Geometry Matching Technology of Non-Repeating Acquired Time-Lapse Seismic Data Processing
Bibliographic record
Abstract
Different from the normal time-lapse seismic technology, time-lapse seismic technology with non-repeating acquired data utilize existing multi-period seismic data with different geometries at different exploration period of the same area. Not only the change of reservoir parameters cause the property differences of two-period data but also the difference of geometries, this uncertainty has become a fundamental problem in application of time-lapse seismic technology. In order to solve this problem, this paper takes advantage of the 3D Gaussian beam simulating method for illumination analysis of reservoir model, then we analyzes the impacts of various parameters of geometry on receiving energy of reservoir, finally we put forward the main factors affecting imaging of reservoir : the distribution of offset and azimuth. Basing on this conclusion this paper established the work flow of geometry matching for non-repeating acquired seismic data. By processing real data in S block, this technology could effectively reduce the affects of geometry difference and obtain an obvious result, and also provide an idea to increase value of multi-period seismic data in old oil field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".