Autonomous Decision-Making While Drilling

Abstract

The drilling process is complex because unexpected situations may occur at any time. Furthermore, the drilling system is extremely long and slender, therefore prone to vibrations and often being dominated by long transient periods. Adding the fact that measurements are not well distributed along the drilling system, with the majority of real-time measurements only available at the top side and having only access to very sparse data from downhole, the drilling process is poorly observed therefore making it difficult to use standard control methods. Therefore, to achieve completely autonomous drilling operations, it is necessary to utilize a method that is capable of estimating the internal state of the drilling system from parsimonious information while being able to make decisions that will keep the operation safe but effective. A solution enabling autonomous decision-making while drilling has been developed. It relies on an optimization of the time to reach the section total depth (TD). The estimated time to reach the section TD is decomposed into the effective time spent in conducting the drilling operation and the likely time lost to solve unexpected drilling events. This optimization problem is solved by using a Markov decision process method. Several example scenarios have been run in a virtual rig environment to test the validity of the concept. It is found that the system is capable to adapt itself to various drilling conditions, as for example being aggressive when the operation runs smoothly and the estimated uncertainty of the internal states is low, but also more cautious when the downhole drilling conditions deteriorate or when observations tend to indicate more erratic behavior, which is often observed prior to a drilling event.

Application of Artificial Intelligence in Drilling and Completion

“…Therefore, drilling optimization involves multiple objectives and multiple subsystems, requiring a model that integrates coupled subsystems. The overall optimization and intelligent decision-making of the drilling process is an important scenario for the application of artificial intelligence in the field of drilling and completion [28].…”

An Outlook of Drilling Technologies and Innovations: Present Status and Future Trends

“…According to Macpherson et al [13], the rotary steerable systems are ranked L8-L10, meaning that the need for human intervention is minimal, and only during monitoring/generating of the process. To date, a fully autonomous system, as per the definition provided by Endsley et al [12], has not yet been realized, however, a few attempts have been already published [14]. Although not comparable, it is worth mentioning that the mining industry has reported fully autonomous blast hole drilling systems since 2018.…”