Enabling Real-time Estimation of Borehole Parameters in Deep Drilling
Bibliographic record
Abstract
In this work, the evolution of friction factors across the depth of a horizontal wellbore, downhole rpm and downhole torque are obtained using a distributed drillstring model. The model has been field validated for the off-bottom dynamics and is used to estimate along-drillstring friction factors. This model was later extended to include a bit-rock interaction (BRI) law to obtain the downhole rpm and downhole torque while drilling and has been validated against the field data. The advantage of the model used in this work is that it employs an adaptive soft sensor, robust to capture the disturbances occurring at the downhole. Only the top-drive measurements are used to estimate friction factors (static and kinetic friction coefficients) and the downhole parameters using the soft sensor. Once the bit engages, the BRI takes precedence, and the model stops estimating the friction factors. The model is used to generate estimates for friction factors, the downhole rpm and BRI for a well located in North America. It was observed that in both the cases, the estimates match closely with that of the data considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".