UKF-based estimation fusion of Underbalanced Drilling Process using pressure sensors
Bibliographic record
Abstract
Under Balanced Drilling Process (UBD) is a drilling technique that is usually used as a remedy for the drawbacks of the Over Balanced Drilling (OBD) processes. It is an unconventional drilling procedure which adds the ability of production from the reservoir while drilling and reducing or total elimination of formation damage from the drilling fluid as well as faster drilling rates. Faster drilling and less formation damage can be the economical motivation and added benefit for UBD. However, there exist several limitations of the UBD process parameters which make the monitoring and controlling a necessary task. Because of several limitations on mixture velocities in the annulus, an Unscented Kalman Filter (UKF) approach is introduced to monitor the mixture velocity inside the drillstring and in the annular sections. To improve the monitoring of an actual UBD process, pressure sensors were installed mostly in the annulus to enhance the accuracy and robustness of monitoring and an estimation fusion scheme was developed. This paper illustrates a new efficient estimation fusion scheme for both types of central and distributed configurations, leading to new tool for UBD processes modeling.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".