Real-Time Downhole Weight on Bit (DWOB) Automation in Directional Drilling
Bibliographic record
Abstract
This paper introduces the functionality of a new type of Autodriller software system, which can acquire downhole weight on bit (DWOB) based on surface rig measurement. Field tests are performed, including DWOB measured by downhole measuring tools and the hookload below the top drive using a TTS (Torque and Tension Sub). Three sets of drilling data from three horizontal wells in Western Canada were utilized to verify the models of this new Autodriller system. DWOB comparisons between the model and the measuring tools were carried out. The comparisons indicate a good agreement between the downhole measured DWOB and the new Autodriller predicted values. The difference between the new Autodriller prediction and downhole measured DWOB can be quantified using rooted mean square error (RMSE) or relative error (RE). This paper also analyzes the differences in some sections, and some measures are suggested to potentially reduce these differences. The new Autodriller is a closed loop control system which can automatically in real-time adjust surface weight on bit (SWOB) so that the DWOB is accurate, which will directly improve the performance of drill bits, and decrease the cost of drilling, especially in directional well drilling applications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".