Reservoir Characterization of Multi-Stage Valley Fill through the Use of Hit Cube Stochastic Inversion
Bibliographic record
Abstract
Summary A probabilistic model matching inversion method, called HITCUBE, is used in the reservoir characterization study. This stochastic workflow can be executed with poststack or prestack (partial angle stack, offset gather, AVO gradient) seismic data while matching the real seismic trace with the modeled synthetic trace (similarity, cross-correlation or amplitude spectrum) generated from an isotropic ray tracing method. The property traces from corresponding models with a correlation beyond threshold are stacked to build the output probability grids. Based on rock physics analysis of existing well log data, the relationship of the elastic properties (Vp, Vs and Rho) of the target formation and the rock properties (lithology, porosity, water saturation) is built as a physical representative of the geology in the study area which is then used for pseudo-well generation. A number of pseudowells can be generated through Monte Carlo simulation referring to the rock physics analysis result, the geological feature of the study formation and the uncertainty. This workflow is successfully applied in the Upper Mannville Group clastic reservoir characterization using a public seismic dataset with multiple wells. The seismic gather data are preconditioned with an AVO friendly workflow before the inversion. Optimized reservoir facies with better reservoir quality are characterized.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".