Category: Media

Carbon sensors are critical tools for emissions monitoring and climate reporting. Explore how NDIR, TDLAS, and CRDS technologies work, their strengths, and where they still face limitations.

Read time: 5 minutes

As global attention turns toward decarbonization and emissions transparency, the ability to measure carbon dioxide (CO₂) accurately and reliably has never been more important. From climate research to industrial monitoring and methane mitigation programs, carbon sensors have become foundational tools for understanding how carbon moves through our atmosphere and infrastructure.

But like any measurement technology, carbon dioxide sensing comes with both strengths and limitations. While the technology has advanced significantly in recent years, understanding what these sensors can, and cannot, do is critical for interpreting the data they produce.

In this post, we explore where carbon dioxide sensing technology stands today, what types of sensors are commonly used, and how they fit into the evolving landscape of emissions monitoring.

Why Carbon Measurement Matters

Carbon dioxide is the most widely tracked greenhouse gas and plays a central role in climate reporting frameworks, emissions inventories, and atmospheric research. Measuring CO₂ helps operators and researchers:

• Track emissions from industrial processes and energy production

• Validate reported emissions inventories

• Understand combustion efficiency and fuel use

• Improve atmospheric transport models

• Support regulatory and voluntary reporting frameworks

Because CO₂ is produced alongside methane in many industrial processes, measuring both gases together can provide important context when interpreting emissions data.

Common Types of Carbon Dioxide Sensors

Several sensor technologies are used to measure carbon dioxide, each with its own advantages depending on the application.

Non-Dispersive Infrared (NDIR) Sensors

NDIR sensors are among the most widely used CO₂ detection technologies. They work by measuring how infrared light is absorbed by CO₂ molecules within a sample cell. Their popularity comes from a combination of reliability, relatively low cost, and ease of deployment. NDIR sensors are commonly used in building ventilation systems, indoor air monitoring, and portable environmental sensors. However, while NDIR sensors are robust and versatile, they generally offer lower precision than more advanced spectroscopic methods and may require periodic calibration to maintain accuracy. Additionally, NDIR sensor can have reduced selectivity where some other species or instrumental signals can sometimes mimic the signal of CO₂.

Tunable Diode Laser Absorption Spectroscopy (TDLAS)

TDLAS technology uses a narrow-band laser tuned to a specific gas absorption line. This enables extremely precise detection of gas concentrations and is widely used in industrial and research environments. Because TDLAS sensors measure gas absorption directly at specific wavelengths, they offer excellent disambiguation from other gases and fast response times. Additionally, these sensors are characteristically lightweight and require low power, so they are well suited for mobile applications like on drones. The tradeoff is complexity and cost. Laser-based systems are typically more expensive than NDIR and may require careful optical alignment and calibration.

Cavity Ring-Down Spectroscopy (CRDS)

CRDS is one of the most precise techniques available for atmospheric gas measurement. It works by measuring how quickly a laser pulse decays inside a highly reflective optical cavity. Because the light travels thousands of times through the gas sample, CRDS systems can achieve extremely high sensitivity and accuracy. These systems are widely used in atmospheric research and high-end emissions monitoring. However, they also require longer sampling time which makes surveying large areas more time consuming and increases uncertainty due to atmospheric variability. An additional downside is that CRDS instruments are generally larger, more expensive, and highly sensitive to vibration, making them less suitable for lightweight mobile deployments compared to other sensor technologies.

The Strengths of Modern Carbon Dioxide Sensors

Over the past decade, carbon dioxide sensing technology has improved dramatically in several key ways. Sensors have become smaller and more ruggedized, enabling deployment on mobile platforms such as drones, aircraft, and autonomous monitoring systems. Advances in optics and electronics have also improved detection limits and stability, allowing for higher confidence measurements in dynamic environments. Another important development is the integration of environmental sensing. Many modern systems measure temperature, pressure, and wind conditions alongside gas concentrations, allowing measurements to be interpreted in a more physically meaningful way. SeekOps has a long heritage in leading such advancements in ruggedization, precision engineering, and scientific validation. Together, these improvements have expanded the range of applications for carbon sensors, from laboratory instruments to field-ready monitoring tools.

Where Carbon Sensors Still Struggle

Despite these advances, carbon sensing is not without challenges. One limitation is that CO₂ concentrations are naturally high in the atmosphere relative to methane. This means that detecting small incremental changes in CO₂ can be more difficult, particularly in open environments where background concentrations fluctuate. Environmental factors can also affect measurements. Temperature variations, humidity, and pressure changes can introduce measurement drift if not properly accounted for.

Another challenge lies in the fact that many carbon sensing technologies were originally developed for applications such as indoor air quality monitoring, industrial process control, or carbon capture, utilization, and storage (CCUS). These use cases often prioritize stable, high-concentration environments or point measurements, rather than detecting subtle concentration enhancements from combustion sources at a distance. As a result, applying these sensors to open-air emissions monitoring, especially in dynamic environments, can introduce additional limitations.

A further challenge lies in translating concentration measurements into actual emissions rates. Measuring gas concentration alone does not directly reveal how much gas is being emitted. To estimate emission rates, measurements must be combined with wind data and atmospheric transport models or data-driven measurement strategies designed to avoid such models. This is why many modern monitoring systems integrate gas sensors with meteorological instruments and modeling frameworks.

Carbon Dioxide Sensors in Emissions Monitoring

As methane monitoring programs evolve, CO₂ measurement is increasingly being used as a complementary tool. For example, CO₂ measurements can help evaluate combustion processes such as flaring, where carbon dioxide and methane ratios can indicate flare efficiency. They can also help validate bottom-up emissions inventories by providing an independent top-down perspective. Because methane and carbon dioxide often originate from related processes, measuring both gases together can improve source attribution and reduce uncertainty in emissions estimates.

The Road Ahead for Carbon Sensing

The next generation of carbon sensing technology is likely to focus on three areas: increased sensitivity, better environmental integration, and broader deployment. Miniaturization will continue to enable smaller, lighter sensors suitable for drones and autonomous platforms. Improved calibration techniques and sensor fusion will reduce measurement drift and improve reliability in field environments. At the same time, integration with atmospheric modeling tools will make it easier to translate concentration measurements into actionable emissions data. As emissions monitoring frameworks such as OGMP 2.0 and other measurement-based reporting programs mature, the demand for reliable, field-ready carbon sensing technologies will continue to grow.

SeekOps’ Carbon Dioxide Measurement Capabilities

While SeekOps is best known for its methane detection and quantification systems, our sensing platform is expanding to include the ability to measure carbon dioxide alongside methane. This evolution reflects a broader industry shift toward multi-gas measurement as a way to better understand emissions behavior in complex environments.

We are deploying this capability in the field, with our flagship application in combining methane (CH₄) and carbon dioxide (CO₂) measurements. These campaigns provide valuable insight into how simultaneous measurements can improve interpretation of combustion processes, emissions attribution, and overall site performance.

We expect to learn more about the practical benefits and limitations of measuring both gases together in real-world conditions. We’ll be sharing those insights in more detail in a future post focused specifically on simultaneous multi-gas measurement.

As carbon measurement technologies continue to evolve, integrating multiple gas measurements with in situ environmental data will play an increasingly important role in improving transparency and confidence in emissions data.

References

Hodgkinson, J., & Tatam, R. P. (2013). Optical gas sensing: A review. Measurement Science and Technology, 24(1), 012004. https://doi.org/10.1088/0957-0233/24/1/012004

Werle, P., Mücke, R., & Slemr, F. (2002). The limits of signal averaging in atmospheric trace-gas monitoring by tunable diode-laser absorption spectroscopy. Applied Physics B, 57, 131–139. https://doi.org/10.1007/BF00425997

Berden, G., Peeters, R., & Meijer, G. (2000). Cavity ring-down spectroscopy: Experimental schemes and applications. International Reviews in Physical Chemistry, 19(4), 565–607. https://doi.org/10.1080/014423500750040627

Zimmerle, D., Vaughn, T., Bell, C., Bennett, K., Deshmukh, P., & Thoma, E. (2020). Detection limits of optical gas imaging for natural gas leak detection in realistic controlled conditions. Environmental Science & Technology, 54(18), 11506–11514. https://doi.org/10.1021/acs.est.0c01285

U.S. Environmental Protection Agency (EPA). (2023). Greenhouse Gas Reporting Program: Subpart W – Petroleum and Natural Gas Systems. https://www.epa.gov/ghgreporting

National Institute of Standards and Technology (NIST). (2022). Carbon Dioxide Measurement Techniques and Calibration Standards. https://www.nist.gov

Offshore research shows hidden methane emissions during loading operations and the value of UAV-based measurement and real-time field data.

