Introduction to Borehole Studies
Bibliographic record
Abstract
Borehole methods exploit some of the same anomalies in physical properties of gas-hydrate-bearing sediments as do regional geophysical methods described in the previous two sections. These include anomalies in elastic properties and hence in P- and S-wave velocities, as well as anomalies in electrical resistivity. A log-based characterization of gas-hydrate environments also typically includes logs of the caliper (borehole diameter as a proxy for data quality), gamma ray (used, e.g., for sand-detection), porosity, and density. Special logging applications using the nuclear magnetic resonant (NMR) technique have also been used (e.g., Kleinberg et al., 2005) but appear to be most successful in thick sand-rich gas-hydrate occurrences. In principle, one can divide borehole logging approaches into two groups: logging-while-drilling (LWD) and measurement-while-drilling (MWD) as well as wireline logging. LWD/MWD offers an opportunity to determine the physical properties of sediments as the borehole is advanced, whereas wireline logging is always deployed after a borehole has already been drilled and measurements are sometimes made after considerable time delays. Thus, wireline logging data suffer more from potential borehole deterioration (or infill), and the risk is higher that gas hydrate in the near-well bore environment have either dissociated or additional artificial gas hydrate has been formed if drilling fluids were cooler than the ambient in situ temperatures. Wireline logging is also typically performed with the drilling pipe deployed up to 60-m deep into the formation, thus the shallow sediment section is typically not logged. LWD/MWD in contrast can (if carefully deployed) provide full coverage of the entire sediment column penetrated. A comprehensive summary of the logging tools, techniques, and data from various drilling campaigns is provided by Goldberg et al. (2010).
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.023 |
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".