Three-dimensional geosteering coherence attributes for deep-formation discontinuity detection
Bibliographic record
Abstract
ABSTRACT Deep-formation oil/gas exploration is a key objective in the geophysical field, and structural and stratigraphic discontinuities, such as faults and channels, usually contribute to the construction of traps and reservoirs. Coherence has been used successfully to identify these abnormal features in seismic amplitude volumes. However, the current coherence algorithms seldom involve the geologic concept. We propose geosteering coherence attributes by implementing the coherence calculation perpendicular to the direction of the structural trend in a 3D curved plane. We estimate a group of time lags between the original analysis trace and each original neighboring trace along a certain spatial direction by using dip scanning. For each spatial direction, we subsequently construct two new model traces by weighting phase traces derived from the complex seismic traces, in which time lags are eliminated. We then use the new model traces to compute the crosscorrelation coefficients for each spatial direction. We finally obtain the 3D geosteering coherence attributes by taking the minimum values among the modulus of the crosscorrelation coefficients along different spatial directions to approximately characterize the coherence perpendicular to the structural trend in a 3D curved plane. An example of the 3D physical modeling involving fracture groups and faults embedded in the deep formation is used to demonstrate the effectiveness of the 3D geosteering coherence attributes. The applications on two real 3D seismic data sets of sand reservoirs from western deep formation illustrate that our method can alleviate the influence of dipping strata and can highlight subtle structures. Compared to the conventional coherence method, our method can highlight subtle geologic structures more and better, suggesting that it may be serve as a future tool for detecting the distribution of geologic abnormalities in deep exploration.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".