Introduction
For years, optical gas imaging cameras have given inspectors one simple answer: yes, there's a leak, or no, there isn't. That was enough when the goal was just finding leaks. It isn't enough anymore.
Regulators, investors, and ESG auditors now want a number. How much gas escaped? For how long? At what rate? A visual plume on a screen doesn't answer any of that, and it certainly doesn't hold up when the EPA or an OGMP 2.0 reviewer asks for measurement-based data.
This gap is exactly why quantitative optical gas imaging, or qOGI, has moved from a niche technical term to a compliance necessity.
This article breaks down what qOGI actually is and how it works at a mechanical level. It also covers the field factors that determine whether the numbers are trustworthy, and how continuous monitoring is redefining what "accurate" means in this space.
Key Takeaways
- qOGI converts visual leak indications into measurable mass or volumetric leak rates through algorithmic processing
- Three factors govern accuracy: gas infrared absorption, temperature differential, and sufficient gas concentration in view
- Distance, wind speed, camera cooling, and background conditions affect accuracy as much as the algorithm
- Continuous, AI-validated monitoring boosts real-world accuracy by quantifying only confirmed leaks, not every frame
What Is Quantitative Optical Gas Imaging (qOGI)?
Quantitative optical gas imaging pairs a cooled infrared camera with software that converts a visualized gas plume into numeric output. That output typically takes the form of a concentration path length in ppm-m, plus a mass or volumetric leak rate in units like g/hr or L/min. It's the difference between "I can see gas" and "here's how much gas."
Standard hydrocarbon optical gas imaging (OGI) cameras, the kind used in most leak detection and repair (LDAR) surveys, only do the first part. EPA's Appendix K protocol defines OGI as a method for determining the presence and location of a leak, not its rate. The EPA's own technical fact sheet on Appendix K makes clear that OGI on its own is not a direct emission-rate method.
That distinction matters more than it sounds. Without a leak rate, operators are stuck guessing which repairs to prioritize and have no defensible number to hand an auditor.
Multi-Compound Detection Through Response Factors
qOGI systems typically use Response Factors to extend a single-gas calibration across a broader range of compounds. A Response Factor describes how visible a given compound is relative to a reference gas (usually propane). A compound with a Response Factor of 0.3, for example, is roughly 30% as detectable as the reference. This lets one calibrated system estimate quantities for hundreds of chemically similar compounds without a separate calibration for each one.
Visual Evidence as a Verification Layer
Unlike a sniffer or flow sampler that just spits out a number, qOGI keeps the video. Operators and auditors can watch the plume that generated the reading and verify it independently. A Toxic Vapor Analyzer or Hi-Flow Sampler can't offer that same visual check.
qOGI is still a relatively young technology, but it's already being used for ICR requests, tank emissions estimates, DTM and LDAR components, and internal repair-prioritization decisions across upstream operations. Well Checked's Zentinal IQ™ applies this same quantification layer to emissions that its Zentinal Core™ sensors have already validated, so operators get a defensible number only after a true leak is confirmed.
How Quantitative Optical Gas Imaging Works: The Science Behind Performance
qOGI performance comes down to standardizing three variables so that a reading taken on a windy Tuesday means the same thing as one taken on a calm Friday.
IR Absorption (α)
The target gas needs an infrared absorption peak that overlaps the camera's spectral band. Hydrocarbon OGI cameras typically isolate wavelengths near the 3.3 μm absorption region where methane and similar compounds are strongly visible. No overlap, no image: this is the baseline requirement before any quantification can happen.
Delta Temperature (∆T)
Plume visibility depends on the temperature contrast between the gas and its background, not the gas's absolute temperature. A higher ∆T produces a stronger signal-to-noise ratio and a more reliable reading.
CONCAWE's 2017 evaluation of a first-generation qOGI system required a ∆T greater than 5°C for reliable quantification across 61 controlled releases ranging from 1.7 to 1,000 g/hr. Below that threshold, plumes tend to wash into the background.
Gas Presence (Concentration)
Gas volume in frame has to exceed the system's minimum detection limit before the algorithm can do anything useful with it. From there, the software aggregates pixel-level concentration data across the plume, adjusts for distance and wind speed, and totals it into a leak rate.
That calculation happens in one of two ways, depending on when and where the processing occurs:
- Real-time quantification: a tablet connects directly to the camera and processes the reading on the spot
- Recorded quantification (sometimes called Q-Mode): footage is captured in the field and quantified later, offline
Camera stability matters in both cases. Motion during capture degrades measurement accuracy, which is why handheld inspection work generally relies on a tripod rather than a free hand.
Platforms like Zentinal IQ™ apply this same three-variable standard, quantifying only emissions that Zentinal Core™ has already flagged as true anomalies.
Key Factors That Determine Real-World qOGI Performance and Accuracy
The algorithm is only one piece. Field conditions can swing a reading dramatically, and understanding why helps operators interpret the numbers they're getting.
Wind speed directly affects signal strength. A landmark study by Ilonze et al., involving 357 single-blind controlled-release measurements across 26 releases, found mean error swung from -29% at 0-1 mph wind to +216% above 10 mph.
Distance matters just as much: mean error measured +35% at 2-10 meters versus +95% at 1.5-2 meters, with some of that close-range error tied to the camera failing to capture the full plume.
Cooled vs. Uncooled Cameras
Cooled mid-wave IR (MWIR) cameras remain the sensitivity baseline for reliable quantification. Manufacturers describe them as capable of detecting gas at meaningfully lower concentrations than uncooled alternatives. That said, Long-Wave Infrared (LWIR) cameras have closed much of the gap while enabling day-and-night detection, often at roughly a third of the cost of traditional MWIR systems. Well Checked has a USPTO provisional patent filing for Detecting and Quantifying Fugitive Methane and Vapor Emissions Using Infrared Imaging and Machine Learning.
