C3 IoT Case Studies Large Oil Producer Leverages Advanced Analytics Platform
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Large Oil Producer Leverages Advanced Analytics Platform

C3 IoT
Large Oil Producer Leverages Advanced Analytics Platform - C3 IoT Industrial IoT Case Study
Analytics & Modeling - Machine Learning
Analytics & Modeling - Predictive Analytics
Infrastructure as a Service (IaaS)
Oil & Gas
Process Manufacturing
Predictive Maintenance
Root Cause Analysis & Diagnosis

Approximately 17,000 wells in the customer's portfolio have beam pump artificial lift technology. While beam pump technology is relatively inexpensive compared to other artificial lift technology, beam pumps fail frequently, at rates ranging from 66% to 95% per year. Unexpected failures result in weeks of lost production, emergency maintenance expenses, and costly equipment replacements.

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One of the largest oil and gas producers in the U.S. with an upstream portfolio consists of more than 22,000 wells distributed across 10 countries in North America, South America, and the Middle East.
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As part of the C3 IoT analytic software suite, C3 Predictive Maintenance employs machine learning-based algorithms to enhance failure prediction and diagnostic capabilities. The application augments traditional systems by continuously monitoring all instrument signals, tracking complex failure modes, and detecting operating anomalies associated with impending equipment failures for a large range of assets. In this deployment, C3 IoT integrated daily sensor readings from in-field equipment and unstructured data from maintenance work orders. This comprehensive data integration and analysis gives service teams a comprehensive weeks-ahead view of emerging equipment maintenance requirements, with detailed supporting data and diagnostic tools to support maintenance decision making. Hardware Components - Daily sensor

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Asset Status Tracking, Fault Detection, Operation Performance, Overall Equipment Effectiveness, Per-Unit Maintenance Costs
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[Efficiency Improvement - Maintenance]
Cloud solutions enable prediction of emerging equipment maintenance requirements weeks before equipment failures.
[Efficiency Improvement - Maintenance]
Real-time status reports enable maintenance personnel to remotely diagnose the status of a device, often before a failure occurs.

The C3 Predictive Maintenance application accurately predicted 45% of equipment failures that were to occur within 6 months.

The C3 Predictive Maintenance application analyzed over 1,000 wells.

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