Bibliographic record
Abstract
Abstract With the increase of demand for crude oil, heavy oil production becomes an increasingly important crude source for the energy industry. Non-thermal recovery methods, as a supplementary technique, hold the key for exploitation of these depleted or nearly depleted thin heavy oil reservoirs1. This paper focuses on gas recharging processes study for post-cold heavy oil production. Although there is a diverse research effort on improving oil recovery ongoing for years, most of the experiments to date were conducted using small-scale core samples. It is always difficult at the laboratory level to conduct experiments at real representative field conditions, but larger - scale experiment can provide more realistic conditions15 compared to the reservoir scale. This is very helpful in understanding the prevailing mechanisms which may occur in the reservoir. To the best of our knowledge, the longest cores2-14 which have ever been used in the past are two 18m in length cores (glass beads and sand packed separately) tested by Goodarzi and Kantzas3,5 and Shi and Kantzas4. This study involves three post-cold heavy oil production processes: CH4 recharging and depletion, CO2 and C3H8 huff-puff, and C3H8 flood. To generate data that is a step closer to reservoir scale, the two 18m long cores mentioned above and one 1.5m sand-packed core were used for testing. The mechanisms of each process were investigated. NMR and GC techniques were applied to help better understand production mechanisms. CT-scan was also used as the tool to monitor the core saturations before and after each process on 1.5m core. Density profiles as a function of core length were investigated. An attempt was made to use the results from short-core tests to explain the long-core results. The effects of core length and permeability on oil recovery were also compared and discussed. Multiple parameters were monitored and analyzed, such as pressure and temperature variations along the core, injected and produced gas composition, produced oil viscosity and water cut, core density before and after each process etc.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".