Most “lost profit” stories in methane seem impressive. Big leak, big number, big ROI slide.
The math is straightforward. Estimate a leak rate, assume it lasted for weeks or months, multiply by the gas price, and you get a substantial dollar amount.
That approach misses what methane truly indicates. Methane acts as a profit signal. It reveals where product leaves the system, where operations fail, and where time and money are wasted in response. The real question isn’t “how big was it.” It’s “how long did it last, how often does it happen, and what should we do next.”
That’s where Loss Signals comes in.
The standard “loss of profit” story, and what’s missing
For years, aerial surveys have mainly shaped most lost profit stories.
You fly over an asset and detect a plume. You estimate an emission rate in kg per hour. Then, you assume the leak has been ongoing since the last aerial survey or LDAR cycle, projecting that rate until the next scheduled survey. Multiply total tonnage by gas price to find your “lost profit.”
On paper, it is simple. In practice, it rests on one fragile assumption: that the leak was continuous for the entire period between snapshots.
If that interval is 30, 60, or 90 days, the estimate quickly inflates. You can end up much higher than the actual value.
The math is simple. The assumptions are not.
What duration really looks like
When you combine extensive aerial coverage with ongoing monitoring, a different picture appears.
Aerial flyovers help you understand the likelihood of different emission sizes from an asset, such as 0 to 5 kg per hour, 5 to 10, 10 to 25, and 25 or more. Continuous monitors show how long events in each size range actually last.
The pattern is consistent: many meaningful events last days, not months. Large, persistent leaks are the exception. The usual cause is short-term, recurring events on specific assets or configurations.
One example: for a 25 kg per hour event, the average duration is around two days. Aerial-only ROI estimates often assume that the same event has been ongoing since the last quarterly flyover – about 91 days. That gap can significantly inflate “extra gas captured” by roughly 45x.
The product loss is real. The story around it often isn’t.
The real loss signal, misaligned strategy
If your lost profit calculations assume events last for months when they usually last days, you don’t just get the wrong number. You get the wrong strategy.
The consequences are predictable:
- Overestimated returns from specific interventions.
- Overspending on repeated flyovers that don’t change the risk profile.
- Underinvestment in sites with frequent, smaller events that quietly add up.
- Programs designed for edge cases, not common patterns.
Loss of profit isn’t just like gas escaping the pipe. It also includes crews sent to the wrong sites, work orders targeting the wrong problems, and leadership decisions based on ROI figures that fall apart once duration is corrected.
There is another, quieter loss signal as well. Manual workflows waste money. Spreadsheet-based programs slow response time, fragment follow-up, and delay action. Small issues repeat, and teams lose the predictive advantage – the early warning needed to prevent bigger losses tied to equipment stress and repeated failure modes.
A better approach, baselines you can defend
The answer isn’t to abandon aerial; it’s to incorporate it as one layer in a broader data canopy.
A more honest view of profit begins with two steps.
- Use aerial to understand the distribution of emission sizes across an asset.
- Use continuous monitoring to measure how long events in each size range actually last.
Together, this provides a stronger foundation for emissions baselining, revenue-at-risk calculations, and strategy development across LDAR, aerial, and continuous monitoring.
It eliminates the assumption that “every event lasted since the last flyover” and grounds investment decisions in observed behavior, showing how emissions appear, persist, and resolve over time.
That’s where lost profit stops being a guess and becomes a strategic input.
From tonnage to decisions
Once size and duration are accurately characterized, risk-based analysis becomes more valuable than any single detection method.
Instead of asking where the biggest plume appeared, you can ask better questions:
- Which sites show recurring events rather than one-offs?
- Where do emissions cluster in the 10 to 25 kg per hour range instead of rare extremes?
- Which assets or equipment types appear repeatedly?
- Where does a change in method actually alter outcomes?
A risk-based tool can use historical aerial data, continuous monitoring, and operational context to create a prioritized worklist. It clarifies where continuous monitoring is valuable, where targeted LDAR or follow-up is sufficient, and where additional aerial data offers real insights.
Seeing the full cost
Instead of guessing how long an event lasted between snapshots, continuous monitoring allows you to see when it started, how it developed, how quickly it was resolved, and how often similar events occur at the same site.
Combine that with operational data – maintenance logs, throughput, equipment profiles, and crew activity – and the full cost curve comes into view. It shows how much product was actually lost, how much time teams spent responding, how many truck rolls and work orders could have been avoided, and where process changes could eliminate entire classes of events.
Profit, in this context, isn’t just gas in the pipe. It’s time saved, work avoided, and capital allocated correctly.
Start honest. Then get smarter.
Resetting the conversation about lost profit requires recognizing three facts.
- Methane emissions represent real product loss.
- You don’t need exaggerated duration assumptions to justify taking action.
- Decisions should be based on real, observed event behavior – understanding how often events happen, how long they last, and how targeted interventions can change that trend.
From there, strategy becomes clearer. Use existing data, including aerial, to identify where risks are concentrated. Add higher frequency data when it influences decisions. Link emissions and operations to redesign processes instead of just tracking plumes.
Loss of profit isn’t just about the product. It’s the cost of acting on incorrect assumptions and the benefit of finally having the data to improve.