Dynamic Pore-Network Simulator for Modeling Buoyancy-Driven Migration During Depressurization of Oil-Saturated Systems
Bibliographic record
Abstract
Summary A number of vertically oriented heavy- and light-oil-depletion experiments have been conducted in recent years in an attempt to investigate the effect of gravitational forces on gas evolution during solution-gas drive. Although some experimental results indirectly suggest the occurrence of gas migration during these tests (especially at slow depletion rates), a major limitation of such an interpretation is the difficulty in visualizing the process in reservoir-rock samples. In contrast, experimental observations using transparent glass models have proved invaluable in this context and provide a sound physical basis for modeling gravitational gas migration in gas/oil systems. However, the experimental observations often exhibit somewhat contradictory trends—some studies showing dispersed gas migration, while others describe fingered, channelized flow—and, to date, there appears to have been little systematic effort toward modeling the wide range of behaviors seen in or inferred from laboratory tests. To this end, we present a new pore-network simulator that is capable of modeling the time-dependent migration of growing gas structures. Multiple pore-filling events are modeled dynamically with interface tracking allowing the full range of migratory behaviors to be reproduced, including braided migration (i.e., discontinuous flow of gas through narrow channels) and discontinuous dispersed flow. Simulation results are compared with experiments and are found to be in excellent agreement. Moreover, simulation results clearly show that a number of network and fluid parameters interact in a rather complex manner and, as a consequence, the competition between capillarity and buoyancy produces different gas-evolution patterns during pressure depletion. The implications of evolution regime on recovery from gas/oil systems undergoing depressurization are discussed extensively.
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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) | 0.000 | 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) | 0.000 | 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".