Estimating barrier shale extent and optimizing well placement in heavy oil reservoirs
Bibliographic record
Abstract
Shale barriers within the bituminous oil sand deposits of the McMurray Formation have a detrimental effect on the steam-assisted gravity-drainage chamber growth and oil recovery. Typically, the non-net shale barrier lateral extents are too small to be detected with a few widely spaced delineation wells. The information on net reservoir and shale interval thicknesses collected from wells, along with a vertical indicator variogram, provide limited information about the horizontal extent and connectivity of these intervals. In this paper, a novel quantitative approach for predicting the lateral extents of the barriers, using thickness information provided by well log data, is proposed. The proposed approach is based on moments of inertia (MOI) applied to the shale objects to determine their effective size. The MOI calculation is aimed to simplify the almost infinite complexity of shale bodies into summary size parameters that can be readily understood and calibrated to production parameters. A case study is presented for optimal well placement accounting for uncertainty in the shale barrier sizes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".