Sedimentary sequence and architecture analysis by integrating multidiscipline data — An example of a sandy conglomerate reservoir in the Qie12 block, Qaidam Basin, northwest China
Bibliographic record
Abstract
Abstract Understanding the sedimentary sequence and architecture of a reservoir is important for planning its development strategy. We have developed a case study of analyzing the sequence and architecture of a sandy conglomerate reservoir by integrating geology, petrophysics, seismic, and development data. The analysis indicates that the depositional environment of the target lower part of the Ganchaigou Formation is an alluvial fan. We define eight lithofacies according to the core analysis and five sedimentary microfacies by integrating the core and petrophysical data. We next adopt four sedimentary architectural models by integrating the lithofacies result, microfacies analysis, and high-frequency sequence models. The four architecture models include the extensively connecting body sandwiched with intermittent channels, the composite channel formed by the overlapping and separation of stable channels, the lateral alternated braided channel and sheet flow sediment, and the runoff channel inlaid in flood plain mudstone. Finally, we build an architecture model for the alluvial fan. The model of the alluvial fan overall shows an upward-fining grain-size feature, and this feature indicates that the alluvial fan belongs to a retrograding sequence. The built model consists of six alluvial fan bodies that migrate from the north.
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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 | 0.003 | 0.003 |
| 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) | 0.001 | 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".