Numerical Simulation of Nonequilibrium Foamy Oil for Cyclic Solvent Injection in Reservoirs after Cold Heavy Oil Production with Sand
Bibliographic record
Abstract
Summary Nonequilibrium foamy oil behavior and solvent transport are two important recovery mechanisms for cyclic solvent injection (CSI) processes in post-cold heavy oil production with sand (CHOPS) reservoirs. The nonequilibrium solvent exsolution and gas bubbles generated during the pressure depletion stage have the typical characteristics of foamy oil flow. In this paper, a field-scale post-CHOPS model is constructed and upscaled from a core model, which was calibrated against detailed experimental data involving various propane (C3H8)-based and carbon dioxide (CO2)-based solvent mixtures. The field model is upscaled from the core model to analyze the impacts of simulation scales, heterogeneous wormholes, and the operating schedules on foamy oil behavior of different solvent systems. Reaction kinetics are implemented to represent the nonequilibrium gas dissolution and exsolution for foamy oil flow. A fractal wormhole network is modeled. To analyze the impacts of pressure depletion strategies, single-stage pressure depletion involving three oil solvent systems, as well as two cycles of CSI production processes, are examined. Detailed sensitivity analyses involving different solvent compositions are discussed. The results illustrate that both C3H8-based and CO2-based solvents exhibit significant nonequilibrium foamy oil characteristics, enabling the oil viscosity to remain close to its value with dissolved solvent during the pressure depletion process. However, the amount of nonequilibrium foamy oil flow is strongly dependent on the pressure depletion rate: A faster depletion rate is beneficial for higher oil recovery. The core model results are more sensitive to the solvent types, whereas the field-scale simulations show comparable recovery performance for both C3H8-based and CO2-based solvents. This observation highlights the significance of domain size, time scale, and wormhole heterogeneities on the ensuing foamy oil behavior. Although several post-CHOPS models were developed in the past, detailed field-scale models that simulate nonequilibrium foamy oil kinetics in a realistic wormhole network are lacking. The simulation model developed here has been calibrated against detailed experimental measurements and upscaled from a core-scale model. Improving our understanding of solvent dissolution/exsolution would aid in the design of operating strategies (e.g., pressure depletion and solvent injection schemes) for enhanced solvent/oil mixing and transport.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".