The Well-Wormhole Model of CHOPS: History Match and Validation
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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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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 | 0.000 |
| 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 it