Blogs

Optimizing the digital oilfield with predictive analytics

WW Analytics Solutions Marketing Executive - Predictive Maintenance & Quality, IBM

At the recent Instrumentation, Systems and Automation (ISA) conference in Calgary, Canada, two salient themes were the digital oilfield and the corresponding management of volumes of data generated through increasingly instrumented operational technologies. A comment often heard throughout the conference was, “we have plenty of data, but we don’t know what to do with it.” 

While that sentiment may seem like a problem, it’s a good problem to have. Operational data, when properly analyzed, can lead to significant improvements in overall productivity. Operational insight can provide a more accurate, detailed understanding of asset performance and process productivity, and enable timely alerts in the event of any deviations from normal operations. 

One of the more promising uses of asset performance data is in the area of predictive analytics. Through careful analysis of historical data obtained from sources such as maintenance logs, predictive models can be developed for specific assets. These models can be used to assess the health of an asset, estimate and extend asset life, proactively deploy maintenance resources and optimize spare-parts inventory. 

For example, consider one critical piece of equipment: the electric submersible pump (ESP). Given that a producer may have thousands of these devices in operation, each with the capability to generate volumes of real-time data, the ability to analyze ESP data can help improve productivity and efficiency of the extraction process. A producer who is simply monitoring ESP performance but not applying any analytical capabilities is forgoing an opportunity to take a major step forward in becoming far more efficient in the management of these critical assets and well production. By employing predictive analytics, producers who currently characterize their maintenance strategy as either reactive or preventive can move toward a predictive maintenance strategy that has demonstrated a highly cost-effective and efficient asset maintenance approach.

In the digital oilfield, an increasing amount of instrumented equipment and the volumes of data generated present challenges and opportunities. Challenges include capturing and analyzing operational data, and opportunities include the capability to apply sophisticated analytics to this data to further improve extraction and production processes. 

Industry-specific solutions from IBM are addressing these needs in the oil and gas industry to help improve the efficiency of production assets. Learn more in the video announcement on May 28, 2015.