Read time: 4 minutes

Offshore methane emissions remain one of the least understood components of the global emissions landscape. While production platforms, flaring, and venting are relatively well studied, other parts of the offshore value chain have received far less attention.

A recent study published in Environmental Science: Processes & Impacts highlights one of these gaps: methane emissions during oil loading operations to shuttle tankers. The work was conducted at a Floating Production Storage and Offloading (FPSO) asset for a major operator on the UK Continental Shelf and combines aircraft and UAV measurements across a full loading cycle to better understand how emissions occur and how much may currently be missing from inventories.

A Previously Under-Characterized Source of Emissions

The study focuses on emissions associated with transferring oil from an FPSO to a shuttle tanker. While these operations are routine offshore, their methane emissions have historically been difficult to quantify.

Using both SeekOps UAV and the Facility for Airborne Atmospheric Measurements (FAAM) aircraft measurements, the study observed that methane emissions increase during loading events, with estimated rates spanning a wide range depending on measurement approach and sampling conditions. Importantly, the two independent measurement methods were correlated across event type, with higher emissions during tanker loading activity.

What makes this finding particularly important is that these emissions are not tied to static infrastructure, but are driven by operational activity at the FPSO during loading. When scaled over time, the study found that loading-related emissions can contribute a meaningful fraction of total site emissions, suggesting that inventories focused only on steady-state processes may miss a significant portion of the picture.

Why This Matters for Methane Inventories

Traditional methane inventories rely heavily on bottom-up methods, where emissions are estimated based on equipment counts, emission factors, and assumed operating conditions.

This study reinforces a growing industry realization: not all emissions fit neatly into those assumptions.

Offshore operations, especially on FPSOs like the one in this study, are inherently dynamic. Activities such as loading cycles, maintenance, and process fluctuations introduce variability that is difficult to capture with static emission factors alone. As a result, inventories may underestimate emissions if these transient events are not explicitly measured.

The Role of Measurement-Based Approaches

One of the most important aspects of this study is its reliance on real-time measurements. By directly observing methane concentrations during real operations, the researchers were able to capture emissions that would likely otherwise go unaccounted for.

This represents a broader shift in methane monitoring, from estimating emissions based on assumptions to measuring what is actually happening in the field.

It also highlights the importance of capturing emissions across complete operational cycles, particularly for offshore assets like FPSOs, where emissions can vary significantly depending on operational state.

Implications for Offshore Measurement Strategies

The findings suggest that offshore methane monitoring must extend beyond traditional sources and account for how operations evolve over time. Capturing emissions from loading events, for example, requires both spatial coverage and temporal awareness, understanding not just where emissions occur, but when.

Technologies deployed offshore must therefore be capable of responding to dynamic conditions, capturing transient plumes, and operating safely within complex logistical environments such as FPSO loading operations.

What This Means for SeekOps

For SeekOps, this study reinforces several core principles that have shaped our offshore measurement approach.

First, it highlights the importance of mobility and adaptability. Offshore emissions, particularly those associated with operations like FPSO loading, do not always occur in predictable locations or at steady rates. UAV-based systems provide the flexibility to respond to operational events as they happen.

Second, it strengthens the case for top-down quantification. By measuring methane concentrations downwind and reconstructing plume behavior through a flux plane, SeekOps captures emissions as they manifest in the atmosphere rather than relying on assumptions about source behavior. In addition, the study showed that UAV-based measurements were broadly consistent with airborne observations collected during the same operations, providing an additional layer of validation across independent measurement approaches.

Third, the study underscores the importance of in situ environmental measurement. Offshore plume behavior is heavily influenced by wind, stability, and marine boundary-layer effects. Measuring these variables directly during flight improves the reliability of emission estimates in dynamic conditions. It also increases the granularity of emissions attribution as we consider the variables.

Finally, the study reinforces a broader industry shift: measurement is uncovering emissions that inventories alone cannot fully describe. SeekOps’ approach is designed to complement existing inventory methods by helping operators reconcile measured data with reported values and identify gaps, particularly for operational emissions that are not easily captured in traditional frameworks.

Looking Ahead

Offshore methane monitoring is entering a new phase. As regulatory frameworks evolve and expectations for transparency increase, operators will need measurement strategies that reflect real-world conditions that capture both steady-state emissions and transient operational events.

Studies like this one — and recent work such as Deshpande et al. (2025) — provide a roadmap for how to get there.

For SeekOps, they reinforce the role of UAV-based measurement as a critical tool for understanding offshore emissions in full context, not just what is emitted, but when, where, and why.

References

Deshpande, S., Collins, E., Joynes, I., & O’Keeffe, R. (2025). Methane emissions measurement insights from an Offshore Measurement, Monitoring, Reporting and Verification study. Australian Energy Producers Journal, 65(2), EP24038. https://doi.org/10.1071/EP24038

Methane emission from shuttle tankers during standard oil loading operations. (2026). Environmental Science: Processes & Impacts. Royal Society of Chemistry. https://pubs.rsc.org/en/content/articlehtml/2026/em/d5em00768b

Ravikumar, A. P., et al. (2019). Single-blind inter-comparison of methane detection technologies – results from the Stanford/EDF Mobile Monitoring Challenge. Elementa: Science of the Anthropocene, 7, 37. https://doi.org/10.1525/elementa.373

SeekOps’ dynamic uncertainty method sets a new standard for methane measurement accuracy by analyzing in situ variables like wind, temperature, and pressure, and dynamically propagating uncertainty to deliver transparent, defensible emissions data.

Read time: 5 minutes

When it comes to measuring methane emissions, knowing how much is only part of the story. Equally important is understanding how confident we can be in that number.

At SeekOps, our technology doesn’t just detect and quantify methane, it also quantifies the uncertainty in every measurement, based on the actual real time survey conditions. This framework, which we call dynamic uncertainty, brings transparency, traceability, and scientific rigor to methane emissions data.

Why Uncertainty Estimation Matters

In environmental monitoring, uncertainty is not a weakness; it’s an acknowledgment of reality. Every emission quantification is influenced by multiple variables: sensor precision, wind speed and direction, temperature gradients, atmospheric turbulence, and model assumptions.

Many methane measurement systems handle this complexity by assigning a fixed uncertainty across all measurements. But the real atmosphere doesn’t behave that way. Each survey has its own meteorology, each plume its own structure, and each flight its own sampling geometry.

At SeekOps, we believe every emission requires its own uncertainty assessment, one that reflects what actually happened in the field. That’s the foundation of dynamic uncertainty.

From Components to Confidence: The Physics of the Flux Plane

Our quantification framework begins with the flux plane, the core output of SeekOps’ methane quantification system. The flux plane represents a vertical, gridded slice of the atmosphere that captures methane concentration enhancements along the drone’s flight path.

Each grid cell corresponds to an integrated measurement of methane mixing ratio, air density (measured by temperature and pressure), and wind speed. When combined, these data reconstruct a 2D flux field which is essentially a picture of how methane moves through the air at a given moment.

Mathematically, the total mass flux through the plane is computed as:

$$
\dot{m}_i = \frac{M_i}{M_{\text{air}}}
\sum_{y,z} \rho_{\text{air}}(y,z) \,
\chi_i(y,z) \,
u_{\perp}(y,z) \,
\Delta y \, \Delta z
\tag{1}
$$

Where:
•   ρ_air: dry air density (from in situ pressure & temperature)
•   χ_i: measured methane enhancement mole fraction
•   u_⊥: wind velocity component normal to the plane
•   M_i/M_air: molar mass ratio of methane to dry air
•   Δy, Δz : grid spacing in the horizontal and vertical directions, respectively

The integration of all these cells yields the total methane emission rate for the surveyed source (ṁ_i). Note that wind direction factors in here as u_⊥ = u cos(Φ) where Φ is the angle between the incident wind direction and the flux plane normal vector.

Step 1: Decomposing the Sources of Error

The first step in quantifying dynamic uncertainty is identifying where uncertainty comes from. Some of these sources were described by Mohammadloo et al. (2025) and include:
•   Instrument errors — from the SeekIR methane sensor and onboard environmental sensors (relatively small contributor).
•   Environmental variability — fluctuating winds, turbulence, and thermal stratification (moderate contributor).
•   Sampling geometry — how well the drone’s flight pattern intersects the plume (largest contributor).
•   Model assumptions — simplifications made in the mass balance and atmospheric transport equations.

At SeekOps, our emission solution explicitly measures the variables that influence some of these error sources. So, we can put it all together to see how each contributes to the overall uncertainty in our delivered emission rate.

