Paper SPE-204125-MS, presented at the SPE/IADC International Drilling Conference and Exhibition, Virtual, March 2021.

The paper proposes a method that relies on the detection of events over a long time horizon and the use of key parameters relating to such events to quantify hole cleanliness. These events are then related through duration and frequency to probabilistic features in a Bayesian network, to infer the probability that the hole cleaning process has been efficient or poor. These events are also weighted by their age to ensure that current beliefs are not strongly influenced by those that are far in the past. The method was deployed on a drilling advisory system and is currently used on rigs in North American land operations. The events and features found to be most relevant to quantifying hole cleanliness were the circulation rates during drilling, tight spots when moving the drillstring, bit hydraulics, and prolonged periods of inactivity. Proactive hole cleaning actions such as working of the pipe, off bottom circulation and pipe rotation were also considered. The Bayesian network model used by the proposed method was able to be run with low computational overhead (micro-seconds on a standard edge device) compared to a traditional cuttings transport model. This was enabled by an event logging procedure that keeps track of hole-cleaning events over time and consolidates several hours (days) of drilling information into relevant hole-cleaning features that can be processed quickly.