In the summer of 2014, crude oil prices peaked at $108.93 per barrel right before they began to plunge, finally bottoming out at $26.91 per barrel in February 2016. When prices are at an all-time high, many oil and gas (O&G) companies concentrate on drilling as quickly as possible, in as many locations as possible, in order to maximize their profit. But when oil prices drop, their emphasis shifts to optimization, and they start finding ways to drive profit margin by gaining operational efficiencies, improving the bottom line, and adopting new ways of doing business.
In this volatile economic climate, many O&G companies are turning to predictive analytics to help them optimize their operations, increase productivity, and accelerate energy exploration and production. At the same time, O&G companies are collecting an increasing amount of data generated by a variety of remote Internet of Things (IoT) endpoints, including machines, applications, and devices. They need to bring their analytic abilities out to the edge where the data is created and collected in order to glean real-time, predictive insight that can be used to drive better business outcomes.
According to IDC, by 2019 at least 40% of data created from IoT endpoints will be stored, processed, analyzed, and acted upon close to, or at the edge of, the network. To help O&G companies better leverage the capabilities offered by edge computing, Hewlett Packard Enterprise (HPE) developed a solution framework that delivers early, operational, and deep analytics for O&G companies so they can augment insights delivered by their pre-existing applications.
HPE Edgeline IoT Systems provide an edge-to-core platform that allows customers to ingest and process data at the same location where it’s created, providing real-time insight into the totality of their operations – from downhole all the way up to the surface. When combined with the HPE Vertica Analytics Platform, organizations can capitalize on historical and predictive analytic insights generated from in-database machine learning algorithms to achieve real-time control and decision-making.
Many IoT systems aggregate all data – even information that will ultimately indicate nothing is wrong – back to the data center for processing. HPE’s solutions are built to help customers gain the insight they need right at the location where they most need it, without the risks and latencies associated with transmitting large amounts of data across the network. This helps to reduce network dependency, lower network costs, and decrease the need for local IT staff.
In addition, HPE’s distance deep learning framework splits the conventional architecture and performs data inference at the edge but training at the core, which allows IoT devices to continuously learn from one another and improve over time.
By harnessing the power of real-time insights at the edge, O&G companies can realize a number of key advantages:
- Predictive maintenance: Edge computing enables real-time equipment monitoring, anomaly detection, and failure prediction to help organizations proactively maintain their equipment, reduce downtime, and increase scheduled maintenance.
- Safety improvements: Dangerous equipment malfunctions or even explosions can occur before operational data has even reached the data center for processing. Edge computing provides a real-time view into operations, so companies can pinpoint signs that an incident is about to occur, and take steps to prevent it.
- Better production: Sensors located within active drilling sites can gather data such as pressure and temperature from wells on a more frequent basis, which can be analyzed right at the edge to optimize energy production.
With new edge-to-core platforms from HPE, O&G companies can accelerate IoT insights from the intelligent edge in order to gain real-time, predictive, and deep insight that they can use to improve decision-making and business outcomes. For more information on how many other industries are capitalizing on IoT innovations, please follow me on Twitter at @Bill_Mannel. And for general news, insight, and stories about how HPC and Big Data can transform your business, check out HPE’s HPC Twitter page at @HPE_HPC.