Step 2: Defining the Error Propagation Framework

The flux plane’s uncertainty is computed using optimal estimation theory, a framework originally developed for satellite and lidar retrievals (e.g., Rodgers, 2000; Burton et al., 2016).

We define the measurement state vector as:

$$
\textbf{x} \in
\begin{cases}
\Delta \textrm{y} &: \text{distance along the flux plane},\\
\Delta \textrm{z} &: \text{vertical step of drone intervals},\\
u &: \text{wind speed},\\
\theta &: \text{wind direction},\\
T &: \text{temperature},\\
P &: \text{pressure},\\
\chi &: \text{gas enhancement concentration}
\end{cases}
$$

and the forward model F(x) that outputs the retrieved mass flux is given by Eq. (1).

Uncertainty is then propagated by differentiating the mass balance equation per each state parameter using central finite differencing which applies first-order Taylor expansion to ignore high order terms. This process builds the Jacobian matrix (K).

Covariance matrices that describe the empirical variability from controlled release studies (presented in Corbett and Smith, 2022), instrument accuracy, and measurement variability are constructed and propagated with K per the optimal estimation framework.

The terms in our error propagation framework are:

Step 3: Building the Covariance Matrices

The total prior covariance matrix combines random, measurement, and empirical components:

$$
S_\textrm{a}=S_\textrm{r}+S_\textrm{m}+S_\textrm{e}
$$

Each term plays a specific role:
•   S_r: random measurement noise
•   S_m: atmospheric state variable correlations
•   S_e: empirical uncertainty (~30% derived from validation campaigns)

The posterior covariance matrix incorporates correlation across the flux plane grid and state variable sensitivity through the Jacobian:

$$
S_\textrm{p}=\left( K^TS_\textrm{g}^{-1}K+S_\textrm{a}^{-1}\right)^{-1}
$$

Here, K represents the forward model sensitivity matrix (Jacobian) and S_g is the measurement covariance across the flux plane grid.

These matrices allow SeekOps to translate the field measurements into gridded uncertainty estimates and state variable uncertainty components, showing not only how much methane was detected but also how reliable each part of the plane is, and which state variables contribute most to the overall error.

Step 4: Visualizing the Uncertainty Grid

Once the covariance and Jacobian are computed, the uncertainty is reshaped into a 2D spatial grid corresponding to the flux plane geometry.

Each grid cell now contains a local estimate of uncertainty (σ) in the same units as the mass flux (e.g., kg/hr). The uncertainty grid looks similar to our plume concentration enhancements as the highest uncertainty typically occurs in the same spatial region where the signal is strongest, because that is where the model is doing the most “work.” In contrast, areas far outside the plume contribute almost nothing to the mass flux calculation, so even if there is noise there, its impact on the final emission estimate is negligible. As a result, the uncertainty naturally concentrates where the plume energy is, reflecting where errors actually matter.

• The top image shows the spatial distribution of uncertainty. Brighter areas indicate regions of greater variability — for instance, where the plume edges were only partially captured or where wind shear increased error propagation.
• The bottom chart visualizes a state parameter sensitivity analysis, showing how much each variable (wind, concentration, temperature, etc.) contributes to the total uncertainty.
• Together, these visualizations demonstrate how dynamic uncertainty converts complex retrieval theory into an intuitive map of measurement confidence.

Finally, the total uncertainty is computed as the quadrature sum of the gridded variances:

$$
\sigma_{\textrm{total}}=\sqrt{\sum_{\textrm{i}}\sigma_{\textrm{i}}^2}
$$

and reported as a one-standard-deviation range on the emission estimate:

$$
M_{\textrm{i}}\pm\sigma_{\textrm{total}}
$$

Step 5: Interpreting Dynamic Uncertainty in the Field

Dynamic uncertainty means that every SeekOps emission rate reflects its true field context.
•   In steady, well-defined plume conditions, uncertainty tightens.
•   In variable atmospheres and ill-defined plume geometries, it widens.

Rather than masking that variability, we make it visible. Each measurement’s confidence is quantified and mapped, providing operators and regulators with a transparent view of what’s known, and what’s less certain.

This empowers operators to:
•   Compare sites and surveys with statistical rigor.
•   Prioritize mitigation based on both emission size and confidence.
•   Defend results in ESG or regulatory reporting with traceable mathematical justification.

Looking Ahead: Beyond Dynamic Uncertainty

Dynamic uncertainty is the first half of a broader quantification philosophy. The next stage of SeekOps’ framework will address scenario uncertainty: accounting for “what if” cases like plumes drifting between flight lines or unmeasured variability in time.

Together, these two frameworks will deliver a total uncertainty envelope, encompassing both measurable and inferential confidence limits, an industry first for drone-based methane quantification.

How Uncertainty Transparency Informs the Future Standard

By making uncertainty visible, quantified, and tied directly to real field conditions, SeekOps is enabling the industry to move beyond “black box” emissions reporting and toward evidence-based methane accountability. This level of transparency is exactly what emerging regulatory frameworks, methane intensity certifications, and voluntary reporting programs are beginning to demand, not just a number, but how confident we are in that number.

As methane standards evolve, the expectation will shift from simply reporting emissions to demonstrating defensibility, being able to show why a measurement should be trusted, how it responds to weather and operational conditions, and how it compares across time and technologies. SeekOps’ dynamic uncertainty framework future-proofs operators by aligning directly with this direction of travel. It transforms methane quantification from a static output into a traceable, decision-grade dataset, the foundation required for what will become the next generation of international measurement standards.

References

Corbett, A., & Smith, B. (2022). Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters. Atmosphere, 13(5), 804. https://doi.org/10.3390/atmos13050804

Burton, S. P., Hair, J. W., et. al. (2016). Observations of the spectral dependence of linear particle depolarization ratio of aerosols using NASA Langley airborne High Spectral Resolution Lidar. Atmospheric Measurement Techniques, 9, 5555–5572. https://doi.org/10.5194/amt-9-5555-2016

Rodgers, C. D. (2000). Inverse Methods for Atmospheric Sounding: Theory and Practice. World Scientific.

Discover how SeekOps combines advanced engineering, scientific rigor, and operational know-how to deliver reliable, field-validated methane monitoring solutions worldwide.

Read time: 5 minutes

Why Integration Matters

In methane emissions monitoring, success doesn’t come from technology alone. It’s the integration of engineering design, scientific validation, and operational execution that sets SeekOps apart. Each survey requires not only advanced hardware but also the methodologies to use it effectively and the field expertise to adapt to real-world conditions.

SeekOps was built on this integration. From the earliest development of the SeekIR sensor at NASA’s Jet Propulsion Laboratory to today’s global deployment across six continents, the mission has always been the same: to combine the best of science and engineering with practical, real-world operations that make data both reliable and actionable.

Engineering Excellence – Technology Built for the Field

At the heart of SeekOps is the SeekIR sensor, a lightweight, high-sensitivity methane detector capable of measuring emissions down to parts per billion. Its origins in NASA’s Mars Curiosity Rover program demanded extreme durability and precision, and that legacy continues today.

But engineering doesn’t stop at the sensor. SeekOps systems are drone-agnostic, ruggedized, and efficient. This engineering foundation ensures that every mission can be executed safely, efficiently, and with high-quality data capture whether onshore, offshore, or in complex industrial environments.

Scientific Rigor – Measurement You Can Trust

Technology alone isn’t enough; it must be scientifically validated. That’s why SeekOps emphasizes continuous field validation and peer-reviewed collaboration. Our methods are grounded in spectroscopy, fluid dynamics, and controlled release testing, ensuring that every measurement is backed by science, not assumption.

SeekOps technology has been validated in:

• Blind controlled release trials such as METEC and landfill experiments.

• Independent academic research comparing technologies for detection and quantification.

• Peer-reviewed publications and global methane trials, including TADI 2024 and inter-comparison studies.

This scientific rigor translates into uncertainty reporting, confidence intervals, and reconciliation analysis, giving operators not just numbers, but numbers they can stand behind in regulatory and ESG contexts.

Operational Expertise – Delivering in the Real World

Even the best sensor and methods fall short without operational excellence. That’s why SeekOps invests heavily in training, safety, and adaptability. Our field teams are experienced in working alongside ongoing operations without disrupting production or safety protocols.

Operational expertise means launching drones from helidecks, supply vessels, or tight industrial installments, adapting flight plans in real time to shifting winds or unexpected hazards, and ensuring consistency by repeating flight paths for comparative analysis.

By uniting engineering and science with field know-how, SeekOps ensures that technology translates into reliable performance under the toughest conditions.

