Simulation Analysis of Steam-Based EOR Using MultiObjects Grosmont Models
Bibliographic record
Abstract
Abstract Carbonates contain more than 50% of the world's known hydrocarbon resources. Although natural fractures are a common feature in carbonates and they can improve primary production from tight matrix carbonates, they pose a unique challenge for enhanced oil recovery (EOR) from heavy oil and bitumen (HOB) carbonates. The current focus on ‘energy mix’ to sustain the world energy demand has once again put HOB carbonates in global spotlight. Among the known HOB carbonate reservoirs globally, the Grosmont carbonate in Northern Alberta contains the largest original in-place HOB (~406.5 billion barrels). However, the Grosmont carbonate poses the greatest development challenge of all other known HOB carbonates due to its petrophysical complexity. We recently published our progress towards developing a methodology for characterizing the Grosmont carbonate that is suitable for direct reservoir simulation. The objective of the current paper is to assess the performances of different steam-based EOR recovery technologies using multiobject reservoir models of the Grosmont. Our results show that simulations on appropriate reservoir models representative of the most hydraulically active objects produce a good account of heat and fluid flow in complex carbonates. Cyclic steam stimulation (CSS) in this paper describes steam injection below fracture pressure and can therefore be related to what has recently been presented in the literature as cyclic single well steam-assisted gravity drainage.
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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".