3D Geomodelling and Flow Simulation of the Late Devonian Bakken Formation in South-Central Saskatchewan - Case Study: Smiley Buffalo Heavy Oil Waterflood
Bibliographic record
Abstract
Abstract A sound geological model, a consistent geocellular model, and flow simulation are all critical components to successful optimization of a heavy oil waterflood project. Despite the availability of a sound geologic model, high quality seismic and abundant core, log, and production data, optimization of the waterflood project at the Smiley Buffalo field in south-central Saskatchewan held many challenges The primary reservoir at Smiley is the middle Bakken sandstone which was deposited as offshore sand ridges in late Devonian to early Mississippian time. The reservoir structure has been subjected to post-depositional solutioning of the underlying Torquay Formation and karstification which causes reservoir breaks, fracture networks, and irregularities in the saturation functions. These features have important effects on reservoir performance. The geologic model and seismic information were used to construct a consistent structural model. A facies classification model was built with fuzzy logic which used the core and log data as well as the geologic model. Sequential indicator simulation was used to populate the structural model with facies information, and sequential Gaussian simulation was used to populate the petrophysical properties. Representative models were selected for upscaling and flow simulation and subsequent well location selection.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".