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 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 | 0.000 | 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) | 0.006 | 0.007 |
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; both teacher heads agree on what is shown here.
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".