Non-Equilibrium Reservoir Simulation of Solvent-Steam Processes, Based on Mass and Heat Transfer Inside a Pore
Bibliographic record
Abstract
Abstract Steam processes involving injection of a solvent with steam, such as CSS (Cyclic Steam Stimulation), SAGD (Steam-Assisted Gravity Drainage), and steam injection in isothermal mode are currently receiving a great deal of attention in Alberta. These combination processes are designed to reduce energy consumption and the emission of greenhouse gases over steam alone. Field results have been mixed and the original intent remains elusive. This paper addresses questions regarding the mechanism of solvent dissolution of bitumen and the expected improvement in oil recovery, if any, when a solvent is injected with steam. The unique aspect of this work is that instantaneous phase equilibrium is not assumed, as is customary in numerical simulators for solvent-steam applications. Nor is partial equilibrium assumed based on an empirical factor or concept. Rather, equilibrium is based on an analytical model of the dissolution and mobilization of a drop of bitumen inside a pore by solvent and heat. The results of this part of the study show that solvent requires at least three times as long to mobilize bitumen as by heat conduction. Such a delay is implicit in the nature of diffusion and dispersion of a solvent. Several boundary conditions are tested for a spherical drop. A new thermal compositional simulator with the single drop model was developed for this study and several thermal processes for solvent injection were investigated for non-equilibrium phase behaviour. The results were compared to the case of instantaneous equilibrium, confirming the reason for the lack of success with solvent injection. The results and extensions of this work will be of great interest in heavy oil production because they serve to explain the expected performance and frequent lack of success in solvent-steam injection. Use of the developed simulator would make it possible to determine whether solvent injection is a good choice in a given situation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".