Spatial-vector-based reservoir architecture modeling of point-bar sand
Bibliographic record
Abstract
The spatial-vector-based stochastic modeling of point-bar reservoir architectures is proposed in this paper.Compared with traditional object-based modeling approaches,this proposed modeling approach has no cell or grid definition at the simulation stage.Architecture elements are simulated directly by dropping them into the modeling domain.Borrowing the idea from the vector image format in computer graphics,we used spatial lines,points and surfaces to form simulated architecture element bodies.The architecture elements are defined with spatial vectors which are expressed using their spatial distribution parameters.And the shape parameters are in real data set.Thus,different scale of heterogeneities can be reproduced from the realization.Because the well data conditioning is obtained through the modification of the architecture elements' spatial locations and their shape parameters,it is easier to be satisfied.And the simulation has a faster convergent speed compared with traditional object-based approaches because of grid free and parameter adjustment in simulation.A developed block of one oilfield in eastern China,which has a geological background of meandering-river point-bar sand,is used to illustrate the theory and modeling procedures of this approach.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".