Micro-Model Experiments and Network Modelling of Bubble Growth in Foamy Oil Flow
Bibliographic record
Abstract
Abstract The subject of bubble nucleation, growth, and movement in some of the solution-gas-drive heavy oil reservoirs (foamy oil) has been examined in the last few years. The behaviour of foamy oil reservoirs is different from conventional oil reservoirs in terms of pressure decline, oil recovery, and production gas-oil ratio. We studied the mechanism of bubble growth in an etched glass micro-model of porous media with concomitant network modelling. Bubble growth from sizes smaller than a pore to the sizes covering several pores is investigated. Movements of the gas-liquid interfaces at different stages of growth are examined. The force balances on bubbles at different stages of expansion is studied for two cases where the size of a bubble is smaller than the pore body and where bubble has grown enough to make contact with pore walls and invade the adjacent pore throats. Finally, a network model of porous media (pore level simulator), which includes viscous, capillary and pressure forces is developed to follow bubble growth as a result of pressure depletion in a porous medium. Effects of oil viscosity, pressure depletion rate, wettability, and capillarity on the size of a solitary bubble are investigated. Increased oil viscosity hinders bubble growth and bubbles become unstable and break-up after limited expansion. Introduction Some solution-gas-drive heavy oil reservoirs in Canada, Venezuela, China and Oman have demonstrated unusually high primary production rates, high primary oil recovery factors (>10%), low producing gas-oil ratio, and low reservoir-pressure-decline(1,2). To explain these anomalies, three hypotheses have been advanced: geomechanical effects (3), special fluid properties (4), and unusual flow dependent properties of the oil and gas (5). For the last category, it is believed that local pore-level processes involving the bubble formation, growth, breakup, and coalescence account for the unusual behaviour in solution-gas- drive heavy oil reservoirs. An interplay of bubble formation, growth, coalescence and break-up determines the critical gas saturation and relative mobility of the gas phase. Knowledge of these pore level events is necessary to derive physically meaningful rate expressions for modeling fluid flow on a macroscopic scale such as reservoir simulators or in mechanistic models. Rock properties such as porosity and permeability, fluid properties such as viscosity and composition, and reservoir rock/fluid characterization such as spreading coefficient and wettability affect the local events for bubbles. The topology and morphology of a porous medium affect the mechanism of bubble growth. Therefore, a bubble would expand differently in a porous structure compare to in an open system. Dominguez et al. (6) have demonstrated that the shape and size of bubbles in a network of porous medium and in a Hele-shaw cell are very different. This study is related to describing foamy oil flows when bubbles grow at pore-size level. Network models are simplified mathematical representation of real porous material. The objective of a network model is to provide a reasonable idealization of the complex geometry of the real porous medium at microscopic scale or pore-level, so that related fluid flow and interface movement can be treated mathematically at a manageable level of complexity.
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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".