Case study from northwest China: application of Biot‐Gassmann's equation to identify sand and isolate oil from the prestack seismic data
Bibliographic record
Abstract
There exist many techniques to identify deeply buried sand within reservoir. For example, the inverted impedance can be used to distinguish subsurface shale or sand. However, most of these techniques are based on and inverted from post-stack seismic data. Alternatively, potential information is possibly extracted from prestack seismic data to improve the capability to distinguish sand from shale and to map oil spatial distribution. In this paper, we first use Biot-Gassmann's equation to derive elastic moduli and Biot coefficient. Then substitute water with oil gradually to test elastic parameter's sensitivity to the varying water saturation. These parameters include acoustic velocity, shear velocity, effective density, acoustic impedance, shear impedance, ratio of acoustic velocity versus shear velocity, and Poisson's ratio. Using the experience formula to further isolate fluid component to monitor fluid variations. Based on the derived information, we extract elastic parameters from the common shot gather from which to distinguish sand from shale. Then the final oil spatial distribution is mapped along the target horizon.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".