A scalable Digital Fleet Management system can be leveraged by most organizations with high-volume high-value assets. In such scenarios, predictive analytics for tool health becomes central, as it enables decision-making in terms of planning, maintenance, end-of-life replacement, tool selection, etc. End-to-end solutions, spanning all the way from gathering live tool data to the visual representation of tool health, are certainly of major interest.
Long-term fleet management can be accomplished through a consistent evaluation of the fleet performance profile. Predictive analysis can anticipate maintenance needs and resultant downtimes, and in turn it helps improve scheduling of procurement and distribution of the fleet.
This paper focuses on managing a fleet of thousands of downhole tools based on tool health condition and other variables – a very common use case in Oil & Gas Services. An end-to-end automated scalable cloud framework is described in detail. This framework integrates failure prediction models for each single asset in the fleet of tools. Based on historical tool data, the models generate tool risk indices (one index per asset) which correlate to the probability of tool failure during near-future field jobs. These risk indices can be used for optimal asset-to-job mapping. They also help in de-risking field operations by identifying tools for overhaul or retirement. The proposed method integrates the tasks of: fetching data from 200,000+ tools, performing feature engineering, modeling via Machine Learning (ML) , and visualizing into a cloud pipeline. Framework scalability becomes a key requirement as fleet size increases or decreases over time to match market demands. The framework also allows for the addition of new ML models to the platform by citizen data scientists, who are not cloud experts. Finally, it is shown who this framework provides systematic steps for sustenance of such large cloud platform.
How to Cite
Fleet Health Management; Cloud; Machine Learning; Oil and Gas
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