Comprehensive Prediction of Dominant Reservoirs in the Guantao Formation Volcanic Rock Reservoir in NanPu Oilfield
Bibliographic record
Abstract
Abstract The Guantao Formation volcanic rocks are nearly 200m thick in the No. 1 structural belt of Nanpu Sag. The recognition degree of volcanic lithology and rock equality is low. The reservoir characteristics and accumulation rules of volcanic reservoir are not clear. The features of volcanic reservoir and its accumulation laws are not well - understood. In order to offer a basis for achieving large - scale production, a comprehensive assessment of the favorable volcanic rock reservoirs should be conducted. Analysis is made on the key factors controlling volcanic reservoirs by comprehensively researching lithology, lithofacies, physical properties of reservoirs, reservoir spaces and oil content, etc. The differential enrichment laws of volcanic oil and gas in Nanpu No.1 structural belt are summarized as volcanic crater control area, oil source fault control zone, and dominant reservoir control reservoir. By superimposing multiple maps such as structure, lithology, lithofacies, reservoir physical property classification plan, gas logging ratio, gas logging thickness contour map, it is optimized that the volcanic reservoir near the crater and oil source fault are developed as favorable reservoirs of Guantao Formation volcanic reservoir in No. 1 structural belt of Nanpu Sag. The oil test results show that the evaluation results are reliable. This method is of great value in terms of application and popularization for predicting the dominant reservoirs within volcanic reservoirs. Meanwhile, it has some guiding value for the exploration and development of Guantao Formation volcanic rocks in Nanpu Sag.
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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 | 0.001 | 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".