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Oil & Gas

Aerial Monitoring with Drones for

Methane Emissions Detection

Services Rendered

Product Design

Product Engineering

Product Architecture

Tech stack

React, Redux, Material UI, Spring Boot Java, GraphQL, Apollo Server, Drools Rule Engine, RabbitMQ, PostgreSQL, MongoDB, Redis, Amazon S3, Python, PyTorch, PyMongo, Cucumber, Selenium, Postman, Jenkins, GitHub, Kubernetes, AWS EKS, Docker, AWS CloudFront CDN, AWS SNS, AWS VPC, AWS Network Load Balancer, Cesium GIS API

Introduction

A Fortune 500 oilfield services provider partnered with Zemoso Labs to tackle a critical challenge: how to track methane emissions quickly and at scale during oil and gas production. Traditional inspections were slow, manual, and couldn’t deliver real-time visibility. With net-zero targets looming, delays were costly. Together, we built an aerial monitoring solution powered by drones and a cloud-native analytics platform. Today, the system detects leaks in real time, pinpoints their location, and generates alerts and reports that operators can act on immediately.

iNDUSTRY CHALLENGE

Methane is over 80 times more potent than CO₂ in the short term, and oil and gas production is responsible for about 40% of global emissions. The industry has long relied on ground patrols, handheld devices, and periodic surveys—methods that are labor-intensive and prone to delays. Every extra hour before a leak is detected increases safety risks, regulatory exposure, and reputational damage. Companies are under pressure to move fast, and aerial monitoring with drones has become a critical part of that shift.

Zemoso Labs Partnership Challenge

The client already had a system in place for monitoring oil leaks, but it was clear the architecture wasn’t suited for methane detection. What they needed was a complete pivot—turning a legacy inspection tool into a system capable of handling drone-based data, computer vision, and real-time alerts. 

The challenge went beyond technology: we had to design workflows for multiple roles, from super admins setting up asset hierarchies to emissions engineers reviewing alerts and sending reports to stakeholders. Getting all of these pieces to work together at scale meant rebuilding both the backend and the user experience.

Impact created

The platform delivers measurable emission reductions and accelerates progress toward 2030 and 2050 net-zero goals by enabling real-time computer vision analysis of drone footage for faster leak detection.

What are our clients saying?

Our clients love what we do:

How did we do this?

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We co-created a platform that brings together drone-based sensing, deep learning–powered computer vision, and geospatial rendering into a single, resilient microservices platform. By replacing manual inspections with this real-time system, the client now has the scale and accuracy to meet emissions targets while staying ahead of regulatory pressure.

At its core, the platform combines edge detection, cloud-native processing, and geospatial rendering into one seamless workflow.

  • Drone-to-Cloud Pipeline: Drones equipped with methane sensors captured imagery and streamed it to the cloud, where the system ingested and organized terabytes of visual and sensor data.
  • AI-Powered Detection: Deep learning models scanned drone feeds, flagging anomalies in near real time and reducing detection lag from hours to minutes.
  • Composable Services: A distributed services layer ensured ingestion, analysis, and reporting ran independently, keeping the system reliable even under heavy load.
  • Intelligent Data Fabric: The architecture balanced structured (asset hierarchies, alerts) and unstructured (drone imagery, video) data, enabling smooth analysis without bottlenecks.
  • Geospatial Precision: A digital map layer turned raw detections into actionable insights—pinpointing leak locations directly on field assets.
  • Operator Experience: From field engineers to compliance managers, role-based dashboards delivered the right data at the right fidelity—whether it was live alerts or regulatory reports.

Complex Engineering Highlights 

Behind the scenes, several engineering breakthroughs made this possible:

  • Computer Vision at Scale: Deep learning models processed vast amounts of unstructured drone data in real time, replacing traditional inspections.
  • Hybrid Data Orchestration: Coordinating relational, unstructured, and cached data stores ensured smooth performance across workloads.
  • Geospatial Rendering: Cesium GIS delivered near-digital twin accuracy, helping teams pinpoint leaks down to the asset level.
  • Fault-Tolerant Services: With a microservices architecture, failures in one module never brought down the system—critical functions like alerting stayed live.
  • Edge-to-Cloud Speed: Near real-time processing meant methane releases were identified within minutes, reducing emissions and regulatory risk.

Conclusion

Zemoso Labs worked with the client to reimagine methane monitoring, turning drone imagery into fast, actionable data. What once required slow manual surveys is now a live, automated system that helps one of the world’s largest energy providers cut emissions, improve safety, and take concrete steps toward its net-zero future.

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