°C.
- Demonstrated robust detection and quantification across a wide emission range.
- Validated as a practical tool for LDAR and environmental monitoring.
- Technology has been improved dramatically since this testing was performed
Comparison of QOGI vs. Method 21 (Controlled Release Study)
Criteria |
QOGI (Quantitative Optical Gas Imaging) |
Method 21 (Sniffer-Based Method) |
Study context |
Head‑to‑head comparison using 61 controlled hydrocarbon releases |
|
Leak detection rate |
100% of leaks detected | Detected leaks, but detection does not directly translate to accurate quantification |
Quantification error range |
-23% to 69% | -92% to 667% |
Average quantification error |
6% | 31% |
Consistency of results |
Relatively tight error band with strong agreement to known release rates | Highly variable results with large over‑ and under‑estimation |
Ability to reflect true emissions magnitude |
Strong—measurements closely track known release rates | Limited—correlation equations introduce large uncertainty |
Implications for emissions inventories |
Supports more accurate, measurement‑based inventories when results are aggregated | Can significantly over‑ or under‑estimate emissions at the component level |
Overall conclusion from the study |
Demonstrated superior accuracy and reliability compared to Method 21 | Showed substantially higher uncertainty and variability |
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Blind Testing in Realistic Field Conditions
Detection and Measurement of Fugitive Emissions of Natural Gas from Transmission Systems
Location: Zaragoza, Spain
Sponsor: GERG (European Gas Research Group)
In this study, QOGI (QL320) and other methane quantification systems were tested under controlled outdoor conditions—without operators knowing the true leak rates. QOGI’s quantification was typically within a factor of three of the actual emission rates, with performance influenced by release size, background, and environmental conditions. The study highlighted the importance of operator technique and the value of averaging multiple measurements for improved accuracy.
Key takeaways:
- QOGI reliably measured moderate to high leak rates.
- Accuracy improved with controlled environmental conditions and multiple measurements.
- Operator setup and technique were critical to success.
- Demonstrated QOGI’s robustness in realistic, blind-case field scenarios.
- Numerous tests where outside of calibration capabilities at the time of testing
QOGI vs. Other Methane Quantification Technologies
Performance Based on Factor‑of‑Three (x3) Accuracy Criterion
Technology Category |
Typical Deployment |
Accuracy Performance (x3) |
Key Observations |
QOGI (Quantitative Optical Gas Imaging) |
Ground‑based, near‑field | Highest fraction of measurements within a factor of three | Most measurements fell within factor‑of‑three accuracy, with improved performance under favorable environmental conditions. |
Mobile ground‑based sensors |
Road‑based surveys | Lower fraction within ×3 compared to QOGI | Effective for screening, but quantification accuracy varied, especially for smaller or intermittent emission sources. |
Drone‑based quantification systems |
Aerial, site‑level | Mixed performance relative to QOGI | Accuracy depended strongly on flight patterns, plume interception, and environmental conditions. |
Aircraft‑based methods |
Basin‑ or facility‑scale | Generally lower ×3 agreement for individual sources | Well suited for large emitters, but less accurate for source‑level quantification. |
Sniffer‑based / point sensors |
Component‑level, contact | Not a primary focus of ×3 analysis | Effective for detection, but inconsistent for reliable quantification within factor‑of‑three accuracy. |
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QOGI in the Real World
Field Performance of New Methane Detection Technologies: Results from the Alberta Methane Field Challenge
Location: Rocky Mountain House, Alberta, Canada
Sponsor: Alberta Upstream Petroleum Research Fund (AUPRF)
This large-scale field evaluation compared various methane detection and quantification tools at approximately 50 oil and gas sites, using QOGI (QL320) as a baseline reference. QOGI enabled rapid screening and facility-level emission profiling, supporting site prioritization and emissions inventory development. The study confirmed QOGI’s scalability and value as a first-pass screening tool in integrated MRV workflows.
Key takeaways:
- QOGI provided rapid, facility-level emission estimates.
- Supported prioritization of high-emitting sites.
- Complemented other detection technologies in MRV programs.
- Reinforced QOGI’s role in measurement-based compliance.
- Only true, field-based study of QOGI capabilities
Controlled Validation at Scale
Methane Quantification Performance of the Quantitative Optical Gas Imaging (QOGI) System Using Single‑Blind Controlled Release Assessment
Location: METEC, Colorado State University, USA
Sponsor: Colorado State University / METEC
This study used single-blind controlled experiments to evaluate QOGI (QL320) under conditions simulating well-pad operations, including vegetation and ambient outdoor environments. Of 357 measurements in 26 release scenarios, 75% of QOGI quantifications were within a factor of three of the true emission rates. Accuracy was highest with low wind and clear-sky backgrounds, and improved by averaging multiple vantage points.
Key takeaways:
- QOGI achieved 75% of measurements within 3× of true rates.
- Best accuracy with calm wind and clear backgrounds.
- Multiple vantage points reduced outliers and improved results.
- Validated QOGI’s practical accuracy
- Conditions were environmentally similar to field, but lacked true thermal properties which increase accuracy with an OGI camera
- Few tests in study dramatically skewed the results and were not discussed
Conditions That Affect Quantification
Factor |
Impact |
| Low wind | ^ Higher accuracy |
| Clear‑sky backgrounds | ^ Higher accuracy |
| Multiple vantage points | ≤ Fewer outliers |
| Higher release rates | ^ Higher accuracy |
| Measurement distance (2–10 m) | ^ Higher accuracy / fewer outliers |
Conclusion: What the Combined Evidence Shows
Taken together, the body of independent research demonstrates that QOGI consistently detects and quantifies methane leaks across a wide range of operating and environmental conditions. Accuracy is highest when environmental factors are well understood and best practices are followed, including proper setup and trained operation.
Across many scenarios, QOGI has been shown to outperform traditional sniffer‑based methods, particularly in its ability to visualize emissions, prioritize sources, and support quantification at scale. Collectively, these studies validate QOGI as a reliable, field‑ready solution for emissions detection, quantification, and regulatory compliance, supporting its use in LDAR programs, MRV initiatives, and evolving reporting frameworks.