In the global oil and gas sector, failure of pumps, compressors, heat exchangers and other critical equipment systems can have significant consequences for field operations, safety and revenue. Today, most equipment monitoring systems raise static alarms based on high/low thresholds for individual sensor values. These sensors are often inadequate for capturing the wide range of normal behavior of complex oil field equipment systems and unable to separate true potential failures from false positives. Given the substantial impact of pump and other equipment failure, there is a need to complement existing systems using advanced analytics. The convergence of several long-term technology creates new opportunities for failure prediction in critical equipment:
- Data storage and compute capacity are increasingly inexpensive and effectively unlimited.
- Sensors continue to decline in cost and physical footprint.
- Sensor, device and asset-level connectivity continue to improve in quality and cost.
- Machine learning tools and techniques are increasingly accessible and easy to use, even with limited training and resources.