Methane Pressure-Cycling Process With Horizontal Wells for Thin Heavy-Oil Reservoirs
Bibliographic record
Abstract
Summary The methane pressure-cycling (MPC) process is an enhanced-oil-recovery (EOR) scheme intended for application in some heavy-oil reservoirs after termination of either primary or waterflood production. The essence of the process is the restoration of the solution-gas-drive mechanism. The restoration is accomplished by re-injecting an appropriate amount of solution gas (mainly methane) and then repressuring the gas back into solution by injecting water until approximate original reservoir pressure is reached. This, aside from the replacement of produced oil by water, recreates the primary-production conditions. This novel recovery technique is being developed to target the considerable portion of heavy-oil resources located in thin reservoirs. Primary and secondary methods have managed to recover at best 10% of the initial oil in place (IOIP). Heat losses to overburden and underburden or bottomwater zones make thermal methods unsuitable for thin reservoirs. Sandpack-flood tests in 30.5-cm (length)×5.0-cm (diameter) sandpacks were carried out for oils with a range of dead-oil viscosities from 1700 to 5400 mPa·s. The results showed that the pressure-cycling process could create a favorable condition for recharged gas to contact the remaining oil in reservoirs. This restores the situation whereby substantial amounts of gas are in solution for further "primary" production. The effects on the efficiency of the MPC process of cycle termination strategy, oil viscosity, and mobile-water saturation were investigated. Simulations were conducted to investigate the MPC process in three heavy-oil reservoirs in Saskatchewan, Canada. The effects on the process of infill wells, oil viscosity, gas-injection rate, and the presence of wormholes in reservoirs were studied.
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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 | 0.000 |
| 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) | 0.001 | 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 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".