Using Real-Time Analytics, Downhole Sensor Data, and Hazardous Area Tablets & Sensors to Predict Failures and Confront “Time to Failure” in the FieldBackgroundCurrent market conditions in the Oil & Gas industry require greater efficiencies to remain competitive, and uptime is one of the most important KPIs for an organization’s sustained viability. When unplanned repairs or equipment replacement occurs, costing the organization two to three times more than planned maintenance and causing unexpected downtime, the inevitable question is, “what can we learn from this so that we can better anticipate in the future?”In the last decade, due to the hazardous (potentially combustible) nature of Oil & Gas environments that restrict the use of most computing devices, operational data was largely being reported on paper and clipboard, and none of this data was being used for maximum operational benefit. Now, with the integration of intrinsically safe sensors and mobile devices like tablets, organizations are beginning to connect “everything,” and use data from all component parts to form a better overall picture of an entire operation. With this insight, companies can use predictive analytics and artificial intelligence (AI) to better predict, plan and respond to anomalies, thus reducing cost and risk and improving overall safety and productivity.The Oman Incident: On December 9, 2015, at 4:56pm local time, a Progressive Cavity Pump (PCP) driven oil well in Oman known as “AV0902” suffered a break in the sucker rod string approximately 4700 feet below the surface. The unexpected shutdown required a completion workover service and replacement parts amounting to approximately $75,000. Industry average turn around for artificial lift repair is a week which also amounted to another 2,100 barrels of lost production. (At $40 per barrel = $84,000)
Earlier in the year, AV0902 had been outfitted with an innovative permanent down-hole gauge system from GeoPSI. The gauges had 12 different sensors measuring intake and discharge temperature and pressure, downhole speed, rotor position, twist, downhole vibration, and more.The customer wanted to know:
The chart of Simularity’s Predictive Failure Signature for the sensor data preceding the failure. After a period of normal operations, two significant alerts appear 45 and 33 days in advance of the failure. Had the customer known beforehand that this failure was looming (Alert 2), things would have been different:
About Aegex: Aegex is a technology engineering and design company that provides intrinsically safe Industrial Internet of Things (IIoT) and mobile solutions for hazardous industries. Our globally certified intrinsically safe Windows 10 tablet, sensors and partner monitoring systems, form an IoT platform that manages big data to improve efficiency, safety and productivity in hazardous industrial environments in oil & gas, chemical, pharmaceutical, utilities, public safety, defense and other industries with potentially explosive atmospheres.