Present-day temperature and pressure fields in key areas of Northeast China: Implications for unconventional resource evaluation
Bibliographic record
Abstract
The temperature-pressure fields within hydrocarbon-bearing basins are key geological factors controlling hydrocarbon generation, migration, and accumulation. In this study, we focus on the Qingshankou Formation in the northern part of the central depression of the Songliao Basin, China. A multi-parameter weighted evaluation model was created using present temperature-pressure field characteristics, with formation temperature-pressure as core variables, to evaluate shale oil resource potential. In addition, we explored the control mechanisms of temperature–pressure evolution during geological history on shale oil accumulation and further assessed the applicability of the proposed method. Our results show that the geothermal gradient of the Qingshankou Formation decreases from Member 1 to Member 3 (3.84°C/100 m, 2.93°C/100 m, and 2.49°C/100 m, respectively). High-temperature zones are widely distributed in the Gulong sag, with the average temperature of the Gulong shale exceeding 95°C and reaching an average of approximately 115°C. Overpressure in the Qingshankou Formation exhibits a west–high to east–low trend. The overpressure zones of the Gulong shale are mainly concentrated in the Qijia–Gulong and Sanzhao sag, with average pressure coefficients of 1.52 and 1.36, respectively. The Opc model identified Class I and II favorable zones, mainly located in the central and southern parts of the Gulong Sag, as well as the central and southwestern Sanzhao Sag, with estimated shale oil resources of 7.1 × 10 8 tons and 17.2 × 10 8 tons, respectively. Evolutionary profiles from representative wells indicate that elevated temperatures enhance organic matter maturation and light oil generation, improving shale oil mobility, while overpressure suppresses hydrocarbon dissipation and provides a sufficient driving force for oil production. This study demonstrates that present-day temperature-pressure fields effectively reflect the evolution trends of paleo-thermal and pressure regimes. The proposed evaluation method shows strong applicability and scalability, offering a new technical framework and theoretical foundation for the exploration of unconventional hydrocarbon resources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".