Advances in technology have allowed the oil and gas industry to collect massive amounts of data. The challenge is to derive value from these datasets. To achieve this aim you need oilfield production software that not only handles the volume of data but finds the insights in it.
Big Data Trends
The increasing use of data recording sensors in the oil and gas industry has led to the collection and storage of massive datasets. Data is now being collected in virtually all areas of the industry, from exploration to drilling and production. These data sets can be analyzed using statistical and machine learning techniques, and used for production forecasting, well optimization, and many other applications. One of the most important applications is asset reliability and improving it via preventive maintenance.
Preventive Maintenance Strategies
Preventive maintenance strives to detect issues such as equipment failure before they occur. Data recording sensors placed on various parts or subsystems automatically collect data. Simultaneously, analytic software monitors the collected data for change using machine learning techniques. If a change threshold is exceeded in any part, then the operators are alerted and informed about the location and nature of the problem. This early detection allows the problem to be corrected before it becomes a major issue, thereby reducing downtime. Rather than waiting for a piece of equipment to break, taking it out of service and putting it in a waiting line for repair, preventive maintenance strategies give advance warning and allow you to make any necessary adjustments or repairs to the equipment before the break occurs. This approach also eliminates the domino effect of one broken part causing problems with interconnected parts or subsystems in an actual failure.
The volume of data collected in the oil and gas industry will continue to increase with advances in technology. The potential value in the data will also increase if you take advantage of the power of analytics to harvest insights from it.
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