The Challenges of Managing Emissions Data: Bridging the Gap Between Measurement and Operations

Leveraging Automation and Data Integration to Transform Emissions Management in Oil and Gas

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In the oil and gas industry, advancements in emissions monitoring technologies have led to a wealth of data from multiple sources—on-site sensors, aerial surveys, and satellite observations. However, while this multi-sensor approach offers valuable insights, the lack of correlation between emissions and operational data is a significant problem. Without this critical link, operators cannot perform effective root-cause analysis, limiting their ability to reduce emissions.

Moreover, all emissions measurement systems come with inherent uncertainty. Not all sensors provide continuous uptime, aerial surveys often struggle with accurate quantification, and satellite data is limited by its spatial and temporal resolution. Addressing these issues requires better data integration and automating the manual workflow logic necessary to act on these insights in real time.

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The Growing Complexity of Emissions Data

​​As emissions monitoring technologies advance, the industry faces an increasingly complex landscape for data collection and interpretation. Multi-sensor environments—combining on-site sensors, aerial surveys, and satellite observations—generate vast amounts of data, offering a more comprehensive view of emissions than ever before. However, this abundance comes with challenges: the diverse and disparate data sources create complexities in data integration, consistency, and analysis. The sheer volume and variety make it difficult for operators to obtain a unified and clear understanding of emissions events.

1. On-site Sensors: Installed directly on equipment, sensors provide detailed emissions data at the asset level, but they present challenges:

• Downtime and Data Gaps: Sensors don’t always run at full capacity. Equipment failures, maintenance, and network disruptions can lead to periods where no data is collected.
• Uncertainty in Readings: The accuracy of sensor data can vary depending on calibration, environmental conditions, and placement. These issues add layers of uncertainty when interpreting the data.

2. Aerial Surveys: Aircraft and drones can offer broader emissions visibility, yet they bring their own set of limitations:

• Uncertain Quantification: Aerial surveys may only sometimes accurately measure the concentration of emissions, and environmental factors like wind and temperature can distort the data.
• Limited Temporal Coverage: These surveys provide only intermittent snapshots, making capturing transient emissions or matching data to specific operational activities difficult.

3. Satellites: Satellites provide a large-scale view of emissions, but they lack the granularity needed for operational decision-making:

• Resolution Limits: Satellites are great for detecting regional emissions, but their spatial resolution is often too coarse to pinpoint minor leaks at individual facilities.
• Time Lags: Satellite data is only sometimes collected in real-time, which makes it hard to connect emissions events with specific operational conditions.

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The Need for Correlation with Operational Data

While emissions data provides critical information, it’s only one-half of the equation. Operational data—collected continuously at most oil and gas facilities—includes metrics like pressure, flow rates, temperature, and equipment status. These operational metrics are essential for understanding the context in which emissions events occur.

For example, a spike in emissions might coincide with a maintenance activity, an equipment failure, or a sudden change in pipeline pressure. Companies cannot diagnose the cause of emissions events without correlating emissions data with these operational conditions.

By integrating emissions data with operational data, operators can:

• Automate Root-Cause Analysis: Instead of relying on manual investigation, automated workflows can immediately correlate emissions events with operational anomalies, speeing up diagnosis and response.
• Enable Targeted Response: When operational data is linked with emissions monitoring, operators can prioritize actions and target specific equipment or processes causing emissions events.

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Accounting for Uncertainty in Emissions Measurements

No measurement system is perfect, and emissions data is inherently subject to uncertainty:

• Sensor Data: Even with robust sensor networks, downtime, and calibration errors can result in incomplete or inaccurate readings.
• Aerial and Satellite Data: Environmental factors such as wind or temperature distort the accuracy of aerial and satellite measurements, while the periodic nature of their data collection can miss critical emissions events.

The only way to manage this uncertainty effectively is to integrate emissions data with operational data, providing the necessary context to interpret and act on emissions events.

Automating the Manual Workflows Around Emissions Management

One of the most time-consuming aspects of emissions management is the manual analysis required to correlate emissions events with operational data. Traditionally, teams must manually coordinate emissions data, operational reports, and other relevant logs to analyze root causes. This manual process is not only slow but also prone to human error.

Automating this workflow offers a far more efficient approach:

• Real-time Triggers: Set up automated workflows that trigger immediate action when an emissions event is detected. These workflows can automatically pull relevant operational data and correlate it with emissions measurements, enabling faster response times
• Conditional Logic: Create logic-based workflows that handle different types of emissions events in different ways. For example, a small leak might trigger a maintenance request, while a more significant emissions event could initiate an immediate shutdown procedure.
• Consistent Documentation: Automated workflows ensure that every emissions event and the corresponding operational conditions are logged and documented consistently, improving auditability and compliance.

Final Thoughts: Closing the Loop on Emissions and Operations

Managing emissions in the oil and gas industry requires more than collecting emissions data—it demands that emissions data be correlated with operational data to understand the full picture. With the added challenges of data uncertainty and inconsistent uptime from emissions monitoring systems, it's clear that automation is critical to streamlining emissions management.

By integrating and automating the correlation of emissions data with operational conditions, companies can eliminate manual workflows, improve the accuracy of root-cause analysis, and respond to emissions events more effectively. This data-driven approach is crucial for reducing emissions in a meaningful and sustainable way.

SensorUp’s platform addresses these challenges by providing the tools to automate the workflow logic that bridges emissions data with operational data, enabling operators to take real-time action with greater confidence.

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