The Well-Wormhole Model of Cold Production of Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Cold Heavy Oil Production with Sand (CHOPS) is a non-thermal heavy oil recovery technique used primarily in the heavy oil belt in eastern Alberta and western Saskatchewan. Under CHOPS, typical recovery factors are between 5 and 15% with the average being under 10%. This leaves approximately 90% of the oil in the ground after the process becomes uneconomic, making CHOPS wells and fields, prime candidates for EOR and field optimization. CHOPS wells show an enhancement in production rates compared to conventional primary production, which is explained by the formation of high permeability channels known as wormholes. The formation of wormholes has been shown to exist in laboratory experiments as well as field experiments conducted with fluorescein dyes. The major mechanisms for CHOPS production are foamy oil flow, sand failure and sand production. Foamy oil flow aids in mobilizing sand and reservoir fluids leading to the formation of wormholes. Foamy oil behaviour cannot be effectively modeled by conventional PVT behaviour, leading to the use of a kinetic model, which can be easily implemented with the kinetic reaction features in CMG STARS. The sand is mobilized due to sand failure, determined by a minimum fluidization velocity. The individual wormholes will be modeled in CMG STARS using existing wellbore features. The ability to grow a wellbore dynamically is not built into STARS, leading to the creation of a Dynamic Wellbore Module. The module continuously restarts the STARS simulation runs and determines the growth criteria for wormhole growth. If the criterion is met, the wormhole is grown in the appropriate direction; otherwise the simulation is run again until the criterion is met. The proposed model incorporates the major factors in CHOPS production and shows an adequate to fit to model general production trends of typical CHOPS well. The model demonstrates there is a criterion for which wormhole growth occurs as wells as limits its extent of growth in a reservoir. The model can then be used for follow-up EOR processes such as cycle solvent injection as well as field optimization.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".