Experimental Feasibility of the In-Drilling Alignment Method for Inertial Navigation in Measurement-While-Drilling
Bibliographic record
Abstract
Conventional methods in horizontal drilling processes incorporate magnetic surveying techniques for determining the position and attitude of the bottom-hole assembly (BHA). This results in an increased weight of the drilling assembly, higher cost due to the use of nonmagnetic drill collars necessary for shielding the magnetometers, and significant errors in the position of the drilling bit. A novel inertial navigation system (INS)-based technique has been previously proposed as an alternative to magnetometer-based downhole surveying. Previous studies have shown theoretically that an adaptive-filter-based in-drilling alignment (IDA) fine alignment method successfully limits the error growth associated with INS. This study aims at examining IDA's practical feasibility and, specifically, its ability to estimate the azimuth angle. Experimental testing of the IDA method was conducted under laboratory conditions with an apparatus that can easily be adopted for downhole conditions. The experimental results demonstrate that the IDA-estimated azimuth is more precise, compared with the one estimated by conventional magnetic surveying systems. The high accuracy and implementation simplicity of the proposed INS-based surveying system render it a preferred method for future horizontal drilling operations.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".