A New Modeling Approach to Optimize Methane-Propane Injection in a Field After CHOPS
Bibliographic record
Abstract
Abstract Although cold heavy oil production with sands (CHOPS) is an economically attractive method, ultimate recovery does not exceed 10%. Cyclic solvent injection (CSI) has been under consideration as a follow-up EOR application in the industry. This method targets extracting large amounts of remaining oil in the matrix by solvent diffusion, taking advantage of its high contact area with wormholes. Methane and propane are two potential solvents to be used in this practice. Methane is preferred due to its availability and stronger foaming characteristics while propane has lower foaming but better mixing capability. A far-reaching -core to field scale- study was conducted in this paper to test out the potential of pure methane and its mixture with propane as prospective CSI solvents. A 1.5 m long and 5 cm diameter sand-pack was initially saturated with brine. Then, live oil (saturated with methane and methane-propane mixture at different ratios) was transferred to drain the brine out. Typical field scale pressure decline rates were applied and pressure was monitored through eight equally spaced transducers placed onto the core holder. The experimental data from core and PVT tests were matched to a core scale simulation model to obtain K-values. These data were carried to a field scale model to analyze the CSI performance for methane and methane-propane mixture. In field scale modeling, 15-well data from CHOPS field from Alberta, Canada were history matched and 6-cycle CSI performances were followed as post-CHOPS with different well patterns (central, peripheral, all-wells). As a result of these experiments, methane showed about 14% oil recovery, but with additional CO2 huff ‘n’ puff around 15% recovery was added, totaling 29% recovery. Methane-propane mixture resulted in a lower oil recovery, which was about 5%, due to decreased foamy effect. Valid core-scale simulation was completed by tuning K-values and considering non-equilibrium or equilibrium impact depending on solvent type, showing mostly less than 5% error. In field scale modeling, central and peripheral well patterns yielded oil recoveries consistent with the experiments while all-well huff ‘n’ puff- type pattern showed a slightly higher value.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".