A Probabilistic Belief System to Track the Cleanliness of a Hole in Real-Time
Bibliographic record
Abstract
Abstract Good hole cleaning is essential to maintaining drilling efficiency and preventing non-productive events such as stuck pipe during well construction operations. A cuttings transport model may be used to determine the cleanliness of a hole, but its real-time rig site implementation is often made difficult by lack of necessary inputs to the model. There is a need for a simpler yet reliable approach to quantifying hole cleanliness using data readily available at any rig site. This 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 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 functionality is 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. The proposed method differentiates itself from the published methods on hole cleaning analysis in two main ways. First, it does not attempt to estimate the cuttings bed height or accumulation over time. Instead, it attempts to infer the probability that the hole cleaning operations are effective over time using features in data that suggest efficient or poor hole cleaning. Second, this method provides a clear indication of when hole cleaning actions are needed and why. The approach was validated with statistical methods using surface datasets from six wells involved in North American land operations. Through this validation it was determined that the method was highly effective in correctly characterizing hole conditions throughout the well operation. On the rig, the system was helpful not only in alerting the drillers whenever hole cleanliness deteriorated but also in providing the most likely causes of the deterioration. This provided the rig crew real-time guidance to make actionable decisions to avoid non-productive events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".