Hydrocarbon generation and expulsion features of the Upper Triassic Xujiahe Formation source rocks and their controlling effects on hydrocarbon accumulation in the Sichuan Basin, Central China
Bibliographic record
Abstract
Understanding the features of the hydrocarbon generation and expulsion of the Upper Triassic Xujiahe Formation (T 3 x) source rocks is important to properly guide petroleum exploration in the Xujiahe Formation in the Sichuan Basin. Based on mass balance, the hydrocarbon generation and expulsion from the T 3 x source rocks are characterized in this paper through a method (i.e., hydrocarbon generation potential index using pyrolysis data) different from that used in previous studies. The threshold and peak of hydrocarbon expulsion are determined to be R o = 0.9% and 1.15%, respectively. The amounts of hydrocarbon generated and expelled in the T 3 x source rocks are 4,525.5 × 10 8 and 2,420.5 × 10 8 t, respectively. Moreover, three hydrocarbon expulsion regions T 3 x have been identified in the Sichuan Basin. On the basis of the hydrocarbon generation and expulsion features of the source rocks and the distribution of the oil‐and‐gas reservoirs, the quantitative model of the hydrocarbon accumulation probability under the control of the T 3 x source rock is established via single‐factor regression analysis. The predictions obtained through this analysis show that the favourable oil‐and‐gas exploration areas in the Xujiahe Formation are distributed in the Qionglai, Chengdu, and Deyang regions, as well as in the west of Yilong region. Further, the results show that 78% of the discovered oil‐and‐gas reservoirs are located in the predicted favourable and relatively favourable oil‐and‐gas exploration areas.
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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 | 0.001 |
| 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".