Multivariate stochastic seismic inversion with adaptive sampling
Bibliographic record
Abstract
ABSTRACT We have developed a fully coupled categorical-multivariate continuous stochastic inversion with a combined petro-elastic model and convolution. The new multivariate stochastic seismic inversion approach simulates multiple reservoir properties simultaneously and conditions them to the well and seismic data at the same time through the close integration of multivariate geostatistical modeling and stochastic inversion. This approach combines a trace-by-trace (column-wise) adaptive sampling algorithm with multivariate geostatistical techniques to select reservoir properties that match the seismic data. The adaptive sampling method uses an acceptance-rejection approach to condition geostatistical models to the well and seismic data. The adaptive sampling algorithm defines a practical stopping criteria based on the inherent uncertainty due to modeling assumptions and the size of the uncertainty space. This technique samples the realizations inside the space of uncertainty; the number of realizations attempted increases with the size of the space of uncertainty. Characterizing multiple reservoir properties simultaneously through the close integration of seismic inversion and multivariate geostatistical techniques leads to improved high-resolution reservoir property models that reproduce the original seismic data. A case study is considered to compare the proposed stochastic inversion approach with the conventional methods. The case study represents multivariate stochastic inversion provides high-resolution facies and reservoir physical properties simultaneously that reproduce the original seismic data within quality of data better than the other approaches.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".