Determination of Residual Oil Distribution during Waterflooding in Tight Oil Formations with NMR Relaxometry Measurements
Bibliographic record
Abstract
The NMR relaxometry measurements have been designed and applied to quantitatively determine residual oil distribution during waterflooding in tight oil formations. A tight core sample is first saturated with water to measure its NMR transverse relaxation time ( T 2 ) spectrum. NMR T 2 spectrum is then measured for the core sample after it has been displaced with the fluorinated oil. Subsequently, the core sample is displaced with water until residual oil saturation is achieved, and the NMR T 2 spectrum is measured again at the end of the displacement. Subsequently, the constant-rate mercury injection method is used to experimentally measure the size of the pore and throat in the core sample. The residual oil saturation is determined as a function of pore size by comparing the difference between the first and last NMR T 2 spectrum. It is found from four core samples with permeability of 0.04–1.70 mD that the average pore size is in a range of 129–145 μm, and the pore throat has a radius of 0.17–0.89 μm. The original oil saturation is found to be 76–83%, whereas the oil recovery factor is 36–62%; 4–27% of the original oil is distributed in pores larger than 100 μm, 50–54% in pores from 10 to 100 μm, and 21–46% in pores and throats smaller than 10 μm. Residual oil saturation is 1–2% in pores larger than 100 μm, 29–64% in pores from 10 to 100 μm, and 34–69% in pores and throats smaller than 10 μm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".