The Advantage in Action

SeekOps’ combined approach delivers tangible value to operators:

• Regulatory Compliance – Data that meets international standards like OGMP 2.0, EPA rules, and EU Methane Regulation.

• Operational Efficiency – Faster survey turnaround with live GCS streaming and FASTR reporting.

• Trusted Transparency – Uncertainty and reconciliation included in every report.

• Global Reach – Proven deployments across North and South America, Europe, the Middle East, Africa, Asia, and Australia.

From oil and gas fields in Texas to offshore rigs in the North Sea, from tropical biogas plants to desert well pads, SeekOps delivers consistent, high-quality results that operators can act on with confidence.

Building a More Sustainable Future

The climate challenge demands not only innovation but also collaboration. SeekOps works hand-in-hand with operators, regulators, and research institutions to ensure that emissions monitoring evolves with industry needs and global climate goals.

By uniting engineering ingenuity, scientific rigor, and operational expertise, SeekOps empowers industries to measure, manage, and ultimately reduce methane emissions. It’s not just about technology; it’s about making emissions data actionable in the pursuit of a more sustainable energy future.

Ready to see the SeekOps Advantage for yourself?
Request a Demo today and discover how SeekOps can help you achieve your emissions monitoring and sustainability goals.

References

Webster, C. R. (2005). Measuring methane and its isotopes 12CH₄, 13CH₄, and CH₃D on the surface of Mars with in situ laser spectroscopy. Applied Optics, 44(7), 1226–1235. https://doi.org/10.1364/AO.44.001226

NASA (National Aeronautics and Space Administration). (2019). Methane Detector Sniffs Out Leaks. NASA Technology Transfer Program. https://spinoff.nasa.gov/Spinoff2019/ps_7.html

Ravikumar, A. P., Sreedhara, S., Wang, J., et al. (2019). Single-blind inter-comparison of methane detection technologies – results from the Stanford/EDF Mobile Monitoring Challenge. Elementa: Science of the Anthropocene, 7(1), 37. https://doi.org/10.1525/elementa.373

Corbett, A., & Smith, B. (2022). Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters. Atmosphere, 13(5), 804. https://doi.org/10.3390/atmos13050804

Smith, B. J., Buckingham, S., Touzel, D., et al. (2021). Development of Methods for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment Using a Miniature Methane Spectrometer and Long-Endurance UAS. SPE Annual Technical Conference and Exhibition, Dubai, UAE. https://doi.org/10.2118/206181-MS

Tavner, C. A., Touzel, D. F., & Smith, B. J. (2021). Application of Long-Endurance UAS for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment. SPE Offshore Europe Conference and Exhibition, Virtual. https://doi.org/10.2118/205467-MS

Hossian, R. I., et al. (2024). A Controlled Release Experiment for Investigating Methane Measurement Performance at Landfills. Environmental Research and Education Foundation (EREF). https://erefdn.org/eref-funded-study-highlights-advances-in-measuring-landfill-methane-emissions

Dawson, K. W., Smith, B. J., Stocker, I., & Evans, P. (2024). Assessing the Application of Drone TDLAS Methane Emissions Monitoring Technology in the Intertropical Convergence Zone Using Machine Learning. APOGCE 2024. https://doi.org/10.2118/221317-MS

Gully-Santiago, M. A., Smith, B., Frederick, T., Dawson, K., & Elliott, D. (2025). Results and Learnings from the TADI 2024 Methane Quantification Trial. SPE Europe Energy Conference and Exhibition, Vienna, Austria. https://doi.org/10.2118/225634-MS

Learn how SeekOps helps operators compare measured and reported methane emissions using reconciliation reporting and the Celsius analytics platform.

Read time: 5 minutes

Beyond Measurement: Why Reconciliation Matters

Accurately detecting and quantifying methane emissions is just the first step. But raw data, no matter how precise, doesn’t solve the bigger question: what does this mean for the operator?

That’s where reconciliation comes in. At SeekOps, we don’t just hand over measurement numbers, we can help operators interpret them in the context of what’s already known. Reconciliation aligns top-down measured data with bottom-up reported values from emissions inventories, equipment logs, and operational activity.

This “closing of the loop” delivers practical insights. It helps operators meet regulatory and voluntary reporting standards like OGMP 2.0, EPA requirements, and the EU Methane Regulation for one. Additionally, it can improve the accuracy of corporate emissions inventories, identify sources that may be under- or over-reported in bottom-up data, and feed continuous improvement cycles for emissions reduction strategies.

In short, reconciliation turns numbers into context, and context into action.

What Is Reconciliation Reporting?

Reconciliation reporting is the process of comparing what is measured during a SeekOps survey to what is reported by the operator. A SeekOps Reconciliation Report provides more than just totals. It includes site-level emissions estimates with uncertainty bounds clearly defined, breakdowns of emissions by equipment groups or component areas, annotations of major discrepancies that warrant further investigation, and suggestions for follow-up actions such as retesting or repairs.

This level of detail helps ensure that operators don’t just comply with requirements but actually gain operational intelligence they can use.

Introducing Celsius – An Emissions Intelligence Dashboard

Measurement and reconciliation are powerful on their own, but they become transformative when paired with Celsius, SeekOps’ cloud-based analytics platform that can be accessed anywhere in the world. Celsius turns raw measurement and reconciliation results into interactive dashboards that make emissions data easy to understand and act on. Rather than static spreadsheets or one-off reports, Celsius provides a central hub for emissions intelligence, a single source of truth accessible across teams, sites, and geographies.

How It Works in Practice

Here’s how SeekOps’ reconciliation and Celsius workflow comes together in real-world operations:

1. Survey Conducted – SeekOps drones scan the site, capturing methane concentrations and wind vectors.

2. Celsius Visualization – Survey results are uploaded into Celsius for easy review, filtering, and export.

3. Emission Quantification – Mass flow rates are calculated using validated top-down scientific models.

4. Data Analysis – Our analysts use Celsius, thoroughly reviewing each survey, to generate reports that inform operators of where their leaks are and how much is leaking

5. Reconciliation – Operators take the finalized report and can compare against their inventories and logs, highlighting gaps or discrepancies.

This workflow ensures not just the data, but the entire data pipeline, is auditable and traceable.

Real-World Value for Operators

For oil & gas producers, landfill operators, or renewable natural gas facilities, the challenge isn’t just detecting methane, it’s proving reductions over time. Regulators and stakeholders alike demand measured, verified, and contextualized data.

SeekOps helps close this gap. By reconciling measurements with reported values, we enable operators to demonstrate compliance with confidence, benchmark emissions performance against industry standards, and support transparent ESG disclosures backed by field-validated science.

From Numbers to Action

Methane reduction isn’t about single measurements, but about building a trusted, repeatable workflow. SeekOps reconciliation and Celsius create that workflow by connecting measurement, interpretation, and reporting.

With this combination, operators move from asking “what are our emissions?” to confidently answering “how are we performing, where can we improve, and how fast can we act?”

Stay tuned for the next post in our series: “The SeekOps Advantage – Uniting Engineering, Science & Operational Expertise”

Want to see how SeekOps reconciliation and the Celsius platform can enhance your emissions management strategy?
Request a Celsius Demo or Ask to Speak to an Expert

References

Corbett, A., & Smith, B. (2022). Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters. Atmosphere, 13(5), 804. https://doi.org/10.3390/atmos13050804

Ravikumar, A. P., Sreedhara, S., Wang, J., et al. (2019). Single-blind inter-comparison of methane detection technologies – results from the Stanford/EDF Mobile Monitoring Challenge. Elementa: Science of the Anthropocene, 7(1), 37. https://doi.org/10.1525/elementa.373

Smith, B. J., Buckingham, S., Touzel, D., et al. (2021). Development of Methods for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment Using a Miniature Methane Spectrometer and Long-Endurance UAS. SPE Annual Technical Conference and Exhibition, Dubai, UAE. https://doi.org/10.2118/206181-MS

Tavner, C. A., Touzel, D. F., & Smith, B. J. (2021). Application of Long-Endurance UAS for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment. SPE Offshore Europe Conference and Exhibition, Virtual. https://doi.org/10.2118/205467-MS

Hossian, R. I., et al. (2024). A Controlled Release Experiment for Investigating Methane Measurement Performance at Landfills. Environmental Research and Education Foundation (EREF). https://erefdn.org/eref-funded-study-highlights-advances-in-measuring-landfill-methane-emissions

Dawson, K. W., Smith, B. J., Stocker, I., & Evans, P. (2024). Assessing the Application of Drone TDLAS Methane Emissions Monitoring Technology in the Intertropical Convergence Zone Using Machine Learning. APOGCE 2024. https://doi.org/10.2118/221317-MS

Gully-Santiago, M. A., Smith, B., Frederick, T., Dawson, K., & Elliott, D. (2025). Results and Learnings from the TADI 2024 Methane Quantification Trial. SPE Europe Energy Conference and Exhibition, Vienna, Austria. https://doi.org/10.2118/225634-MS

Discover how SeekOps combines its Ground Control Station with the FASTR system to stream real-time methane data, accelerate reporting, and give operators actionable insights during flight.

