The Comparison between Full-Stack Data and Pure P-Wave Data on Deeply Buried Ordovician Paleokarst Reservoir Prediction
Bibliographic record
Abstract
<span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The present burial depth of the paleokarst reservoirs in the Tarim Basin is greater than 5000m, which premises rigorous demands for the data quality for accurate predictions. Although routinely used for reservoir prediction, the conventional full-stack data are often contaminated by the AVO (amplitude versus offset) effects no matter how much signal to noise ratio degree can be enhanced via stacking. This contamination usually changes with different geological deposits, and could reach an inappropriate and unacceptable level for the deeply buried carbonate reservoir in the Tarim Basin. In this paper, the pure P-wave data theoretically inverted through AVO effects removal are employed to improve reservoir prediction, which are illustrated by the comparisons with that of the conventional full-stack data for the classical four AVO models and the field data in the ZG8 area, Tazhong Uplift, central Tarim Basin. The dominant frequency has been improved for 8 Hz from 15 to 23 Hz in the target Yingshan formation, Ordovician through AVO effects removal, and the reflection events of the pure P-wave data are obviously more continuous comparing with that of the full-stack data. The fake potential reservoir caused by AVO effects in the northeast area has been removed, while the integral delineation of paleokarst reservoirs have been significantly improved with a higher fitness with the oil-testing results, which are more beneficial for later on exploration. The pure P-wave data inversion is in essence an important complement to current processing strategy.</span>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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; both teacher heads agree on what is shown here.
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".