The Field Tests for Measurement of Downhole Weight on Bit (DWOB) and the Calibration of a Real-time DWOB Model
Bibliographic record
Abstract
The Drilling Engineering Research Group at the University of Calgary has been seeking better ways of improving drilling operations and decreasing costs by the use of advanced real-time modeling and simulation technologies. It is well known that the performance of a drill bit directly affects the overall drilling performance. The bit performance is often evaluated by the rate of penetration (ROP) which is dependent on the weight on bit (WOB). Therefore, obtaining actual downhole weight on bit (DWOB) is crucial in achieving good performance of a drill bit. This paper defines the procedures or steps to measure DWOB and an analytical model for calculating DWOB using typical surface collected drilling data. Field test data is used to initially calibrate the analytical model. The calibrated analytical model is next used in a forward calculation to predict DWOB on the same well. DWOB is also predicted on a second well using the same drilling rig. The results from the calibrated model are compared to the DWOB collected by the CoPilot, a Baker Inteq downhole measurement tool. The comparison shows that the DWOBs from the model match those from the CoPilot well. The model can be integrated in a new directional Autodriller system, which can in real time set the DWOB from surface measurements. The directional Autodriller can automatically conduct real-time analysis and calculations of DWOB as well as maintaining a precise DWOB for the drill bit. This will improve drilling efficiency and reduce cost.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".