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Stage: Prototype

Innovators at Los Alamos National Lab are developing a software tool called EDGEip to intelligently process data on deployed sensors to enhance energy production efficiency. To be specific, EDGEip is being developed with the aim to smartly monitor earth and environmental processes via fast and automated data processing at sensor nodes. EDGEip replaces the difficult, messy, and manual practice of data collection with an automated methodology.

Currently environmental sensing is typically manual, expensive, time-consuming, and requires high-power. Every year there are around 30,000 oil & gas wells drilled across the US and there are over 500,000 wells in production. A loss of $500/hour is incurred if a well is not operational. Around 27,000,000 daily US readings are collected. Effective data analytics software is needed to condense the data into actionable information for real-time monitoring of oil & gas wells. The technology is agnostic to data streams; the key decisional features of a dataset can be extracted from gigabytes of data. This is achieved by combining low-cost and energy-efficient sensors, smart computing devices (such as Raspberry Pi), and LANL’s machine learning software to reduce large volumes of sensor data (order of gigabytes to terabytes) to actionable information (couple of kilobytes). For decision making, features are identified by the software automatically at the sensor nodes. Currently, the most popular solution to this problem is edge computing. Companies supplying such computing platforms on Internet of Things devices include Intel, Alacatel-Lucent, Accenture PLC, Cisco, Google, HP, Splunk, Foghorn, and IBM. Among these companies, Foghorn and Splunk are developing edge-computing solutions. Our EDGEip software is using new and recent advances in machine learning and signal processing to condense voluminous amounts of sensor data in real-time. The EDGEip software has undergone successful testing in a lab-setting using datasets related to oil/water cut measurements. The next round of testing should be on commercial oil & gas, seismic, and/or environmental datasets. We seek partners, with relevant data streams who are interested in exploring their datasets using our methods. This could be accomplished through collaborating on a government-funding opportunity, a CRADA, or a non-federal entity (NFE) funding vehicle.

Applications and Industries

In 2018, the US total market for Internet of Things monitoring of sensor data within the oil & gas, seismic processing, and environmental contaminant monitoring industries was $28B. Other environmental sensor data (e.g. climate related, engineering structure monitoring) represent another large segment of this market.


The benefits of the technology are that it provides a real-time monitoring tool that can develop actionable information. Analyzing sensor data in real-time can reduce downtime/maintenance/economic burden for multiple industries.

    Low cost
    Fast deployment into the field
    A portable and mobile solution