Design and Algorithm Verification of a Gyroscope-Based Inertial Navigation System for Small-Diameter Spaces in Multilateral Horizontal Drilling Applications
Bibliographic record
Abstract
In the recent years horizontal drilling (HD) has become increasingly important in oil and gas exploration because it can increase the production per well and can effectively rework old and marginal vertical wells. The key element of successful HD is accurate navigation of the drill bit with advanced measurement-while-drilling (MWD) tools. The size of the MWD tools is not significantly restricted in vertical wells because there is enough space for their installation in traditional well drilling, but the diameter of devices for HD must be restricted to less than 30 mm for some applications, such as lateral drilling from existing horizontal wells. Therefore, it is essential to design miniature devices for lateral HD applications. Additionally, magnetometers in traditional MWD devices are easily susceptible to complex downhole interferences, and gyroscopes have been previously suggested as the best avenue to replace magnetometers for azimuth measurements. The aim of this paper is to propose a miniature gyroscope-based MWD system which is referred to as miniature gyroscope-based while drilling (MGWD) system. A prototype of such MGWD system is proposed. The device consists of a two-axis gyroscope and a three-axis accelerometer. Miniaturization design approaches for MGWD are proposed. In addition, MGWD data collection software is designed to provide real-time data display and navigation algorithm verification. A fourth-order autoregressive (AR) model is introduced for stochastic noise modeling of the gyroscope and the accelerometer data. Zero velocity and position are injected into a Kalman filter as a system reference to update system states, which can effectively improve the state observability of the MGWD system and decrease estimation errors. Nevertheless, the azimuth of the proposed MGWD system is not observable in the Kalman filter, and reliable azimuth estimation remains a problem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".