Study of the effect of acquisition parameters of NMR on fluid identification
Bibliographic record
Abstract
Two-dimensional (2D) NMR logging has better application in the evaluation of fluid properties in unconventional reservoirs by introducing the longitudinal relaxation time (T1) to obtain more observational information. There are differences in the acquisition parameters between the Schlumberger high-resolution instrument CMR-NG and the Niumag 2D NMR experiments, and the comparison reveals the following main differences in the instrument parameters: the NMR experiments are high-frequency measurements, while the borehole instrument is a low-frequency frequency; the experimental acquisition of the number of echo numbers has a higher numerical value; and the borehole instrument utilizes six different sets of waiting times.Therefore, the analysis of the influence factors of acquisition parameters on NMR is carried out from two scales. The effects of instrument frequency and echo spacing on 2D maps are investigated through experiments, and the effects of echo spacing, waiting time and number of echoes on echo train and 2D maps are investigated through numerical simulations. The influence law of acquisition parameters on 2D NMR fluid distribution is investigated, and the correspondence between core measurement results and logging data in the study area is established to improve the reliability of NMR logging interpretation data.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".