Object modeling for reservoir characterization in carbonates
Bibliographic record
Abstract
Summary During the past 15 years, reservoir modeling methods based on objects have been developed and applied successfully (e.g. Deutsch & Wang, 1996; Holden et al., 1998; Hauge & Syversveen, 2003). A Boolean approach is generally better at integration of conceptual geologic information than traditional pixel-based methods using semivariograms. Variogram based methods struggle to accurately model reservoirs where depositional bodies and geologic shapes (which are typically curvilinear) control the distribution of flow properties (i.e. porosity and permeability). Fluvial reservoirs, characterized by a complex network of individual sand bodies, are hence well suited to object-based models (e.g. Holden et al., 1998). For carbonate systems, the application of object-based models has seen limited application, primarily due to the problem of defining carbonate depositional geometries and distributions and the impression that random, diagenetic influences are more important than depositional characteristics. Further challenges arise for Boolean methods with integration of denser well and 3D-seismic datasets (Strebelle & Levy, 2008). An application of carbonate-object models is presented here with results and sensitivities associated with large-scale CO
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".