Qube’s Data Driven Approach to Optimizing Sensor Placement
TL;DR
Strategic sensor placement is critical for reliable, real-time methane detection. Detection depends not just on technology, but on understanding wind behavior, emission sources, and site constraints.
Qube’s deployment methodology uses historical wind data, source mapping, and MILP-based optimization to design sensor arrays that maximize detection coverage and minimize false negatives.
The Qube deployment tool turns this methodology into a practical workflow—helping operators visualize trade-offs, configure sensor layouts, and balance detection accuracy with budget.
Introduction
Effective methane monitoring in real time means having both accurate sensors and knowing where to place them to maximize performance. Ensuring proper coverage requires a strategic understanding of where emissions are likely to originate and how they will move across a site.
At Qube, we’ve developed a data-driven deployment methodology designed to help operators increase detection probability, improve quantification, and stay within operational budgets.
Ideally, operators could run weekly aerial surveys or blanket their facilities with dense sensor arrays. But cost and scale make that impractical – especially for operators managing multiple assets. Effective monitoring hinges on optimized sensor placement: positioning devices to catch emissions as they happen and locate where they’re coming from.
This article outlines the core considerations for continuous monitoring deployment. We’ll explore how site layout, equipment, wind patterns, and terrain affect sensor performance, describe Qube’s modeling approach to placement optimization, and show how smarter deployment translates into more reliable, scalable methane monitoring.
Why Placement Strategy Matters
Real-time methane monitoring only works if sensors are positioned strategically. Even the most advanced device can miss a leak if it’s not in the right place at the right time. This is especially true for intermittent or short-duration emissions, which can slip past poorly placed sensors.
Wind plays a critical role. A sensor upwind of an emission is essentially blind. While we can’t control wind behavior, we can model it. By analyzing long-term wind data, we can make reasonable statistical assumptions about future conditions. This allows us to predict where plumes are likely to travel and position sensors accordingly.
Effective placement ensures high detection coverage and source localization while staying within operational and budget constraints.
Key Considerations
Emission Source Mapping
Methane rarely escapes uniformly across a site. It originates from specific equipment—separators, tanks, flares, compressors. Mapping these high-risk sources is the foundation of any monitoring strategy. But it’s not just the static infrastructure that matters. Operational events like tank loadouts or maintenance can cause short-lived, high-rate releases. Identifying where and when these activities occur improves both detection and attribution.
Understanding Wind Behavior
Wind determines whether a plume reaches a sensor. If no device is downwind at the moment of a leak, detection is unlikely. Qube’s planning tools analyze historical wind data to identify high-probability detection zones (Fig. 1). By aligning sensors with prevailing wind patterns and known sources, we increase the chances of capturing emissions when they happen.
Figure 1. Wind rose diagram illustrating dominant wind patterns and optimal sensor zones. These zones reflect areas with high historical probability of downwind plume transport from known sources.
Qube’s Deployment Methodology
At the core of Qube’s approach is a placement engine built on Mixed Integer Linear Programming (MILP)—a mathematical optimization technique used to solve problems with multiple constraints.
The model considers:
A predefined set of eligible sensor locations
The location and characteristics of known emission sources
Probabilities of wind arriving from each direction based on historical data
The result is a site-specific sensor layout that maximizes detection likelihood while balancing budget, terrain, and operational constraints.
In complex environments, offsite emissions can pose a risk of misattribution. To mitigate this, Qube’s tool allows users to specify mandatory upwind devices. These function to filter out background plumes and ensuring the emissions detected are coming from the site, not a neighboring facility.
Each sensor is part of a system. While any single device may or may not detect a given event, the network, taken as a whole, builds a time-resolved view of the emission. This cumulative picture allows for accurate detection and localization (Fig. 2).
Figure 2. Sequential snapshots of a deployed sensor array detecting evolving methane plumes. These time-stamped detections are used to reconstruct the emission’s rate, duration, and source location.
Qube’s Deployment Tool: Maximizing Impact by Balancing Coverage and Cost
Qube’s dashboard-based deployment tool helps operators strategically balance detection performance with budget constraints. It simplifies what can be a complex planning process into four actionable steps. Here’s how it works:
Step 1. Upload Your Site Layout
Begin by uploading a visual reference—typically an aerial image or site map—into the Qube dashboard. This provides the spatial foundation for mapping emissions and potential sensor placements.
Uploading Site Layout.
Step 2: Label Emission Sources
Next, identify and label known or likely emission points. These typically include flares, tanks, separators, wellheads, and compressor units. Accurately tagging these sources is critical for modeling emission behavior and optimizing sensor locations.
Label all potential emission sources
Step 3: Set Parameters
Input site-specific constraints and conditions:
Define your sensor budget (total number of deployable devices)
Specify wind sources based on local meteorological data
Outline eligible placement areas (e.g., restricted zones, infrastructure barriers)
These inputs help the optimizer model site realities and tailor the deployment plan accordingly.
This can be an iterative process in which the operator balances the number of devices deployed with the corresponding site coverage.
Step 4: Balance Coverage and Cost
Use the dashboard to visualize how sensor quantity and placement affect overall coverage. The interface allows iterative adjustments—enabling you to see trade-offs in real time. Throughout this process, Qube’s Customer Success Team is available to assist with scenario testing and decision support.
An exported summary of a device deployment plan.
This streamlined workflow transforms sensor placement from a manual task into a data-guided procedure—ensuring each device delivers maximum value.
Field Tested with Proven Customer Success
Check out these success stories where customers paired with Qube to get real results:
Want to Optimize Emission Monitoring on Your Site?
Contact Qube Technologies today to schedule a deployment planning demo and talk to one of our specialists.
Interested in more operational examples how Qube Technologies is driving emissions reduction and sustainability? Explore our other resources and case studies or reach out directly.