During the last SPE ATCE conference in Amsterdam, I attended the DSATS segment (Drilling Systems Automation Technical Section) of the SPE . In case you don’t know, the purpose of DSATS is to accelerate the development and implementation of systems automation in the well drilling industry by supporting initiatives which communicate the technology, recommend best practices, standardize nomenclature and help define the value of drilling systems automation.
This was not the first time I attended this meeting and I must say I have not seen much progress in recent years. It is, however, always interesting to hear different opinions on how to move forward. Basically, the solution is entangled between adding more technology to make the drilling rig fully automated and the ROI of such solutions. Does the cost really justify replacing humans on the drilling floor? Do the automated systems perform better than humans? To date, safety is the major beneficiary of any automation, which is a good reason to pursue automation further. Another issue is the emphasis on automating “drilling” operations, which represent around 30% of total time on the rig. As it was pointed out, the rig spends more time in “flat time” than in making a hole, so the automation needs to focus on those flat time operations as well.
As a future trend, I believe we will see simpler rigs capable of working almost independently. They could even have much lower specifications than the current high-tech rigs. But they would be cheaper and safer to run. We could therefore have more rigs operating simultaneously than we can today. This is similar to Google’s driverless car, where a very basic and simple car can transport people from A to B autonomously. The business model would need to change to cater for such rigs.
Another interesting result that came out of the discussions was that one essential problem with drilling automation is data – more specifically, data quality control. It is illuminating to observe that all the sophisticated equipment used to automate a rig will rely on data from sensors, as well as decisions made by human supervisors. So, it does not matter what degree of automation is implemented on a rig, there will still be a need to look into data management processes in order to achieve greater success.