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Developing a smarter way to drill for oil with analytics

Developing a smarter way to drill for oil with analytics

In recent years, the energy market has been especially tough for oil companies. Oil prices have fluctuated wildly, and new market entrants are encroaching on the market share of established producers.

All of this has put oil companies under increased pressure to streamline their operations and make sure that rigs run as effectively as possible.

At TOTAL, we understand that running an oil well profitably and efficiently is mechanically and technically complex. That’s why we are always looking for new ways to innovate and become more efficient.There are cases in which a significant amount of drilling time ends up being classed as non-productive time (NPT). That’s the time taken to return to an original drilling plan after a deviation necessitated by failure of equipment or any other event that causes drilling operations to stop.One of the common causes of NPT is a phenomenon that we call “stuck pipe.” It’s quite literally when the drill pipe or string can no longer be moved up or down, or it can’t be rotated within the wellbore to continue normal drilling operations.

We had a new idea. We decided to use NCPL Analytics technology to build a near-real-time picture of the condition of a well. With this insight, we could provide engineers with a tool to spot conditions that could lead to stuck pipe and other events that typically cause NPT.

When we told our engineers, who have been working on this same problem for their entire careers, that we had found a potential solution, they were skeptical. Understandably, they wanted proof.

So we put our money where our mouth is and started a project to develop a predictive drilling analytics solution. The idea was to combine our engineers’ expertise with physics-based models of wells to build statistical models to help us predict when stuck pipe might occur.

Using cutting-edge technology from  Cloud and the  Studio platform helped us get our idea off the ground fast. Choosing a cloud solution meant we avoided spending lots of time and money sizing and deploying infrastructure. It enables us to easily scale up performance and capacity as our models become more sophisticated and data volumes grow.