High-frequency anomalies in carbonate reservoir characterization using spectral decomposition
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Bibliographic record
Abstract
Abstract Low-frequency shadows have often been used as hydrocarbon indicators in the application of spectral decomposition. The reason behind the low-frequency anomaly has been explained as high-frequency energy attenuation caused by hydrocarbons. However, in our practice on carbonate reservoir characterization in two areas, Precaspian Basin and Central Tarim Basin, China, we encountered high-frequency anomalies, i.e., the isofrequency slices or sections at high frequencies exhibit anomalies associated with the good carbonate reservoir, particularly in the tight limestone background. We used the product of porosity and thickness as a parameter to measure the quality of the carbonate reservoir of each well and classified the 46 wells in our two studied areas into three types. Type I wells contain high-porosity thick reservoirs, type II wells contain reservoirs with moderate porosity and thickness, and type III wells contain only low-porosity thin reservoirs. The results were that 12 out of 13 type I wells exhibit high-frequency anomalies, and 30 out of 33 type II and type III wells do not exhibit high-frequency anomalies. We further validated the existence of this high-frequency anomaly by forward modeling analysis and fluid substitution experiments using the actual well-log curves measured in the carbonate reservoir. The results showed that in our two studied areas the high-frequency anomalies are more common than low-frequency shadows that can be observed only when the thickness of the reservoir is more than half of the wavelength or the reservoir rocks are extremely unconsolidated. Therefore, this high-frequency anomaly may be used as a more reliable indicator for a good carbonate reservoir than low-frequency shadows in real applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it