Oil & Gas

Real-time Intelligence for Operational Excellence

Oil and gas extraction are high-stakes technology-driven operations that depend on real-time onsite intelligence to provide proactive monitoring and protection against equipment failure and environmental damage. Because these operations are very remote and lack reliable high-speed access to centralized data centers, FogHorn’s Edge AI platform provides onsite delivery of advanced analytics and machine learning, and enables real-time responses required to ensure maximum production and safety.



Improve automation, enhance practitioner collaboration, reduce manual data entry and errors.

Condition-based Monitoring

Lower maintenance costs, improve asset performance and longevity, and maximize yield.

Prescriptive Maintenance

Reduce repair costs, minimize downtime, maximize output, and improve remediation efficiency.

Operational Intelligence

Optimize systems for maximum KPI performance, quickly adjust to changing conditions.

Asset Performance Optimization

Maximize high-value asset output, balance reliability, performance, and cost.

Flare Stack Monitoring

Ensure regulatory and environmental compliance, reduce maintenance costs, leverage deep learning models.

Use Cases

Monitoring pressure and other conditions related to a machine’s health to prevent pump damage and costly downtime

Centrifugal pumps are widely used across all industrial and commercial sectors such as Power, Water, Oil and Gas, Healthcare, Manufacturing, Utilities, and Transportation.

Cavitation is a condition that can occur in centrifugal pumps when there is a sudden reduction in fluid pressure. Pressure reduction lowers the boiling point of liquids, resulting in the production of vapor bubbles if boiling occurs. This is more likely to happen at the inlet of the pump where pressure is typically lowest. As the vapor bubbles move towards the outlet of the pump where pressure is higher, they rapidly collapse (return to a liquid state) resulting in shock waves that can damage pump components.

FogHorn’s edge intelligence can ingest streaming data from pump sensors and apply real-time analytics to identify any significant changes in pressure and alert operations personnel before damage occurs. It can also send a signal to the main system to automatically move the flow of the fluid to a different pump to prevent damage and reduce maintenance and downtime costs associated with pump cavitation.

Reduce operating costs and improve safety by deploying real-time proactive pipeline optimization systems

There are 2.5 million miles of pipelines that distribute oil and gas across the United States. To increase safety and improve efficiency, the pipeline industry has invested heavily in standard sensors to measure flow, pressure, and compressor conditions. Modern acoustic and ultrasonic sensors are also being added to monitor other factors such as corrosion and soil movement and their impact to the pipeline. The combination of all these sensors generates large amounts of data. An average 100-mile long pipeline segment alone can generate 50 gigabytes of data which amounts to many terabytes across all pipelines worldwide.

The pipeline industry is continually seeking new ways to use sensor data to become more proactive at reducing risk, lowering maintenance costs and increasing overall pipeline uptime. FogHorn’s edge intelligence solution can play a major role in this by monitoring and performing analytics on streaming data in real-time and by responding automatically to issues detected by the sensors. For example, it can immediately shut down a valve and send an alert to a mobile device to avoid a major disruption or damage to a pipeline.

The real-time and proactive nature of FogHorn’s edge solution cannot be duplicated by a cloud-only solution. Network latency and reliability are two major inhibitors and transporting such a large amount of data to the cloud is also cost prohibitive. With FogHorn’s edge intelligence, pipeline operators can lower costs and risk, while increasing data throughput and customer satisfaction and confidence.

Anticipating machines failures before they actually happen through predictive analytics

Predictive maintenance prevents unnecessary repairs, maximizes effective asset lifetime, and significantly reduces major failures and downtime. This results in cost savings with an increased return on investment and customer satisfaction. Providing predictive maintenance at the edge and sending only relevant data to the cloud is a lower cost alternative to cloud-only based solutions. It provides real-time actionable insight with extremely low latency and substantially lowers data transfer and storage costs.

The Electric Submersible Pump (ESP) is a long in-ground piece of equipment at the center of an oil well extracting oil from the bottom of the well and pumping it to the surface. Failure of an ESP will stop the entire operation which can take time and result in costly repairs and loss of revenue. FogHorn’s edge predictive maintenance solution can monitor the operational data gathered from the ESP and apply advanced analytics in real-time to predict failures. If a potential failure is detected, the system can automatically stop the pump to prevent damage as well as alert operations to repair or replace the ESP based on current machine health and maintenance models developed by the operators of the ESP. FogHorn’s edge solution can reduce the cost of data transfer to the cloud by preprocessing real-time data at the edge and sending only relevant data to the cloud. It also provides real-time availability without requiring an uninterrupted network connection that cloud-based solutions depend on.