The efficient utilization of automation systems necessitates a clear understanding of the interaction of the human operator, the automation system and any automated routines being run. If automated routines perform actions not desirable to the human operator, time is lost as the routine is interrupted and human control re-engaged. In addition, automatic handoff back to the human operator, both due to human intervention and due to exist conditions or anomalies must also be managed. Activity data from rigs across North America is analyzed to understand automation process utilization and interrupt timing. Realtime and historic data is tagged, either automatically, semi-automatically using machine learning, or manually, to create a minute-by-minute timeline of rig operations. Operations are then classified both by operation – steering, reaming, making hole, etc. – and well plan to understand how operational demands change automation system utilization. This results in a new set of metrics which can be used to precisely quantify the performance metrics of both the human and automated drilling systems. Performance of the automation system is found to be a strong function of hole deviation with the system outperforming during simple operations and in the vertical hole, but with reduced performance while in the curve and horizontal, due to high interruption of certain tasks. It is found that standard performance metrics, such as slip to slip or weight to weight are affected by standard practices and if these are used to grade system performance, these practices must be account for. This paper presents a detailed investigation of the interaction of the driller with an automated drilling automation system and lays out the utilization of the automation system as a function of rig operations and well path. It is specially noted that standard performance metrics must consider standard practices which may differ between operations.

You can access this article if you purchase or spend a download.