A shifting landscape that demands new thinking

Methane management is moving from aspiration to accountability. As regulatory momentum intensifies and investors sharpen their focus on emissions, oil and gas companies face a defining question:

Frameworks like the Oil and Gas Methane Partnership (OGMP) 2.0 have been pivotal in raising the bar on transparency. But with that progress has come the realization that traditional approaches to building source‑level inventories simply cannot cope with the scale, diversity, and dynamism of modern operations. Direct measurement is indispensable, yet constantly measuring every emission source, at every site, is neither technically practical nor financially feasible.

The industry finds itself at an inflection point; the challenge is no longer whether to measure, but how to meaningfully extrapolate. Not just filling the gaps, but doing so in a way that is defensible, traceable, and responsive to new data.

The core challenge: complexity, variability, and the limits of human‑driven interpolation

Even with the best intentions, operators face three systemic barriers:

1. Operational complexity outpaces manual workflows

Methane sources differ by design, by operating conditions, and by behavior. Human‑driven extrapolation, whether in spreadsheets or bespoke scripts, struggles to keep pace with the contextual diversity that defines real-emissions behavior.

2. Traditional extrapolation methods aren’t built for high‑scrutiny reporting

Regulators and auditors increasingly expect to see how decisions were made:

• Why one measurement set was applied to another.
• What assumptions were embedded.
• How uncertainty was calculated and propagated.

“Because it seemed similar” is no longer defensible.

3. Static inventories don’t reflect dynamic operations

As new measurements become available, inventories need to evolve with them. Most operators cannot continuously update extrapolations without losing transparency or overburdening their teams.

These challenges are not just operational, they threaten the credibility of methane reporting itself. And they open the door for a more intelligent, systematic, and automated approach.

A new paradigm: intelligence‑driven extrapolation

The next wave of methane management will be defined by the combination of advanced analytics, AI‑driven similarity modelling, and digitally traceable decision frameworks.

This isn’t about “flashy software.” It’s about enabling operators to build methane inventories that are:

• Scalable across tens of thousands of sources.
• Traceable down to each assumption and decision.
• Dynamic, updating as new measurement data arrives.
• Statistically defensible, even in sparse or variable datasets.
• Aligned with OGMP 2.0 expectations, without drowning teams in manual effort.

In this emerging model, technology doesn’t replace expertise, it amplifies it, creating a bridge between robust measurement science and the operational realities of global portfolios.

What this moment demands: technology designed for this exact inflection point

Our CH4 methane intelligence solution is an example of this new paradigm in action.

Traceable and auditable by design
Our approach captures the reasoning behind every extrapolation decision; including the measurement sources used, rationale for similarity, and uncertainty handling. This level of transparency matches what auditors increasingly expect.

AI‑assisted similarity analysis
Instead of relying on manual grouping, the system uses AI to analyze equipment types, emission behaviors, technologies, and operating contexts to ensure that extrapolation is grounded in scientifically meaningful similarity, not convenience.

Scalable, multi‑level extrapolation
The capability to extrapolate within facilities, across facilities, and across asset classes enables operators to produce representative methane inventories even when measurement data is unevenly distributed.

Dynamic updating as new measurements arrive
As measurement campaigns expand, extrapolations update automatically, ensuring inventories stay aligned with real operational conditions rather than becoming outdated snapshots.

Statistical robustness
The system supports mean, median, robust mean, and other appropriate techniques, to avoid the distortions introduced by outliers or small sample sizes.

Making methane accountability operational

The conversation is no longer just about checking boxes for OGMP Level 4. It’s about building a foundation for credible, continuous methane accountability; the kind that satisfies regulators, investors, and operators alike.

Leadership on methane means recognizing this shift and articulating a vision for what comes next: a methane management ecosystem where intelligence and automation support better decisions, stronger reporting, and a more resilient path to net‑zero operations.

Our solution is a response to this challenge, designed around the very pressures reshaping the methane landscape. This can not only drive regulatory compliance, and stakeholder confidence, but also support futureproofing of organizations’ operations in an evolving landscape of methane management.