Application of Grid-Free Geostatistical Simulation to a Large Oil-Sands Reservoir
Bibliographic record
Abstract
Summary Geostatistical simulation is performed for reservoir characterization to depict local variability in the modeled properties. The conventional simulation methods are implemented in a grid-dependent manner that makes regridding of realizations, refinement of existing grids, and the simulation on irregular grids challenging. The grid-free-simulation (GFS) method has been recently developed for flexible reservoir characterization. The geostatistical realizations of a reservoir are expressed as an analytical function of the coordinates of the simulation locations and, thus, are infinitely resolvable. The resulting model is conditioned to primary scattered point-scale hard data and secondary exhaustively sampled block-scale soft data. The former data are sampled along wells, whereas the latter data are from seismic surveys. The GFS methodology is applied to the Firebag oil-sands thermal project operated in northern Alberta, Canada. The conditioning data are point-scale core measurements, log observations, and block-scale acoustic impedance (AI). The models of correlated porosity, permeability, and water saturation attributes are constructed on three different grids by facies and are consistent with each other. These models are intended for resource estimation, reserves estimation, and subsequent-flow simulation, respectively. The modeling results of the grid-independent simulation method are promising for industrial application to petroleum reservoir characterization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".