Read time: 5 minutes

Real-Time Awareness with GCS

When we fly a site, our UAV operators don’t have to wait hours, or days, to understand what they’re seeing. Our Ground Control Station (GCS) streams live data straight from the drone to the operator’s screen, giving immediate insight into emissions behavior.

This live dashboard includes:

• Methane concentration levels visualized as the drone flies.

• Drone position and altitude for precise context.

• Wind direction and speed from our on-site 3D ultrasonic anemometer.

• Active maps and flight paths overlaid on satellite imagery.

• Telemetry data like battery life and drone status for safe operations.

By combining environmental data, navigation details, and live methane concentration maps, the GCS empowers operators to make smarter decisions on the spot.

If a methane plume appears unexpectedly, the drone operators don’t just see it, they can adapt instantly, rerouting the drone to capture more detail, focusing on emission hotspots, or adjusting altitude to map the plume structure more completely.

FASTR: Accelerating Results Beyond the Field

While GCS is all about awareness during the flight, the FASTR system, released mid-2025, addresses what happens after the flight. Traditionally, emissions monitoring relied on lengthy post-processing workflows, sometimes taking weeks before results were ready. FASTR shortens that cycle dramatically.

By automating the ingestion, processing, and reporting pipeline, FASTR delivers results in minutes, not days, without requiring any cellular connection. The platform organizes data from multiple flights, integrates environmental parameters, and produces clear emissions quantification reports that are easy to interpret and act upon.

For operators and asset managers, this speed translates into immediate value through rapid response to unexpected or large leaks, integration with mitigation workflows so that identified leaks can be prioritized, and quick validation of repairs, allowing teams to verify success before leaving the site.

Why GCS + FASTR Matters

The real strength of SeekOps comes from combining the real-time situational awareness of GCS with the accelerated reporting power of FASTR. Together, they provide a seamless workflow from detection to action:

• In the field: Drone operators see emissions data live, with the ability to investigate plumes before the drone even lands.

• Post-flight: FASTR processes the mission data quickly, generating quick view quantification reports.

This dual system transforms emissions monitoring from a reactive process into a proactive management tool, enabling real-time awareness, rapid response, and quality assurance.

The Value of Speed and Certainty

In methane monitoring, speed is about more than convenience. Methane is over 80 times more potent than carbon dioxide in the short term, which means rapid detection and mitigation have a direct climate impact.

With GCS streaming and FASTR reporting, SeekOps helps clients move from data collection to climate action faster than ever before. Whether it’s an oilfield in Texas, a biogas plant in Europe, or an offshore platform in the Middle East, operators can trust that the data they’re seeing is accurate, timely, and actionable.

For industries under increasing regulatory pressure, from OGMP 2.0 requirements to the EU Methane Regulation, this combination provides both compliance support and operational efficiency.

Designed for Flexibility and Reliability

Both SeekOps apps are designed with field realities in mind:

• Drone-agnostic integration means SeekOps sensors and software work with multiple airframes, from small quadcopters to long-endurance fixed-wing drones.

• Lightweight payloads ensure minimal impact on flight performance.

• Independent power systems maximize flight time without draining the drone’s batteries.

• Built-in redundancy and QC checks reduce error margins and enhance reliability.

Whether operating in desert heat, offshore winds, or tropical humidity, SeekOps systems are tested to deliver dependable performance.

From Sky to Screen, From Data to Decisions

Methane monitoring has entered a new era. No longer do operators need to wait weeks for reports or rely on incomplete detection methods. With GCS providing live awareness and FASTR delivering accelerated results, SeekOps bridges the gap between measurement and mitigation.

Together, these tools give industries the power to act quickly, reduce emissions effectively, and meet the world’s growing demand for transparent, trustworthy climate data.

Stay tuned for the next post in our series: “Making Emissions Actionable – How SeekOps Reconciliation Reporting and Celsius Help Operators Close the Loop”

Ready to see FASTR in action?
Request a Live Demo today and discover how SeekOps helps you move from sky to screen—and from data to decisions—faster.

References

Corbett, A., & Smith, B. (2022). Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters. Atmosphere, 13(5), 804. https://doi.org/10.3390/atmos13050804

Dawson, K. W., Smith, B. J., Stocker, I., & Evans, P. (2024). Assessing the Application of Drone TDLAS Methane Emissions Monitoring Technology in the Intertropical Convergence Zone Using Machine Learning. APOGCE 2024. https://doi.org/10.2118/221317-MS

Smith, B. J., Buckingham, S., Touzel, D., et al. (2021). Development of Methods for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment Using a Miniature Methane Spectrometer and Long-Endurance UAS. SPE Annual Technical Conference and Exhibition, Dubai, UAE. https://doi.org/10.2118/206181-MS

Tavner, C. A., Touzel, D. F., & Smith, B. J. (2021). Application of Long-Endurance UAS for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment. SPE Offshore Europe Conference and Exhibition, Virtual. https://doi.org/10.2118/205467-MS

Ravikumar, A. P., Sreedhara, S., Wang, J., et al. (2019). Single-blind inter-comparison of methane detection technologies – results from the Stanford/EDF Mobile Monitoring Challenge. Elementa: Science of the Anthropocene, 7(1), 37. https://doi.org/10.1525/elementa.373

Gully-Santiago, M. A., Smith, B., Frederick, T., Dawson, K., & Elliott, D. (2025). Results and Learnings from the TADI 2024 Methane Quantification Trial. SPE Europe Energy Conference and Exhibition, Vienna, Austria. https://doi.org/10.2118/225634-MS

Discover how SeekOps designs intelligent drone flight paths and uses high-resolution imagery, orthomosaics, 3D models, and flux planes to assist in accurate, efficient methane emissions monitoring in any environment.

Read time: 5 minutes

Optimized Routes for Emissions Detection

Before we begin a project, we program detailed flight paths tailored to each site’s unique conditions. Every route is designed with two key priorities: maximizing methane plume coverage and ensuring operational safety.

Plans are built before our teams even arrive on site, using forecasted wind data to anticipate plume behavior and shape efficient flight patterns. If conditions shift, such as a sudden wind change, our skilled operators can generate new routes instantly, ensuring no data gaps and no wasted flight time.

This approach isn’t just about flying drones, it’s about flying the right path for the environment, infrastructure, and emissions target.

Flux Planes: Mapping Plumes in Motion

At the heart of SeekOps’ quantification strategy is the use of flux planes, or vertical slices of space where methane concentration and wind data are measured simultaneously. As the drone flies back and forth across these planes, it builds a precise, three-dimensional profile of the methane plume in real time.

Flux planes allow us to quantify the total mass flow rate of methane passing through the plane, capture plume shape, density, and movement under actual site conditions, and adapt flight patterns mid-mission if the plume shifts due to wind changes.

By integrating flux planes into our flight paths, we turn aerial surveys into dynamic, high-fidelity measurements. It’s not just about detecting a leak, it’s about understanding exactly how much methane is moving, where it’s going, and what’s driving it.

A Bird’s-Eye View

To support our detailed analysis and data presentation, we can capture high-resolution, up-to-date imagery that shows the site exactly as it is, whether that means newly installed process equipment, scaffolding, or temporary construction zones. This visual intelligence is far more reliable than outdated satellite images, ensuring that we have context that matches the reality in the field.

Orthomosaics: Detailed Maps for Analysis

While flight paths are the backbone of our approach, orthomosaic maps add a layer of precision. These are high-resolution, georeferenced images stitched together from hundreds of individual aerial photographs.

For SeekOps, orthomosaics are:

• Operationally current – showing the exact site layout on the day of the survey, including temporary installations, scaffolding, and safety barriers.

• Analysis-ready – allowing our findings to be overlaid and interpreted directly within operational workflows

• Reusable – serving as a reference for future surveys and change detection over time.

Orthomosaics are especially valuable for sites where outdated satellite imagery might show an empty lot but are actually a busy industrial environment.

3D Models: Adding Height to the Equation

In certain environments, such as offshore platforms, urban facilities, or sites with stacked infrastructure, 2D maps alone aren’t enough. That’s where 3D models come in.

