The Hydrocarbon Mobility Evaluation of middle Eocene low Maturity Lacustrine Shale in the Bohai Bay Basin: Implications from NMR Analysis
Bibliographic record
Abstract
A vital factor influencing shale oil exploration in lacustrine shale reservoirs is oil mobility, which is closely associated with the shale pore structure and fluid properties, especially for the low-maturity lacustrine shale in China. In this study, the oil mobility and shale oil potential in the Middle Eocene Shahejie Formation lacustrine shale (MES shale) of the Nanpu Sag in the Bohai Bay Basin were evaluated by using nuclear magnetic resonance (NMR) experiments. The low-maturity MES shale has low porosity with various pore types including intergranular and dissolution pores and microcracks. Its pore space spans nano- to microscale with dominant mesopores. The portion promoting fluid flow is complex and has good self-similarity with high high fractal dimensions. The porosity is related to the thermal maturity, and a higher maturity facilitates pore space development. The oil saturation in low-maturity shale is lesser with low free hydrocarbon due to the low the maturity. Considering the high viscosity and the dead oil, the NMR relaxation mechanism in smaller pore space of low-maturity shale is proposed to bulk relaxation. The movable oil with a viscosity lower than 10 cp accounts consideble pore space in the MES shale. Its viscosity relates with TOC content and thermal maturity. Comparing with other shale oil producing areas, MES shale has similar geological conditions and good brittleness, which hints a suitable and promising shale oil potential at low tectonic position in the Nanpu Sag under the technologies of in situ conversion process and hydraulic fracturing.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".