Accuracy limitations of fog-based continuous measurement-while-drilling surveying instruments for horizontal wells
Bibliographic record
Abstract
Horizontal drilling processes in the oil industry utilize directional measurement-while-drilling (MWD) instruments to provide real-time monitoring of the position and the orientation of the bottom hole assembly (BHA). It has been reported that a single fiber optic gyroscope (FOG) can be incorporated with three-axis accelerometers to provide real-time MWD surveying of horizontal wells. However, the long-term performance and the accuracy limitations of this FOG-based instrumentation system have not been discussed. This article aims at describing a methodology for quantitative long-term analysis of the various surveying errors while drilling the near-vertical sections of the well. It also offers some techniques to enhance the long-term surveying accuracy in an experimental model of the FOG-based downhole-surveying instrument. The surveying errors are optimally estimated by the Kalman filtering techniques, and their long-term analysis is based on studying the corresponding mean square estimation errors. In order to limit the long-term growth of the surveying errors, we suggest improving the velocity computation provided by the FOG-based system either by continuous velocity update or by zero velocity update at some predetermined surveying stations. These techniques have significantly limited the long-term growth of the position errors (less than 100 m over a more than 2-h experiment). Moreover, the errors associated with the BHA orientation components were kept at less than 1/spl deg/. Suggested methodology significantly improved the surveying accuracy in an experimental model of the FOG-based MWD surveying system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".