Bibliographic record
Abstract
Abstract Cold heavy oil production exploits the mechanism of enhanced solution gas (or foamy-oil) drive to achieve an economic oil flow rate and ultimate recovery. Understanding the dynamics of foamy-oil drive and being able to simulate the process make it possible to evaluate the potential of cold production more realistically. A generic kinetic model based on known solution gas exsolution and evolution process has been proposed and tested. The sequence of gas exsolution in foamy-oil drive can be described by the following four major steps and corresponding (first-order) mechanisms: Supersaturation from pressure drawdownBubble nucleation under super-saturationBubble growth under molecular diffusionBubble coalescence from film drainage The effects of dispersed gas on the gas phase mobility has been modeled with a simple viscosity mixing rule by assigning a much higher viscosity to the dispersed gas component. The benefits of this approach is that the apparent low mobility of the gas phase in the foamy-oil drive process can be attributed to the viscosity change, instead of rate dependent relative permeability. Therefore, a “normal” relative permeability can be used for a wide range of rate conditions. The proposed dynamic model has been validated with the history matches of both the volume draw down and the pressure draw down laboratory test data. The validation reveals that one can use a set of consistent dynamic parameters to match the results of various tests under different draw down rates. The dynamic model has been implemented in a commercial reservoir simulator (CMG-STARS) with available options.
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 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".