SAGD Well Placement in Ultra-Defined Point Bar Deposit
Bibliographic record
Abstract
Abstract The creation and evolution of point bar systems is well understood in meandering river deposits. A large fraction of Athabasca oil sands deposits are ancient point bar systems characterized by bedded, sandstone-dominated strata with interbedded siltstone layers. The recovery process of choice for these deposits is the Steam-Assisted Gravity Drainage (SAGD) process due to the high viscosity of the oil, low solution-gas ratio, and often caps rock not sufficient to with stand injection pressures of Cyclic Steam Stimulation. However, because of the presence of siltstone interbeds, these reservoirs commonly have lateral and vertical lithological heterogeneity which interfere with the formation of uniform steam chambers along SAGD wellpairs. Other units in point bar deposits that impact SAGD chamber development within the formation include remnant channel succession and channel lag. The objective of this research is to construct a detailed three-dimensional point bar model to determine how its heterogeneity impacts SAGD performance. Here, the point bar model is based on the Lower Cretaceous Middle McMurray Formation in the Athabasca oil sands deposit in Alberta, Canada. Single SAGD wellpair submodels at different locations and orientations were extracted from the point bar model. The results of the reservoir models simulation suggest that attention must be paid to SAGD wellpair placement in point bar systems.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".