OspreyData’s unique human augmented machine-learning cycle brings deep and predictive insights that can dramatically improve your field’s productivity. Our cloud-based system identifies problems as they start, giving you the power to take action. The action you take makes our system smarter over time.
OspreyData tells you which assets are at a high risk of failure at the forefront of the app. Our physics-based predictive models create Key Indicators which suggest problems may occur, while the confidence of these predictions updates as new data is received. Leverage this data to address potential problems while having long lead times.
Our Asset View offers you advanced tools to examine deviations in changing sensor feeds. Review your asset’s key sensors, overlay events, and view dynacards in context. Use this view to determine what action is best to take, or create a workflow to ensure action happens automatically.
OspreyData’s predictive Events are designed to surface as early as possible when problem events are likely, giving you 1-2 weeks of lead time to address problems before they become critical. Use these events to intervene prior to failure and improve your ROI.
Use OspreyData’s automated Workflow Engine to link predictive Events with your company’s decision processes. Set your own operational rules and assignments to schedule assessments, repairs and maintenance to keep wells optimized.
The data Explorer offers a multi-dimensional investigation of the major factors that affect your wells over time. Look at your data and pivot in any direction to explore how Production, Events, Data Fidelity and Operations have worked to affect your overall uptime.
With our Human-Augmented Machine Learning model, interactions from your SMEs will help improve diagnosis over the long-term. As experts review and add input on what changing well conditions mean, the system will become smarter while embedding your company’s high-level knowledge.
Questions? View our FAQ
From functionality to implementation, view our most frequently asked questions here.