Driverless cars – just a few years ago it was science fiction, yet today we are increasingly comfortable with stepping foot into a driverless cab thanks to the convergence of several important technologies. In the same way, oil & gas drilling is in the midst of an important transition from manual data analysis and drillstring control to real-time drilling analytics and automation. Big data, cloud computing, and real-time drilling analytics are the key technologies that have converged to make it all happen. Are you ready to put your rig on autopilot?
Evolution of Big Drilling Data
Punching a hole in the ground generates a torrent of data, including BHA data, wellbore position, directional information, and logs. Over time and throughout the development of a field, it all adds up into a massive historical data set that holds insight into future drilling performance. In addition to volume, big data is also about the velocity of data streaming in from the field. Thanks to advances in measurement while drilling and up-to-the-second data transmission from the rig, drillers now have unprecedented access to real-time rig information.
Rise of Cloud & Mobile Computing
Analyzing torque and drag data and detecting potential drilling problems often took a large block of time on a mainframe in the 80’s. Since then, personal computers have evolved to perform the extensive calculations required by drilling engineers but still require significant data management and run time. Cloud, or on-demand, computing helps in two ways: first by providing centralized management of drilling data, and secondly by allowing complex calculations to be performed in real-time by provisioning processing power as needed, on demand.
The cloud has also evolved alongside mobile computing. Software built on the cloud can be accessed anywhere, anytime from any device, including smart phones, tablets and laptops. As a result, drilling teams can work together, collaborate and make decisions in powerful new ways.
Real-Time Drilling Analytics
Understanding drilling efficiency, torque and drag, and nonproductive time is a complex mix of data, model predictions and sophisticated visualization. A broomstick plot, for instance, has historically relied on extensive data preparation and manual processes, delaying real-time analysis and insight into current hold conditions at the rig site.
Big data and analytics have converged in the cloud to enable a clear view of current drilling operations. Cloud-based drilling solutions are now able to tap directly into the live data streamed from the field. Combined with automated model prediction, rig detection, machine learning, and rich visualization, drilling operators gain powerful real-time analysis capabilities. And with advances like monitoring by exception, the rig is increasingly being put on autopilot, enabling engineers and rig to work more efficiently.
What’s more, the cloud drives new levels of collaboration among drilling teams, providing a common decision space and increasing transparency across drilling projects. Teams, who are often geographically dispersed, can monitor dashboards, data-driven broomstick plots, and other analytics in real-time while receiving alerts and recommendations that leverage a holistic model of historical project data and current drilling. All from the coffee shop, office, or on the go using the devices staff already have, including smart phones.
Corva.ai is a cloud-based big data and drilling analytics platform that improves ROP and reduces NPT. With Corva, drilling teams can easily monitor current operations using 38 stunning visualization apps and respond to drilling problems before they happen. Use Corva’s T&D analytics, such as automated broomstick plots, to proactively prevent hole cleaning and drag problems in real-time. Plus, monitor and improve drilling efficiency, mud motor efficiency, directional guidance, and circulation