Trial and progress of PetroChina onshore multi-component seismic techniques
Bibliographic record
Abstract
The multi-component seismic exploration techniques have drawn great attention in the petroleum industry and realized industrial application in offshore petroleum exploration, because they apply the S-wave information, reduce the ambiguity in reservoir prediction, and improve the prediction and identification possibility of reservoir fluids. Digital geophones emerged in late 1990s further promoted the progress of onshore multi-component seismic exploration techniques. To push forward the application and development of the technique in China, onshore 2D or 3D multi-component technical tests have been carried out since 2002 in Sulige Gasfield, Ordos Basin; Guang'an Gasfield, Sichuan Basin; Xushen Gasfield, Songliao Basin; and Sanhu area, Qaidam Basin, with certain progress achieved. The imaging of the converted wave is better than that of the P-wave for the deep igneous rocks in Songliao Basin and the gas-bearing structures in Qaidam Sanhu area, which can define the reservoir boundaries more accurately. In the description of tight sandstone gas reservoir in Sulige and Guang'an Area, the converted wave imaging improves the accuracy of reservoir prediction and fluid identification, paving the way for the arrangement of wells. With continuous progress, multi-component seismic exploration techniques will play a more important role in exploration of complex lithologic reservoirs, as well as evaluation and development of hydrocarbon potential.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".