Improved prediction of thin reservoirs in complex structural regions using post-stack seismic waveform inversion: a case study in the Junggar Basin
Bibliographic record
Abstract
The Mahu sag slope area, which holds significance as an oil and gas resource, still have some underexplored regions because of structural mismatches, presenting a potential challenge to be properly addressed. To resolve, this study conducts a comprehensive investigation concerning structural characteristics, fault combinations, favorable reservoir distribution, reservoir control factors, and oil-water distribution characteristics within the Triassic Baikouquan Formation, evaluating the impact of depositional environments and sedimentary dynamics on reservoir quality. For this purpose, constrained sparse spike inversion and seismic waveform indication inversion were employed to comparatively evaluate oil and gas reservoirs, further integrating petrophysical and geological data with geological modeling to enhance accuracy in complex structural geology and enable high-precision reservoir prediction. The findings elucidated the distribution range of the Baikouquan Formation and the location of oil reservoir sand bodies, as exemplified by well B and identified potential hydrocarbon traps, offering valuable insights into reservoir performance. It demonstrated comparatively reliable effects and considerable predictability power of seismic waveform indication inversion. These outcomes provide a strong foundation for future evaluations and multi-layer system deployment in the region by serving as a novel valuable framework for subsequent development activities not only in the Mahu sag but also in similar regions.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".