Modelling of Cold Heavy-Oil Production With Sand For Subsequent Thermal/Solvent Injection Applications
Bibliographic record
Abstract
Summary Although proved beneficial and economic for thin reservoirs, the cold heavy-oil production with sand (CHOPS) method has several limitations. The sand produced during CHOPS changes the geomechanical and petrophysical properties continuously and results in open channels in the reservoir known as wormholes. Also, the CHOPS method results in a low oil recovery (8–10% original oil in place). This entails a follow-up enhanced-oil-recovery (EOR) process, which is always an option for further exploitation, referred to as post-CHOPS. Assessment of such a process through numerical simulation, as the most inexpensive yet most powerful tool, necessitates a comprehensive modelling approach to capture its dynamic physical nature. Only through such a realistic model can one obtain reasonably reliable reservoir characteristics after CHOPS that are critically important to assess post-CHOPS applications. To this end, we implemented a fractal pattern of different kinds by use of a diffusion-limited aggregation (DLA) algorithm as wormhole domain with a partial dual-porosity approach and a step-by-step simulation technique, taking advantage of a simple mathematical model to integrate the sand-production data with fractal patterns. The wormhole network is assumed to grow with more sand production, respecting geological conditions and well perforation. Moreover, its effective properties and the contained fluid can be controlled along its length and pattern at different steps. Such an option is of great assistance in the history-matching process. The model was validated successfully with available Alberta field data. As a preliminary step to post-CHOPS, several thermal, solvent, and hybrid combinations of both scenarios were considered. The proposed method for CHOPS modelling is a useful approach to initiate a quick post-CHOPS study in practice if sand production history is provided. One may also take advantage of its compatibility with any black-oil, compositional, or thermal simulators.
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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 | 0.002 | 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 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".