The Well-Wormhole Model of CHOPS: History Match and Validation
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 western Alberta and eastern Saskatchewan. Under CHOPS, typical recovery factors are between 5 and 15% with average ~10%. This leaves ~90% of the oil in the ground after the process becomes uneconomic. CHOPS exhibits an enhancement in production rates compared to conventional primary production, which is explained by formation of high permeability channels known as wormholes. The formation of wormholes has been demonstrated to occur in both laboratory experiments and field tracer studies. The ability to model growth of wormholes does not currently exist in commercial reservoir simulators. Here, wormholes are modelled as multi-lateral wells, which grow dynamically in the reservoir, using existing wellbore features. A module was coupled to CMG STARS™ to dynamically grow wormholes in the reservoir taking foamy oil flow, sand failure, and sand production into account. Here, we present on the results of history matches against field data to tune model parameters. The history-matched model reasonably predicts production trends of field CHOPS operations. The results provide a methodology to model CHOPS and predict under uncertainty where the wormholes will tend to grow into the reservoir. This provides a tool for placing new wells in the reservoir that will most likely not be in direct contact with existing wormholes. Multiple realizations of the reservoir can be used to mark the region of the reservoir that undergoes wormhole formation. The model can then be used for follow-up EOR processes such as cycle solvent injection as well as field scale 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 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".