Large Oil Producer Leverages Advanced Analytics Platform
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.
C3 IoTC3 IoT provides a full-stack IoT development platform (PaaS) that enables the rapid design, development, and deployment of even the largest-scale big data / IoT applications that leverage telemetry, elastic Cloud Computing, analytics, and Machine Learning to apply the power of predictive analytics to any business value chain. C3 IoT also provides a family of turn-key SaaS IoT applications including Predictive Maintenance, fraud detection, sensor network health, supply chain optimization, investment planning, and customer engagement. Customers can use pre-built C3 IoT applications, adapt those applications using the platform’s toolset, or build custom applications using C3 IoT’s Platform as a Service.Year founded: 2009
- CONNECTIVITY PROTOCOLS
- USE CASES
Predictive MaintenancePredictive maintenance is a technique that uses condition-monitoring sensors and Machine Learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive Maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on Predictive Maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using Predictive Maintenance and Predictive Maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.
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.
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
- DATA COLLECTED
Asset Status Tracking, Fault Detection, Operation Performance, Overall Equipment Effectiveness, Per-Unit Maintenance Costs
- SOLUTION TYPE
- SOLUTION MATURITY
Emerging (technology has been on the market for > 2 years)
- OPERATIONAL IMPACT
Impact #1 [Efficiency Improvement - Maintenance]
Cloud solutions enable prediction of emerging equipment maintenance requirements weeks before equipment failures.
Impact #2 [Efficiency Improvement - Maintenance]
Real-time status reports enable maintenance personnel to remotely diagnose the status of a device, often before a failure occurs.
- QUANTITATIVE BENEFIT
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.