Predicting oil saturation of shale-oil reservoirs using nuclear magnetic resonance logs
Bibliographic record
Abstract
Abstract Oil saturation is an important parameter in shale-oil reservoir evaluation. However, due to complex wettability and pore construction, we find that conventional resistivity and nuclear magnetic resonance (NMR) methods do not perform well in calculating oil saturation in shale-oil reservoirs. Hence, we have developed a practical NMR-based method to calculate the oil saturation of the Lucaogou shale-oil Formation, Permian, in Jimusar Sag, Junggar Basin, China. First, we analyze the relationships among the wettability, oil saturation, and T2 distribution based on the theoretical formula and core analysis data. Results indicate that the ratio of the surface area wetted by water and oil is approximately equal to the ratio of water saturation and oil saturation. So we conclude that oil is mainly stored in relatively bigger pores and the surface relaxivity of the oil-wet surface is lower than that of the water-wet surface, resulting in long relaxation signals, that is, the long relaxation signals of NMR T2 spectra of shale-oil reservoirs are primarily attributed to oil signals. We have made a series of NMR measurements of as-received samples and confirm this point. Thus, we propose a T2 cutoff for water and oil to calculate the oil saturation, and we determine 6 ms as the T2 cutoff based on the oil saturation analysis of cores and NMR logs. Finally, we verify and make application of our method and acquire good results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".