Decarbonization at scale: Operationalizing real-time methane remediation

Methane is invisible and notoriously hard to track across offshore rigs, mountainous terrain, and dense vegetation, and the real hurdle isn't detection, it's operationalization. Manual, fragmented workflows created data silos that kept slow detection from becoming fast remediation, increasing regulatory exposure and stalling decarbonization goals. The client needed a high-scale, role-based platform to process continuous IoT data streams, orchestrate workflows, and intelligently prioritize incidents, on top of a system not built for that volume.

We co-created an emissions monitoring platform for both ground sensors and aerial drones to turn raw signals and images into actionable decisions. This involved continuous ingestion of IoT and drone data, and converting readings into PPM (Parts Per Million) and geospatial views. By replacing periodic checks with always-on monitoring, the client can detect methane and other emissions earlier, prioritize what needs attention, and respond faster.

• Drone and sensor to cloud pipeline: Drones capture images and videos of methane leaks, while ground sensors across facilities continuously collect methane and other harmful emissions every four seconds. All of this data is ingested into the platform and analyzed in real time to surface alerts and insights.

• Composable services: A distributed services layer ensured ingestion, analysis, and reporting ran independently, keeping the system reliable even under heavy load.

• AI-powered detection: Deep learning models scanned drone feeds, flagging anomalies in near real time and reducing detection lag from hours to minutes.

• 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 with right details, whether it was live alerts or regulatory reports.

• High-frequency time-series analytics: Machine learning models analyze sensor readings captured every four seconds for time-series prediction and anomaly detection, turning raw signals into early warnings instead of static reports. 

• Geospatial rendering: Integration with a GIS layer (Cesium) shows anomalies directly on site maps, helping teams see exactly where abnormal readings originate in the field. 

• Fault-tolerant services: With a microservices architecture, failures in one module never brought down the system critical functions like alerting stayed live

• Event-driven data flow: A messaging layer separates fast sensor data collection from later processing and dashboards, so even big spikes in sensor data don’t slow down alerts or screens.

Zemoso helped the client move beyond methane detection to operational decarbonization. By unifying aerial and ground monitoring in a single decision-grade system, teams can now detect leaks earlier, prioritize response, execute remediation, and generate audit-ready reports at enterprise scale.