Calibration Burden
Toxic vapor analyzers require calibration checks before daily use plus a formal precision test every three months. Hi-Flow samplers need similarly frequent field verification. qOGI tablet-based solutions, by contrast, typically don't demand that same recalibration cadence. That's a meaningful operational difference when you're running a program across dozens of wells.
Other performance factors worth flagging:
- Background conditions matter: CONCAWE found mean error of +5% against sky versus +122% against equipment, meaning cluttered backgrounds distort readings
- A single snapshot reading from one site visit can't compete with a rolling average built from repeated observation
- Raw OGI/qOGI systems can generate nuisance alerts from normal operational vapor or steam; separating that noise from true fugitive emissions is a performance factor in its own right, not just an inconvenience
qOGI vs. Traditional Leak Quantification Methods
qOGI isn't the only way to put a number on a leak, but it solves problems the older methods never fully addressed.
| Method | Output | Calibration Burden | Field Requirement |
|---|---|---|---|
| qOGI | Concentration path length + leak rate | Minimal on tablet systems | Line-of-sight from a distance |
| Toxic Vapor Analyzer (TVA) | Concentration only, no flow rate | Daily checks, quarterly precision test | Direct probe contact at component |
| Bacharach Hi-Flow Sampler (BHFS) | Mass flow rate | Frequent field verification | Physical sealing of leak source |
Two limitations show up repeatedly with the older methods:
- TVAs report concentration only, not flow rate, and readings are highly susceptible to wind interference.
- Hi-Flow Samplers calculate mass rate but require physically sealing the component, usually with two technicians — one managing the enclosure, one reading data.
BHFS units also haven't been manufactured since around 2016, which is starting to limit long-term availability and support.
The safety difference is significant. qOGI lets an inspector quantify a leak from a safe standing distance. TVA and BHFS work often means scaffolding, confined-space entry, or standing directly in a gas plume to get a reading.
Improving qOGI Performance at Scale with Continuous Autonomous Monitoring
Even a perfectly accurate qOGI reading has a blind spot: it only captures the moment someone happens to be pointing a camera at the site. Quarterly or route-based inspection schedules mean most fugitive emissions events start and stop between visits, undetected and unquantified for weeks at a time.
This is the problem Well Checked Systems built Zensory.ai™ to solve. Instead of treating qOGI as a single-point inspection tool, the platform extends it into continuous, 24/7 site coverage by combining LWIR optical gas imaging with video and acoustic abnormal-sound detection at each monitored wellsite.
An Architecture Built for Signal, Not Noise
The platform separates visual intelligence, detection, and quantification across three distinct layers:
- Zentinal Ops™ delivers visual and acoustic equipment intelligence: high-resolution video, object recognition, acoustic anomaly detection, and actionable alerts. Well Checked has a USPTO provisional patent filing covering its acoustic anomaly detection technology.
- Zentinal Core™ continuously screens incoming sensor data, learning each site's normal operational baseline over roughly a two-day AI Site Learning cycle, then flags true fugitive anomalies while filtering out routine process vapor and steam
- Zentinal IQ™ only activates once Core has validated an event — quantifying emissions volume, duration, and rate for that confirmed leak, rather than running quantification on every frame the cameras capture
This gate matters for accuracy. It means quantification cycles aren't wasted on false alarms, and the resulting data reflects real fugitive events rather than noise.
Structured for Regulatory Defensibility
Continuous quantification data from Zentinal IQ™ aligns with EPA's methane rule under 40 CFR Part 60 Subpart OOOOb alternative-monitoring pathways, as well as OGMP 2.0 Level 4/5 measurement-based reporting requirements. The platform also structures output for SASB Oil & Gas E&P and TCFD disclosure formats, giving operators audit-ready records instead of a stack of periodic snapshots.
Operators can access the platform through purchase, lease, subscription, or a fixed-fee pilot program built for teams that want to prove out the approach on a defined set of sites before scaling further.
Frequently Asked Questions
What is a quantitative optical gas imaging (OGI) camera?
This is a standard cooled OGI camera paired with software, like Well Checked's Zentinal IQ™, that converts the visualized gas plume into a measured mass or volumetric leak rate rather than just showing a leak exists.
How does quantitative optical gas imaging work?
It standardizes infrared absorption, temperature differential, and gas concentration data at the pixel level, then applies an algorithm that accounts for distance and wind speed to calculate a leak rate.
How much does a quantitative optical gas imaging camera cost?
Costs vary by camera cooling technology and software licensing tier. LWIR-based systems now offer quantification at about a third the cost of traditional mid-wave IR solutions, with subscription or pilot-based options available instead of outright purchase.
Can a thermal camera detect a gas leak?
Only specialized cooled infrared cameras tuned to specific gas absorption wavelengths can reliably visualize leaks. Standard uncooled thermal cameras used for general temperature imaging generally cannot detect hydrocarbon gas plumes.
What's the difference between qualitative and quantitative OGI?
Qualitative OGI only shows that a leak exists. Quantitative OGI adds algorithmic processing to measure how much gas is leaking, enabling repair prioritization and regulatory reporting.
Is optical gas imaging quantification accurate enough for regulatory compliance?
Quantitative OGI has undergone third-party validation, including CONCAWE controlled-release testing, and is increasingly used for internal compliance and ESG reporting. Pairing it with continuous, validated monitoring strengthens its regulatory defensibility further.