Using photogrammetry from multiple angles, our drones can generate 3D representations of the site. This allows us to:

• Understand equipment height and elevation changes for better plume modeling.

• Accurately attribute emissions to the proper equipment groups.

• Depict where the methane plumes are coming from in our reports.

3D models are particularly effective in tight or congested areas where methane plumes may behave unpredictably due to airflow restrictions.

Efficiency in the Air

The drones fly these custom routes autonomously, but each one is backed by human expertise. Skilled operators know how to integrate wind data, site hazards, and plume modeling into the plan, balancing complete coverage with the shortest possible airtime.

This efficiency matters. It reduces battery use, minimizes exposure to harsh environments, and ensures that methane detection happens at the optimal time and place.

Consistency You Can Compare

Once a flight path has been proven effective, we save it for future surveys. This enables exact repeatability, allowing operators to track changes in emissions before and after maintenance, compare seasonal conditions, or analyze the same equipment over time. Consistency like this builds robust data sets and high-confidence trends, something extremely difficult to achieve with handheld tools or one-off aerial flyovers.

From Planning to Action

By pairing accurate site imagery, orthomosaics, and 3D modeling with intelligent flight path design, SeekOps ensures that every drone mission collects the most useful data in the least amount of time.

When combined with our real-time monitoring capabilities, this approach transforms aerial surveys into actionable intelligence, helping operators identify, quantify, and act on emissions faster than ever.

Stay tuned for the next post in our series: “FASTR Insights – Real-Time Awareness from Sky to Screen”

Want to see what methane emissions your site is emitting?

Schedule a survey or Talk to an expert

References

Corbett, A., & Smith, B. (2022). Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters. Atmosphere, 13(5), 804. https://doi.org/10.3390/atmos13050804

Smith, B. J., Buckingham, S., Touzel, D., et al. (2021). Development of Methods for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment Using a Miniature Methane Spectrometer and Long-Endurance UAS. SPE Annual Technical Conference and Exhibition, Dubai, UAE. https://doi.org/10.2118/206181-MS

Tavner, C. A., Touzel, D. F., & Smith, B. J. (2021). Application of Long-Endurance UAS for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment. SPE Offshore Europe Conference and Exhibition, Virtual. https://doi.org/10.2118/205467-MS

Hossian, R. I., et al. (2024). A Controlled Release Experiment for Investigating Methane Measurement Performance at Landfills. Environmental Research and Education Foundation (EREF). https://erefdn.org/eref-funded-study-highlights-advances-in-measuring-landfill-methane-emissions

Ravikumar, A. P., Sreedhara, S., Wang, J., et al. (2019). Single-blind inter-comparison of methane detection technologies – results from the Stanford/EDF Mobile Monitoring Challenge. Elementa: Science of the Anthropocene, 7(1), 37. https://doi.org/10.1525/elementa.373

Emissions data is only useful if you know how reliable it is. Here’s how SeekOps evaluates and improves confidence in methane measurements.

Read time: 5 minutes

Why Uncertainty Matters in Emissions Monitoring

In the world of methane detection and quantification, accuracy is only half the story. Just as important, is knowing how confident you can be in your results, and that’s where uncertainty comes in.

Uncertainty isn’t a flaw in a measurement; it’s an attribute necessary for measurement transparency. In any real-world measurement, especially in a dynamic outdoor environment, there’s always going to be a degree of uncertainty. At SeekOps, we take uncertainty seriously because it gives regulators, operators, and stakeholders a clear picture of how reliable an emissions estimate really is. It builds trust, enables better decision-making, and supports compliance with high-integrity reporting frameworks like OGMP 2.0 and other global methane standards.

In short, when the stakes are high, vague numbers aren’t enough. SeekOps reports how confident we are in those results. It’s data you can act on, with confidence grounded in precision.

What Is Measurement Uncertainty?

Measurement uncertainty is a calculated estimate of how much the actual value could differ from the reported result. In simple terms, it’s our way of saying:

“Here’s the number—and here’s how sure we are.”

As stated above, uncertainty is not an error or a flaw. It’s a necessary part of scientific honesty and provides context to any measured value.

What Causes Measurement Uncertainty?

There are several factors that contribute to uncertainty in all top-down emissions measurements (e.g. drone, aircraft, satellite, continuous, etc.), including:

• Sensor sensitivity and precision
  How precisely can methane concentrations be measured, especially at low levels? Sensitivity and calibration factors extend to the sensors measuring wind, temperature, pressure, and position.

• Environmental conditions
  Wind speed and direction, turbulence, temperature, and humidity can all affect how plumes behave.

• Plume geometry and flight path
  Complex terrain or poorly optimized flight paths can miss parts of the plume or cause the measured concentrations to be artificially lowered.

• Background methane levels
  Differentiating the source signal from regional background can be challenging.

• Model assumptions
  Any model-based approach requires assumptions about wind fields, dispersion, or boundary conditions which all carry their own uncertainties. However, SeekOps only utilizes a small number of assumptions due to our rigorous mass-balance approach.

SeekOps acknowledges and quantifies all of these to provide a realistic, statistically grounded picture of methane emissions.

How SeekOps Quantifies and Reduces Uncertainty

SeekOps applies a rigorous process when evaluating uncertainty in methane emissions measurements. Thanks to our ability to detect leaks as small as 0.02 kg/h, we can reliably observe virtually all measurable emission enhancements in the field. This exceptional sensitivity means that a probability of detection (PoD), a common industry metric, does not meaningfully apply, as our detection rate is effectively 100% (Ravikumar et al., 2019).

Instead of stopping at “we detected it,” we focus on how confident we are in the measured emission rate. That means identifying, quantifying, and transparently reporting the factors that influence uncertainty. Our estimation of confidence includes consideration of:

1. Controlled Release Testing

SeekOps has participated in dozens of blind controlled release trials from landfills to offshore platforms where known amounts of methane are released and measured. These trials serve as ground truth and provide hard data on the system’s accuracy and repeatability. Results consistently show SeekOps’ technology delivers low false-positive and false-negative rates and quantification within industry-accepted tolerances.

2. Environmental Profiling and Wind Modeling

Wind is one of the biggest variables in emissions monitoring. That’s why we pair our methane sensor with a 3D ultrasonic anemometer on site and apply local wind profile models, customized for each site’s surface roughness and topography.

This allows us to:

• Understand how methane plumes move across a site

• Improve the placement of control volumes

• Quantify variability in wind conditions over time

These insights are used to model uncertainty ranges for each quantification result. Recent research in Atmospheric Measurement Techniques (Mohammadloo et al., 2025) further supports this approach, showing that detailed wind profiling and adaptive plume sampling strategies are essential for reducing error margins in drone-based methane quantification.

3. Statistical Confidence Intervals

Rather than provide a single number, SeekOps delivers results with confidence bounds, typically expressed as a 95% confidence interval (2σ). This range reflects the potential variability in mass flow estimates based on real-world sampling conditions.

Incorporating uncertainty into the result isn’t just honest, it’s scientifically rigorous. It enables regulators and inventory teams to prioritize mitigation based on both emission rate and measurement reliability.

4. Cross-Site Comparisons and Continuous Improvement

With hundreds of deployments across 6 continents, SeekOps has built a robust internal benchmark of expected uncertainty under a variety of site types and conditions. We regularly update our models based on new data, seasonal trends, and learnings from large-scale campaigns like:

• The 2024 Controlled Landfill Release Trial (Hossian et al., 2024)

• Offshore OGMP Programs (Smith et al., 2021; Tavner et al., 2021)

• TADI 2024 Quantification Challenge (Gully-Santiago et al., 2025)

Field-Proven Performance

Our real-world uncertainty performance is validated by measured results, not estimates.

• In complex offshore environments, like bp’s Clair Phase 1 facility, our drone-mounted methane spectrometer measured emissions within ±20% of known release rates, with detection sensitivity down to 2.5 kg/h, even from 500 m away (Smith et al., 2021; Tavner et al., 2021).

• Independent studies at landfills and oilfields show our measurements closely match actual emissions, with agreement between platforms within 10–15% (Hossian et al., 2024; Corbett & Smith, 2022).

These results give operators the confidence that reported values reflect real conditions as opposed to modelled assumptions, enabling more reliable regulatory reporting and verification.

Transparency in Reporting

SeekOps integrates uncertainty bounds directly into reports and dashboards, whether through emission rate ranges (e.g., “12.4 ± 3.1 kg/h”), confidence classification for each source, documentation of assumptions and environmental conditions, or repeatability scoring across revisits or campaigns.

This transparency is essential for high-integrity emissions inventories and meets emerging global standards for ESG and methane reporting like OGMP 2.0 or the new EU Methane Regulation.

