Autonomous Decision-Making While Drilling

Eric Cayeux, Benoît Daireaux, Adrian Ambrus, Rodica Mihai and Liv Carlsen
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Eric Cayeux: Norwegian Research Centre, 4021 Stavanger, Norway
Benoît Daireaux: Norwegian Research Centre, 4021 Stavanger, Norway
Adrian Ambrus: Norwegian Research Centre, 4021 Stavanger, Norway
Rodica Mihai: Norwegian Research Centre, 4021 Stavanger, Norway
Liv Carlsen: Norwegian Research Centre, 4021 Stavanger, Norway

Energies, 2021, vol. 14, issue 4, 1-31

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.

Keywords: drilling automation; autonomous systems; Markov decision process; responsible artificial intelligence (AI); hybrid AI; fault detection; mitigation and recovery; safe operating envelope; safe mode management; batch procedure (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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