In situ stress and reservoir quality evaluation of Jurassic Ahe Formation in Kuqa depression of Tarim Basin, China
Bibliographic record
Abstract
The Jurassic Ahe Formation in Kuqa Depression of Tarim Basin shows huge potential for hydrocarbon exploration, however, the stress is intensive due to active tectonic movements. The hydrocarbon exploration process is hindered, therefore, attentions are needed to pay in the stress fields and their controls on reservoir quality. The direction of maximum horizontal stress (SHmax), and magnitudes of SHmax, minimum horizontal stress (Shmin) and vertical stress (Sv) of Ahe Formation were systematically analyzed using image logs, sonic logs and conventional well logs. The controls of in-situ stress fields on reservoir quality (porosity and fracture) are unraveled. The results show that the reservoir pore types include intergranular pores, intragranular dissolution pores and fracture as well as micropores. The SHmax direction is near North-South according to borehole breakouts and drilling-induced fractures, and controls fracture effectiveness. The stress magnitudes control matrix porosity and fracture porosity, and both matrix porosity and fracture porosity decrease with the increasing horizontal stress difference (SHmax-Shmin). Layers with high horizontal stress differences are heavily compacted, and they have no evident pore spaces. Low horizontal stress difference is mainly associated with the fractured zones. The results help predict reservoir quality and fractures of the Jurassic Ahe Formation in Kuqa Depression.
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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 | 0.001 | 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".