Bibliographic record
Abstract
Abstract Passive magnetic ranging (PMR) is a technique used to determine the range and direction to a cased well that may not be accessible during the drilling of an adjacent well. It has been used since the 1970s (wireline) in relief wells and anti-collision applications where dual-well active ranging techniques cannot be used [Grace, Robinson and Vogiatzis, 1972, Morris et al., 1978]. PMR in MWD tools is a nascent business, and encountered some early resistance [Towle and Van Steenwyk, 1999]. It requires very accurate magnetometer measurements, preferably corrected in-situ for residual bias errors, a large number of static surveys, and manual interpretation of the data. All of this has made PMR a slow process, and thus it has not been widely adopted in markets such as the steam-assisted gravity drainage (SAGD) market in Canada, where speed is essential. A new PMR technique is outlined in the paper that achieves speed and accuracy comparable to dual-well active ranging techniques. It uses existing MWD magnetometer hardware, but does not require an excessive number of surveys or pre-magnetization of the casing in the offset well. Using Kalman filtering techniques adapted from aerospace navigation algorithms, the single-well technique provides continuous ranging output while enabling nearly uninterrupted drilling [Gelb, 1974]. The paper includes a discussion of the theoretical underpinnings of the technique and detailed field test results. The ranging algorithm was shown to be robust in above-ground testing, consistently converging in the presence of gross initial position uncertainty. A vertical test well drilled using the new ranging system demonstrated successful ranging over a wide span of well-to-well distances. The MWD ranging estimate showed good agreement with a pair of gyro surveys (test well and cased-target well) performed after tests conclusion. MWD tools with PMR can be broadly applied to enhance accuracy, speed, and safety in markets for SAGD, anti-collision and relief well drilling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".