Accuracy limitations of fog-based continuous measurement-while-drilling surveying instruments for horizontal wells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it