Bibliographic record
Abstract
Abstract Some solution-gas-drive heavy oil reservoirs (foamy oil reservoirs) in Canada, Venezuela, China and Oman have demonstrated unusually high primary oil recovery factor (>10%), low production gas-oil ratio, low reservoir pressure decline, and high oil production rate. One of the hypotheses to explain these characteristics is that bubbles released as a result of pressure depletion, break up into smaller bubbles. Small bubbles have lower tendency to move. In foamy oil reservoirs, the driving force to produce oil is the force exerted by the bubbles expansions. Therefore, if a mechanism tends to keep the bubbles in the reservoir, more oil can be produced. In this study a pore-scale model of porous media was developed to investigate the effect of different parameters such as pressure gradient, oil viscosity, interfacial tension, contact angles or wettability, and pore aspect ratio on the break-up of a solitary bubble. An important result is that, bubble break-up is more likely to happen in network model runs with high pressure gradients. Also the model shows that bubble break-up time increases with increasing oil viscosity. Therefore, it is unlikely that the large number of small bubbles in foamy oil flow created by bubble break-up. Other mechanisms such as hindered bubble coalescence, and ramp out bubble nucleation might be the important mechanisms. Introduction Some solution-gas-drive heavy oil reservoirs in Canada, Venezuela, China and Oman have experienced unusually high primary production rates, high primary oil recovery factors (>10%), low producing gas oil ratio, and low reservoir pressure decline(1). To explain the unusual behaviour, three hypotheses have been advanced: geomechanical effects(2); special fluid properties(3) and nusual flow dependent properties of oil and gas(4). For the last category, it is believed that the low mobility of gas accounts for the unusual behaviour in solution-gasdrive heavy oil reservoirs. One of the mechanisms that contribute in keeping the gas dispersed in foamy oil and its mobility low could be gas bubble break-up. An interplay of bubble nucleation, expansion, coalescence and break-up determines the micro-structure and dynamics of internal gas-oil dispersion. Knowledge of these pore level events is necessary to derive physically meaningful rate expressions for these processes for further implementation in a macroscopic scale fluid flow model such as reservoir simulators or in mechanistic models. As yet, a reservoir simulator that includes the above mechanisms has not been developed. We have used a network model of porous media to investigate the mechanism of bubble break-up at pore level. Network models are simplified mathematical representation of the 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, so that related fluid flow and interface movement can be treated mathematically at a manageable level of complexity. In this work, a previously developed micro-flow simulator(5) (that includes viscous and capillary forces) was modified to include the bubble rupture mechanism (break-up). The intent is to be able to predict conditions for bubble break-up at system parameters such as viscosity and pressure gradient.
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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) | 0.003 | 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 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".