Modeling of Observability During In-Drilling Alignment for Horizontal Directional Drilling
Bibliographic record
Abstract
Navigation performance is an important factor in horizontal directional drilling. In-drilling alignment (IDA) was previously suggested to improve downhole navigation performance when utilizing an inertial navigation system (INS). It was shown that the IDA process enhances the ability to estimate INS bias and drift errors and, particularly, their azimuth-related components. It was suggested that this improvement was related to a better observability that is achieved with the help of the induced dynamics during the IDA phase. The observability of a system is an important parameter that facilitates the estimation of the state parameters and the achievable accuracy of the system. However, observability models that are related to the IDA technique are lacking. This paper presents observability modeling of the newly suggested IDA process to aid horizontal drilling. The presented methodology clearly demonstrates that an induced motion during the IDA process increases system observability and converts the azimuth angle into an observable system state. Adequate system modeling profoundly influences the overall system observability. The utilization of the vertical damped model with a reduced state order is preferable for a faster and more efficient performance due to a decreased computational load but remains inferior when compared to a full system model.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".