Reducing Uncertainty Over Time

Our measurement platform improves with every flight.

As we collect more field data, refine our models, and adjust flight strategies based on terrain and weather, we continually reduce our uncertainty margins, giving customers increasing confidence in our results.

We do it with the use of site-specific wind profiles from on-site anemometers, adaptive flight patterns to capture full plume geometry, real-time quality control during flight operations, and machine learning models to predict and minimize measurement error in complex environments.

Toward a More Confident Climate Future

As the world moves toward net-zero goals and increasingly rigorous climate disclosure frameworks, uncertainty isn’t a liability: it’s a strength.

By quantifying uncertainty, SeekOps empowers operators to prioritize mitigation based on both scale and confidence, communicate transparently with regulators and the public, and build robust emissions inventories that stand up to scrutiny.

Because in the end, reducing emissions isn’t just about knowing there’s a leak, it’s about knowing how much, how sure, and what to do next.

Stay tuned for the next post in our series: “Drone Deployment and Site Mapping – Smarter Surveys Start from the Sky.” And look out for our blog post covering dynamic uncertainty in the coming weeks!

Want to learn how SeekOps quantifies uncertainty and improves confidence in your methane data?

Ask us about our uncertainty and speak with an expert today

Image Credits

Figure 1 from Pérez-Díaz, L., Best, J., Gómez-Martín, F., Hodgson, D., Lang, A., Mather, A., McCarthy, D., & Thorpe, R. (2020). Introduction: Handling uncertainty in the geosciences: identification, mitigation and communication. Solid Earth, 11, 889–897. https://doi.org/10.5194/se-11-889-2020 — Licensed under CC BY 4.0.

References

Corbett, A., & Smith, B. (2022). Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters. Atmosphere, 13(5), 804. https://doi.org/10.3390/atmos13050804

Dawson, K. W., Smith, B. J., Stocker, I., & Evans, P. (2024). Assessing the Application of Drone TDLAS Methane Emissions Monitoring Technology in the Intertropical Convergence Zone Using Machine Learning. APOGCE 2024. https://doi.org/10.2118/221317-MS

Gully-Santiago, M. A., Smith, B., Frederick, T., Dawson, K., & Elliott, D. (2025). Results and Learnings from the TADI 2024 Methane Quantification Trial. SPE Europe Energy Conference and Exhibition. https://doi.org/10.2118/225634-MS

Hossian, R. I., et al. (2024). A Controlled Release Experiment for Investigating Methane Measurement Performance at Landfills. Environmental Research and Education Foundation (EREF). https://erefdn.org/eref-funded-study-highlights-advances-in-measuring-landfill-methane-emissions

Mohammadloo, T. H., Jones, M., Van De Kerkhof, B., Dawson, K., Smith, B. J., Conley, S., et al. (2025). Quantitative estimate of sources of uncertainty in drone-based methane emission measurements. Atmospheric Measurement Techniques, 18, 1301–1325. https://doi.org/10.5194/amt-18-1301-2025

Ravikumar, A. P., Sreedhara, S., Wang, J., et al. (2019). Single-blind inter-comparison of methane detection technologies – results from the Stanford/EDF Mobile Monitoring Challenge. Elementa: Science of the Anthropocene, 7(1), 37. https://doi.org/10.1525/elementa.373

Smith, B. J., Buckingham, S., Touzel, D., et al. (2021). Development of Methods for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment Using a Miniature Methane Spectrometer and Long-Endurance UAS. SPE Annual Technical Conference and Exhibition. https://doi.org/10.2118/206181-MS

Tavner, C. A., Touzel, D. F., & Smith, B. J. (2021). Application of Long Endurance UAS for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment. SPE Offshore Europe Conference and Exhibition. https://doi.org/10.2118/205467-MS

See how SeekOps calculates emissions using real-time methane concentration and wind data, based on conservation of mass.

Read time: 5 minutes

Why Measuring Emissions Matters

Finding a leak is only half the story, knowing how much is leaking is what truly makes a difference.

In the world of emissions monitoring, quantification refers to the process of calculating how much methane is being released, by volume or mass, from a facility or piece of equipment. Whether it’s a small valve or a large storage tank, understanding the magnitude of a leak is critical for regulatory reporting, prioritizing repairs, and making informed decisions on environmental performance.

With climate regulations like OGMP 2.0 and EU MR being enforced more rigorously, and ESG targets growing more ambitious, accurate quantification isn’t just helpful, it’s essential.

From Sensing to Sizing: How It Works

SeekOps begins with drone-based surveys using our SeekIR^® methane sensor. The drone flies a planned pattern over the site while continuously collecting real-time concentration measurements of methane in the atmosphere.

But detecting methane in the air doesn’t immediately tell us how much is being released at the source. We leverage a combination of advanced tools, including on-site wind measurements from ground sensors and real-time wind data collected directly by the drone’s onboard anemometer. This integration ensures that our quantification is based on actual wind conditions rather than assumptions which is a critical distinction that enhances accuracy, particularly in complex or variable environments. That’s where quantification algorithms come in.

We use a combination of:

• Atmospheric modeling (to account for wind and dispersion),

• Sensor positioning data (to locate the plume in 3D space),

• Concentration readings (to measure the strength of the signal),

• and flight telemetry (to understand how the drone moved during measurement).

We collect this data using our drone-mounted sensor and a high-resolution 3D anemometer placed on site. Then we apply proven mathematical models to turn those data points into a mass flow rate that is usually expressed in grams per second or standard cubic feet per hour.

It’s Not Guesswork: It’s Physics

Quantifying emissions involves applying principles of fluid dynamics, gas dispersion modeling, and mass balance equations. Think of it like reverse-engineering a puzzle: we see the effects in the air and work backward to figure out what kind of leak caused them.

Key considerations include:

• Plume height and width

• Ambient wind speed and direction

• Stability of atmospheric conditions

• Distance from the source

Our system adapts in real-time to changing field conditions and uses validated models that have been peer-reviewed and tested at facilities like METEC (Methane Emissions Technology Evaluation Center).

Using the Law of Conservation of Mass

Our measurements rely on a simple, powerful idea: what goes in must come out.

We define an invisible box, or “control volume”, around a facility or piece of equipment. By measuring the air and wind conditions upwind and downwind of this box, we can calculate the difference in methane and determine how much is leaking inside.

This approach is grounded in the conservation of mass, one of the most fundamental laws in physics.

Accuracy You Can Trust

At SeekOps, we’re proud that our quantification system has been third-party validated in blind testing environments, peer-reviewed in academic literature, and deployed in over a dozen countries and diverse climates.

Each SeekIR^® sensor undergoes rigorous calibration and environmental validation, including testing across humidity (0–95% RH) and temperature (-20°C to 55°C) ranges. This ensures the system performs in extreme field conditions, whether in Arctic oilfields or equatorial landfills.

In independent comparisons, SeekOps has consistently demonstrated low measurement uncertainty and high repeatability, even for low-level emissions.

Scalable and Repeatable Data

One of the key benefits of our quantification process is that it’s repeatable over time. This allows facility operators to track emissions reductions after repairs, compare performance across assets or regions, demonstrate emissions improvement for ESG reports or regulatory compliance, and plan maintenance around the highest-volume sources first.

SeekOps leads the industry in application of these approaches with regard to the standardization of workflows, enabling compliance with a wide variety of initiatives globally. By turning emissions into measurable trends, we help our partners move from reactive to proactive emissions management.

Supporting Methane Intensity and Reconciliation

SeekOps quantification feeds into broader metrics like Methane Intensity (MI) or reconciliation of emissions to various, complex emission sources. With accurate site-level data, operators can benchmark performance, calculate carbon equivalencies, and report to frameworks like OGMP 2.0, EU MR, or EPA GHGRP.

Our quantification data can also support reconciliation with bottom-up inventories and mass balance models. This enables companies to align measurement-based and inventory-based methods more effectively, which is crucial for verification and audit-readiness.

Quantification Is Climate Action

The ability to quantify methane accurately transforms environmental responsibility from a guess into a guarantee. With SeekOps, operators gain the clarity to prove performance, meet compliance, and reduce emissions at scale—enabling operators to produce energy sustainably and responsibly. Every leak quantified is a step toward a cleaner, more transparent energy future.

Stay tuned for the next post in our series: “Understanding Uncertainty – How SeekOps Quantifies Confidence.”

Ready to turn emissions data into actionable insights?
Schedule a Walkthrough or Learn About Our Quantification Methods

Image Credits

Ken Doerr, Methane Emissions from Oil Tank, Flickr, Creative Commons Attribution 2.0 Generic (CC BY 2.0).

