High-precision seismic imaging for complex deep structures in the hydrocarbon exploration using a coherent-stacking-based least-squares migration
Bibliographic record
Abstract
ABSTRACT With the rapid development of the petroleum industry, oil and gas exploration gradually changes from conventional shallow and middle-depth reservoirs to deep and ultradeep reservoirs. In western China, especially in the Tarim Basin, the deep Ordovician and ultradeep Cambrian carbonate is the most important hydrocarbon reservoir. But because of complex near-surface conditions and complicated subsurface structures, high-precision seismic imaging for such deep and ultradeep reservoirs is still challenging with the state-of-art migration methods. One of the critical factors is that the reflections from deep and ultradeep strata cannot be coherently stacked in the migration because of accumulative traveltime errors caused by an inaccurate velocity model. To mitigate this issue, we present a coherent-stack-based least-squares migration (LSM) approach to improve the imaging quality for deep and ultradeep structures. Unlike traditional LSM that uses the stacked gradient during iterations, the proposed method updates the reflectivity model in the subsurface half-opening angle domain and then applies a coherent stacking to implement constructive summation for angle-domain common-image gathers. The new LSM scheme enables us to reduce the artifacts caused by an inaccurate velocity model and produces high-quality images for deep and ultradeep strata. Two models with typical steep-dipping faults, overthrust folds, and fault-karst carbonate reservoirs are designed to test the feasibility of the proposed method, and a field data set from a land survey is used to demonstrate its adaptability for low signal-to-noise ratio data.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".