The Major Controlling Factors Analysis of Hydrocarbon Accumulation on the Gentle Slope Belt of the Southeastern Liaodong Bay Depression
Bibliographic record
Abstract
Oil and gas discovery in the Liaodong Bay Depression is mainly distributed in the steep slope, the uplift and the sag zone at current situation. The reservoir developed on southeastern gentle slope zone is the first discovery in this area, which has great significance to the study of the gentle slope zone hydrocarbon accumulation regularity. Based on the analysis of the hydrocarbon accumulation condition of the gentle slope zone, the major controlling factors and hydrocarbon accumulation pattern of the gentle slope zone are summarized. It shows that the gentle slope belt has the superior accumulation conditions, where develops Lithological-tectonics traps and lithological traps Controlled by fracture and provenance. The source rock circumstance is advantageous too, because the gentle slope zone is close to the hydrocarbon-rich Liaozhong sag. The mudstone in Dongying formation(Ed) and the first section of Shahejie formation (Es1) and the sandstone in the second member of Shahejie formation (Es2) are form into a good reservoir cap association. Hydrocarbon accumulation in the gentle slope belt is mainly controlled by 3 factors: the development of the reservoirs in the Es2 is controlled by the valley-slope break coupling; the migration of oil and gas is controlled by “Fault-unconformity-sandbody”(FUS) long-range efficient migration system; reservoir physical properties and oil and gas test capacity are controlled by sedimentary facies differences. There are two reservoir models: lithology - tectonics and lithology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".