MEMS-based Downhole Inertial Navigation Systems for Directional Drilling Applications
Bibliographic record
Abstract
Conventional methods in horizontal drilling processes incorporate magnetic surveying techniques for determining the position and the attitude of the bottomhole assembly. Magnetic surveying results in higher costs and relatively large surveying errors. Micro-Electromechanical Systems (MEMS)-based inertial navigation has been proposed as an alternative to magnetometer-based downhole surveying due to its light weight, small size and low power consumption. The present study explores the feasibility of utilizing MEMS-based inertial measurement unit as a surveying sensor, in conjunction with a Rotary In-Drilling Alignment (R-IDA) method for measurement-while-drilling (MWD) processes. A novel, downhole-mountable, autonomous and cost-effective apparatus to practically implement R-IDA has been proposed and its capabilities to reduce the azimuth error have been assessed. Furthermore, this study discusses the concept of wireless data transmission within drill pipes downhole. It is shown that wireless telemetry inside the drill pipe is potentially capable of transmitting at a relatively high rate with low power consumption.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".