Optimize Emissions Management: The Science behind SOOFIE Quantity and Placement

Efficient and effective leak detection depends not only on the quality of the instruments, but also on finding the optimal sensor quantity and placement to meet your needs. Determining the best location and number of sensors depends on a multitude of factors including:

• Unique site shape and size
• Site-specific equipment layout
• Proximity to off-site methane sources
• Localized wind speed, direction, and frequency
• Your budget, detection goals, and alert thresholds

As a result, determining the best solution for Continuous Emissions Monitoring at your site is not a one-size-fits-all answer, but requires a scientific approach that is customized towards your specific site and needs. Below are a few qualities that our optimized sensor placement algorithm incorporates to determine the optimal solution for you.

The ChampionX Advantage to Optimized Sensor Placement

• Adjustments to leak likelihood: Many sensor placement algorithms assume one central leak location per instrument. However, we argue that doing so does not adequately reflect the likelihood of site-wide leaks. Our optimization approach allows you to customize site-wide leak likelihood based on both equipment size and site-specific knowledge of equipment (e.g. Tank A is known to be the most likely culprit of leaks) for ideal sensor placement.
• Placement that Works for Your Site: There can be a difference between ideal sensor placement and realistic sensor placement. Although a sensor may be theoretically optimal at a given spot, perhaps that spot is in the middle of a highly trafficked road. Our algorithm allows for the incorporation of exclusion zones bringing adaptability to optimization.
• Accounts for Physical Obstructions: Each site has unique placement of equipment, as well as wind conditions. We integrate these two factors, as well as accounting for equipment obstructions to plume movement, to determine the ideal sensor placement.
• Optimized Placement for Resolving Off-Site Emissions: Your given site may be close to another off-site emitter, whose emissions you do not want incorporated into your site’s calculations. Our algorithm allows you to identify off-site emitters, so that sensors can be strategically placed to detect and isolate emitting sources by distinguishing between on-site and off-site emissions.
• Flexible Placement Schemes: Depending on site size, the impact of equipment obstruction to plume movement, and client needs, you can choose between fence line-based sensor placement, or internal site placement.
• Easy Integration of Available Data:
  • The algorithm prompts the user to interactively draw in site-specific information such as site boundaries, off-site sources, equipment location, and exclusion zones. This allows for direct and easy incorporation of site-specific information into our algorithm.
  • If your site has wind data, you can simply upload a .csv file of that data. If it does not, we search both public airport wind records and leverage existing ChampionX anemometer data (from >2,000 sites worldwide) to obtain wind data close to your site.
  • Updated site imagery: If you have a georeferenced image of the site, simply upload it! No image, no problem! You can either create one using Google Earth Pro or take advantage of our partnership with Planet Labs to task a satellite and quickly integrate real-time imagery into sensor placement configuration.
• Unique Placement Options for Your Site: An important factor of site placement that cannot necessarily be readily outlined is the impact of the surrounding topography. Whereas wind distribution at a site 1km away in the Permian Basin may be a good representation of your site, this may not hold true if your site is in a valley of the Appalachian Basin. We therefore provide the user with several placement scenarios that vary based on how much the user wants to value the wind data for sensor placement.
• Determining Optimum Quantity of Sensors: In our simulations, we ask the user to indicate the range of potential instruments they would consider placing. We run our optimization algorithm across this range to show the customer the change in Percent of Detection of adding or removing a sensor. This allows the customer to evaluate the ideal sensor quantity based on their specific site needs, site layout, and project budget.
• Enhanced Field Service Coordination: The optimization workflow will efficiently bring all the drawn-in equipment locations and entered site-specific information into our dashboard, simplifying the coordination between your team, installers, and our customer service team. The algorithm produces a map of optimum sensor placement with GPS coordinates, limiting confusion or miscommunication of final placement locations between you and the installer. If you choose to upgrade to our new, GPS-enabled SOOFIE® 4.0 sensors, the final installed sensor location will be updated live on the dashboard, to provide you with the most accurate data.

The below 3 images show the initial percent of detection, and how we calculate the remaining percent of detection after iteratively placing a SOOFIE on the site. The golden stars=where we would place a SOOFIE optimally.

The below image is a wind rose diagram, which we use to simulate the percentage of detection based on wind and equipment/component configuration.

We are excited to provide you with an enhanced and data-driven approach for effective leak detection. Beginning February 26th, you can reach out to our Customer Success team to start optimizing sensor placement for new and existing sites. While the direct User Interface version is under development and coming soon, our customer success team can get you up and running.