Prediction Technology for Top Gas Reservoir of Oil and Gas Reservoir of Three-Dimensional Seismic Pre-Stack Parameters: Application in Jinzhou South Oil and Gas Field in Bohai Sea
Bibliographic record
Abstract
It is the key of using the oil layer efficiently to avoid the top gas reservoir of the oil and gas reservoir in the development of the offshore oilfield. The oil reservoir in the second member of the Shahejie Formation of the Jinzhou South Oilfield in the Bohai Sea is a complex oil and gas reservoir of top gas, narrow oil ring and edge water. The oil and gas reservoir is longitudinally divided into multiple sets of fluid systems, and the gas-oil interfaces of different fault blocks are not consistent with large differences. The positions of the gas-oil interfaces and oil-water interfaces need to be determined precisely in the development and design of the horizontal wells on the drilling platforms, so as to prevent the premature gas channeling and water invasion of the production wells. Thus the identification of the top gas reservoir is particularly important. In this paper, we used seismic attribute analysis, pre-stack elastic parameter coordinate rotation method fluid detection and other technologies to identify the top gas reservoir of the oil and gas reservoir in the second member of the Shahejie Formation of the Jinzhou South Oilfield in the Bohai Sea, and obtained good application effects. Key words : Oil and gas reservoir; Top gas reservoir; Three-dimensional seismic high resolution amplitude preservation processing; Seismic attributes; Pre-stack elastic parameter coordinate rotation method; Fluid detection
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".