Equation Formula Math Physics Science Poster, Wallpaper Flare.

References

Corbett, A., & Smith, B. (2022). Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters. Atmosphere, 13(5), 804. https://doi.org/10.3390/atmos13050804

Mohammadloo, T. H., Jones, M., Van De Kerkhof, B., et al. (2024). Quantitative Estimate of Sources of Uncertainty in Drone-Based Methane Emission Measurements. https://doi.org/10.5194/egusphere-2024-1175

Dawson, K. W., Smith, B. J., Stocker, I., & Evans, P. (2024). Assessing the Application of Drone TDLAS Methane Emissions Monitoring Technology in the Intertropical Convergence Zone Using Machine Learning. APOGCE 2024. https://doi.org/10.2118/221317-MS

Hanson, R. K., Spearrin, R. M., & Goldenstein, C. S. (2016). Spectroscopy and Optical Diagnostics for Gases (Vol. 1). Springer. https://link.springer.com/book/10.1007/978-3-319-23252-2

Ravikumar, A. P., Wang, J., Sreedhara, S., et al. (2019). Single-blind inter-comparison of methane detection technologies: Results from the Stanford/EDF Mobile Monitoring Challenge. Elementa: Science of the Anthropocene, 7(1), 37. https://doi.org/10.1525/elementa.373

Discover what methane plumes are, how they form, and why detecting them matters for climate change, regulatory compliance, and sustainability goals.

Read time: 5 minutes

Invisible Emissions with Big Consequences

You can’t see methane with your eyes, but that doesn’t mean it’s not there.

Methane is a colorless, odorless gas that escapes from oil and gas operations, landfills, wastewater plants, and agricultural sites around the world. When it leaks into the atmosphere, it often forms what’s known as a plume, a drifting cloud of methane that moves and disperses with the wind.

These plumes can vary in size, shape, and intensity, but they all represent unaccounted emissions, and in many cases, significant sources of greenhouse gases. Methane is over 80 times more potent than carbon dioxide at trapping heat over a 20-year period, which makes its early detection and quantification a critical tool in mitigating climate change.

So, What Exactly Is a Plume?

A plume is simply a section of air where methane concentrations are higher than normal due to a nearby emission source.

Think of it like smoke from a fire: as methane leaks out of equipment or piping, it gets caught in the wind and begins to drift. This creates a “cloud” of gas, but unlike smoke, methane is invisible to the naked eye.

The size and direction of a plume are influenced by several things such as wind speed and direction, atmospheric temperature and humidity, terrain and structures (buildings, tanks, trees), and emission rate and duration.

Because of these variables, plumes can be short and dense, or long and diffuse. And unless you have specialized equipment, they can go completely unnoticed.

How Plumes Are Detected

Detecting a methane plume requires remote sensing technology, or tools that can scan the air in place and identify gas concentrations without needing to interfere with industrial operations.

SeekOps uses a highly sensitive laser-based sensor mounted on drones. The sensor works using wavelength modulation spectroscopy, which allows us to “see” the methane in the air by measuring how laser light is absorbed as it passes through the gas.

Our drones fly over facilities in planned patterns, creating a 3D map of methane concentration in space and time. This not only identifies the plume but also helps trace it back to its source.

A top-down illustration of methane puffs as an unmanned aerial vehicle (UAV) moves through the plume.

Going Where Others Can’t

Our drones can fly close to sources (like tanks and flares) without interfering with operations, over uneven terrain and over water, which is nearly impossible for ground teams, and at high altitudes, sometimes up to 50 meters above the ground, to catch the full vertical profile of the plume.

The drone systems are equipped with anti-collision sensors, pre-programmed flight paths, and built-in no-fly zones. Meaning we can safely conduct surveys without flying over people or sensitive equipment.

By being mobile, we can adapt to changing winds, reach difficult areas, and make sure no emission goes unnoticed.

Where Do Methane Plumes Come From?

Methane plumes can come from a wide variety of sources, including:

• Oil and gas equipment (leaky valves, tanks, pipelines)
• Abandoned or orphaned wells
• Landfills and composting sites
• Wastewater treatment plants
• Biogas and RNG facilities
• Agricultural operations (especially manure and rice cultivation)

Sometimes, plumes form from routine operations such as tank venting or flaring. Other times, they result from accidental or fugitive leaks. Either way, detecting and quantifying these plumes is the first step toward managing and reducing emissions.

Visualization of how air flows impact the movement of methane plumes.

What Plumes Tell Us

Each plume tells a story.

A small, consistent plume might indicate a slow leak from a valve. A large, concentrated plume could point to a sudden release or equipment failure. In some cases, operators may not even know a leak exists until it’s detected by an aerial survey.

By mapping the plume’s shape and location, we can estimate where it started and how fast methane is escaping.

This data is crucial for regulatory reporting, operational efficiency, safety, and ESG compliance.

Why Plumes Matter for Climate and Compliance

Methane is responsible for nearly 30% of global warming to date, and many governments are introducing stricter rules to reduce emissions across industries.

Plume detection helps meet these regulations in several ways:

• Verifiable Measurement: Proves that you know your emissions footprint.
• Source Identification: Supports root cause analysis and repair.
• Trend Tracking: Shows whether emissions are increasing or decreasing over time.
• Reporting Accuracy: Backs up regulatory submissions with real data.

And beyond compliance, every plume that’s identified and stopped means fewer greenhouse gases in the atmosphere and less lost product, translating to cost savings and climate benefits.

What We Do with the Data

Once a plume is detected and mapped, SeekOps converts the raw methane concentration measurements into quantitative emissions estimates. These estimates tell operators exactly how much methane is escaping—not just that a leak exists.

We also integrate this data into dashboards and analytics tools that can be used by field technicians, operations managers, and ESG teams. Combined with GPS coordinates, timestamps, and wind data, plume tracking becomes a powerful decision-making tool.

Making the Invisible Visible

In the fight against climate change, one of the biggest challenges is invisible emissions. Methane plumes are real, measurable, and impactful, but only if you know where to look.

With the SeekIR^® sensor and drone-based measurements, SeekOps helps customers visualize, quantify, and eliminate emissions that would otherwise go unnoticed.

From compliance to climate responsibility, understanding methane plumes is a key step toward a lower-emissions future.

Next up: “Turning Data into Insight – Quantifying Emissions with Accuracy.”

Think your facility might benefit from drone-based emissions mapping?
Talk to a Specialist or Get a Sample Survey Report.

Image Credits

Plume dispersion diagram: Adapted from U.S. Environmental Protection Agency (EPA), AERMOD: Description of Model Formulation, EPA-454/R-03-004. Retrieved from https://www.epa.gov/scram/air-quality-dispersion-modeling-preferred-and-recommended-models

Industrial smokestack with visible emissions: Image by Christine Matthews. Retrieved from Geograph UK: https://s0.geograph.org.uk/geophotos/03/22/03/3220320_1df4aa5c.jpg

References

Corbett, A., & Smith, B. (2022). Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters. Atmosphere, 13(5), 804. https://doi.org/10.3390/atmos13050804

Smith, B., Buckingham, S., Touzel, D., et al. (2021). Development of Methods for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment Using a Miniature Methane Spectrometer and Long-Endurance UAS. Paper presented at the SPE Annual Technical Conference and Exhibition, Dubai, UAE. https://doi.org/10.2118/206181-MS

Tavner, C.A., Touzel, D.F., & Smith, B.J. (2021). Application of Long Endurance UAS for Top-Down Methane Emission Measurements of Oil and Gas Facilities in an Offshore Environment. Paper presented at the SPE Offshore Europe Conference and Exhibition, Virtual. https://doi.org/10.2118/205467-MS

Webster, C. R. (2005). Measuring methane and its isotopes 12CH₄, 13CH₄, and CH₃D on the surface of Mars with in situ laser spectroscopy. Applied Optics, 44(7), 1226–1235. https://doi.org/10.1364/AO.44.001226

Dawson, K. W., Smith, B. J., Stocker, I., & Evans, P. (2024). Assessing the Application of Drone TDLAS Methane Emissions Monitoring Technology in the Intertropical Convergence Zone Using Machine Learning. In APOGCE 2024 (p. D031S020R003). Perth, Australia: SPE. https://doi.org/10.2118/221317-MS

Gully-Santiago, M. A., Smith, B., Frederick, T., Dawson, K., & Elliott, D. (2025). Results and Learnings from the TADI 2024 Methane Quantification Trial. Paper presented at the SPE Europe Energy Conference and Exhibition, Vienna, Austria. https://doi.org/10.2118/225634-MS