Multi-Modal Emissions Measurement for Facility Leak Mitigation
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Solution Overview
Problem
The challenge in the oil and gas industry is to effectively capture and manage emissions at a spatio-temporal scale, including both major and diffuse intermittent emissions, to facilitate responsible emissions reduction and regulatory compliance.
Innovation Solution
A system and method for integrating emissions data from various sources using sensors and simulations to generate emissions models, enabling real-time detection, visualization, and automated mitigation strategies, utilizing a signal processing engine to optimize emissions management and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If emissions data is captured from multiple sensor sources and formatted into structured records, then measurement precision and data reliability improve, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments emissions data processing by creating distinct functional modules: sensor data acquisition, data formatting into standardized records, emissions event detection, inventory generation, and model execution. Each module handles specific aspects of the lifecycle, reducing overall system complexity while maintaining measurement precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces intermediate data structures (emissions records with standardized fields) that mediate between raw sensor data and complex emissions models. These records serve as intermediaries that organize and validate data before processing, enabling precise measurements without requiring direct complex processing of all raw sensor inputs simultaneously.
2Productivity
If emissions records are tracked continuously over duration based on emissions events, then productivity and emissions management efficiency improve, but loss of time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining emissions record structures, pre-configuring emissions models with relevant parameters, and pre-establishing tracking criteria for emissions events. This preparation enables efficient real-time processing during operations without requiring complex computations to be developed and validated during active emissions management.
Solution Approach 2:
The patent implements periodic tracking of emissions records based on emissions events rather than continuous monitoring of all parameters. The system selectively tracks records when emissions events occur (flares, vents, leaks, blowouts), reducing computational burden while maintaining productivity by focusing processing resources on relevant temporal intervals.
3Reliability
If emissions models are generated and executed for simulation, then reliability of emissions prediction improves, but device complexity and computational requirements increase
Solution Approach 1:
The system applies local quality by parameterizing emissions models with data specific to each emissions source type (flares, vents, leaks, blowouts) rather than using uniform models for all sources. Each model is tailored to the local characteristics of the emissions source being simulated, improving prediction reliability while managing complexity through specialized rather than universal modeling approaches.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting emissions model parameters based on tracked emissions record data. The models are parameterized with actual measurements from sensors (emission rates, source characteristics, environmental conditions) rather than relying on fixed theoretical values, improving reliability while keeping model structures manageable through data-driven parameter adjustment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient, real-time emissions detection and mitigation, improving data visibility and compliance by providing accurate emissions maps and models, facilitating automated reporting and mitigation operations.
Implementation Method 1
the one or more sensors comprise one or more of: Lidar emissions sensors; camera emissions sensors; sniffer sensors, drone sensors; or satellite sensors
Data Source
AI summary
The disclosed methods and systems are directed to mitigating against one or more emissions events at a facility. The method comprises receiving first emissions data associated with the facility and formatting same based on a predefined data structure or a source type associated with the plurality of emissions sources of the facility. The method further comprises tracking using one or more sensors a plurality of emissions records generated using the emissions data. The data from the tracked emissions records may be used to generate an emissions inventory that may be used to generate one or more models which are used in one or more simulations to generate second emissions data. The second emissions data may enable tracking of one or more emissions sources associated with the facility. The second emissions data may enable execution of control operations that mitigate against one or more emissions sources associated with the facility.