Gravity-Derived Borehole Azimuth through Gravity In-Field Referencing
Bibliographic record
Abstract
Abstract Safe and economical determination of wellpath in directional drilling is traditionally achieved by the measurement-while-drilling (MWD) method which implements geopotential sensors, i.e., magnetometers and accelerometers. However, inaccuracies in determination of the wellpath arise because of random and systematic errors in measurements. In general, inclination is under good control with gravity measured by accelerometer, while azimuth requires a number of corrections as it also requires magnetic measurement which involves multiple sources of errors such as sensor errors, poorly-modelled crustal magnetic variation, drillstring magnetization, etc. These errors must be completely reduced or minimized to obtain an accurate wellbore position. A magnetic-free system and method to determine a borehole azimuth in the directional drilling is investigated in this study. We show that a borehole azimuth can be properly determined by using a system of coupled accelerometers mounted on measurement-while-drilling (MWD) sensors using Gravity in-Field Referencing (GiFR). In order to reduce errors due to relative oritation of the coupled drillstrings, we developed the Quaternion-based GiRF which considers the relative diaplacements and rotations between the two sets of accelerometer resulting in determining an improved azimuth.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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".