Well/Wormhole Model of Cold Heavy-Oil Production With Sand
Bibliographic record
Abstract
Summary Cold heavy-oil production with sand (CHOPS) is a nonthermal heavy-oil-recovery technique used primarily in the heavy-oil belt in eastern Alberta, Canada, and western Saskatchewan, Canada. Under CHOPS, typical recovery factors are between 5 and 15%, with the average being less than 10%. This leaves approximately 90% of the oil in the ground after the process becomes uneconomic, making CHOPS wells and fields prime candidates for enhanced-oil-recovery (EOR) follow-up process field optimization. CHOPS wells show an enhancement in production rates compared with 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 (or fluidization), and sand production. Foamy oil flow aids in mobilizing sand and reservoir fluids, leading to the formation of wormholes. Foamy oil behavior cannot be effectively modeled by conventional pressure/volume/temperature (PVT) behavior. Here, a new well/wormhole model for CHOPS is proposed. The well/wormhole model uses a kinetic model to deal with foamy oil behavior, and sand is mobilized because of sand failure determined by a minimum fluidization velocity. The individual wormholes are modeled in a simulator as an extension of a production well. The model grows the well/wormhole dynamically within the reservoir according to a growth criterion set by the fluidization velocity of sand along the existing well/wormhole. If the growth criterion is satisfied, the wormhole extends in the appropriate direction; otherwise, production continues from the existing well/wormhole until the criterion is met. The proposed model incorporates sand production and reproduces the general production behavior of a typical CHOPS well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".