The Open Geospatial Consortium (OGC) is establishing a Standards Working Group (SWG) to develop EmissionML, a standardized framework for emissions data interoperability focusing on methane. This initiative aims to create a unified data model that facilitates seamless integration and exchange of emission events and associated observational data from various systems, such as satellite sensors, flyovers, drones, and ground-based continuous monitoring systems. By addressing current challenges in data fragmentation, EmissionML will enable more accurate and efficient Measurement, Reporting, and Verification (MRV) of emissions, which is critical for regulatory compliance, voluntary initiatives such as OGMP 2.0 and MiQ, and methane reduction efforts in the oil and gas industry. Public input on the draft charter is being sought, with a deadline for comments set for September 2, 2024, allowing stakeholders across the sector to contribute to shaping the standard.

Why it Matters:

Methane is one of the most potent greenhouse gases, with a global warming potential significantly higher than CO2. Despite the urgent need to curb emissions, data fragmentation across various monitoring technologies—such as satellite, drone, and ground-based continuous monitoring sensors—creates inefficiencies and uncertainties in emissions tracking. EmissionML aims to eliminate these gaps by offering an ontology and a unified data model for seamless communication between disparate emission data sources, enabling quicker and more accurate mitigation.

Technical Breakdown:

• Conceptual model: EmissionML will develop an ontology and the logical UML model for emission events and their related entities, such as methane emissions observations, the procedures used to conduct the observations, and the geographical features responsible for emitting emissions. This framework will streamline how methane emissions data is modeled and communicated across different platforms, reducing inconsistencies and uncertainties in emissions reporting. 
• JSON encoding: A key deliverable of the EmissionML initiative is developing the physical JSON encoding standard, grounded in the aforementioned ontology, for methane emissions. This will facilitate the exchange of methane-related data in a data exchange format that is both human-readable and machine-processable, making it easier for developers and AI models to integrate and process emissions data in real-time.
• Best practices: EmissionML will also deliver a series of best practice documents that outline how to integrate existing OGC standards for methane observation and data sharing. This includes detailed guidance on incorporating data from systems such as continuous monitoring sensors, remote sensing platforms, and Optical Gas Imaging (OGI) cameras.

Industry Impact:

• Methane's impact: Methane has a short atmospheric lifespan but high global warming potential, making it a priority for rapid emissions reductions. Immediate cuts in methane emissions can significantly impact slowing global warming.
• Data fragmentation: Existing methane reporting methods vary, creating challenges in integrating sensor observations, satellite data, and ground-based systems. These discrepancies lead to inefficiencies and delays in taking action on emissions reduction.
• Uncertainty reduction: The initiative will include modeling uncertainty in emissions and sensor observations, providing a clearer picture of the accuracy of methane emissions quantification. This is crucial for high-stakes decision-making in regulatory compliance and environmental reporting.

Who Benefits:

• Oil and gas operators: EmissionML will simplify how they gather and report emissions data from multiple sources, ensuring compliance with methane reduction commitments. Standardizing data integration across platforms allows operators to focus on more efficient emissions reduction strategies rather than manual data crunching.
• IT teams: Enterprise IT teams prioritize standards-based solutions whenever possible, but the lack of established IT standards for emissions management has been a significant challenge. EmissionML addresses this gap by future-proofing emissions management systems, reducing Total Cost of Ownership (TCO), preventing vendor lock-in, enhancing customizability and flexibility, and enabling seamless integration and interoperability.
• Sensing service providers: EmissionML provides a standardized way to format and transmit emissions data, ensuring sensor technologies integrate seamlessly with broader systems. This allows sensing service providers to expand their product offerings and create new partnerships, as their sensors will be compatible with a wider range of monitoring platforms.
• Data platform providers: EmissionML reduces the need for custom-built data connectors and manual data integration for emissions management platforms. Using standardized JSON encoding, data platform providers can more easily incorporate methane emissions data from various sources, focusing on building advanced analytics and insights to drive emissions reduction efforts.
• Software developers: Those building software solutions for emissions monitoring can leverage EmissionML to create more effective analytics pipelines, benefiting from a unified language for communicating with diverse sensor technologies. The standardization ensures compatibility across different monitoring systems and data sources, making delivering real-time, actionable insights into emissions management easier.
• Regulators and environmental stakeholders: The standardization offered by EmissionML provides regulators with a more accurate and consistent view of methane emissions data. This supports better decision-making and ensures compliance with international environmental goals.

Key voices:

Steve Liang, PhD, founder and CTO of SensorUp, is a lead figure in the EmissionML initiative. He highlights the significance of creating a unified standard for emissions data, emphasizing that "EmissionML will enable seamless integration of emissions data, making it easier to monitor, report, and verify methane emissions."

Final Thought:

EmissionML will standardize how methane emissions data is modeled, shared, and processed, benefiting software engineers, data platform providers, and oil and gas developers. By providing a robust framework for data interoperability and uncertainty modeling, the initiative will drive more accurate and efficient methane emissions management, helping stakeholders meet both regulatory and environmental goals, such as OGMP 2.0.

What's next:

Public input will be crucial as the OGC develops the EmissionML standard. The EmissionML Standards Working Group draft charter is open for comment until September 2, 2024. This is an opportunity for stakeholders across the methane emissions management ecosystem to contribute to developing a standard that will shape the future of emissions management.

Please submit your comments via this email address, using this Comments Template for the message body.

For more information visit OGC's website.

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