Prestack Bayesian lithofacies classification technology and application in oil-sand reservoir prediction
Bibliographic record
Abstract
Abstract The oil-sand reservoirs in the Athabasca region of Canada are estuarine deposits affected by tides. The strata are inclined, and the interlayers are well-developed. Accurate spatial characterization of reservoirs and interlayers is the key for efficient oil-sand development. In this paper, we have used prestack Bayesian lithofacies classification technology to predict the spatial distribution characteristics of reservoirs and interlayers of oil-sand reservoirs. We first use log lithofacies data as a label, select lithofacies sensitive elastic parameters to make a lithofacies classification probability distribution crossplot, and then project the lithofacies-sensitive elastic parameter volumes into the lithofacies classification probability distribution crossplot. Finally, we predict the spatial probability distribution of different lithofacies. Probabilistic characterization can enhance the recognition of transitional lithology and thin layers in the inversion results, reduce the uncertainty in the prediction of reservoirs and interlayers, and significantly improve the prediction accuracy of reservoirs and interlayers. The field application results in the Kinosis study area indicate that the probability volume predicted by this technology can distinguish interlayers greater than 1 m thick and identify interlayers greater than 2 m thick, which meets the technical requirements of oil-sand steam-assisted gravity drainage development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".