The Effect of Pressure-Decline Rate and Pressure Gradient on the Behavior of Solution-Gas Drive in Heavy Oil
Bibliographic record
Abstract
Summary Heavy-oil recovery under solution-gas drive is affected by several interacting factors including pressure-decline rate and pressure gradients. It has been suggested that a high pressure decline rate (dp/df) generates larger supersaturation and faster nucleation that leads to more-dispersed gas bubbles, while a high pressure gradient (∇p) increases the viscous forces acting on the gas phase, enhancing bubble break up and gas dispersion. Both effects lead to lower gas mobility, affecting oil recovery; however, the relative importance of each is not known. Finding this is important to develop mathematical models and to allow extrapolation of experimental results to field conditions, where the relative importance of these factors changes with time and space. Previous experimental studies were affected by a combination of the two effects. In this paper, we distinguish between the effect of the pressure-decline rate and pressure gradient on gas mobility and oil recovery by varying these independently. In the experimental work reported in this paper, change in confining pressure is used to create a change in pressure-decline rate, and a change in production rate is used to change the pressure gradient. Several depletion experiments at varying pressure-decline rates and production rates are reported here. At a constant pressure-decline rate, the recovery factor tripled when the flow rate was increased by one order of magnitude. Similar experiments were conducted when the pressure-decline rate was increased by one order of magnitude but the flow rate was kept constant. In this case, the recovery factor did not change significantly. The results of this study clearly indicate that the pressure gradient has a much greater effect on gas mobility and oil recovery than pressure-decline rate has. This paper presents the experimental results and their analysis, along with the implications of these findings on modeling of solution-gas drive in heavy oils.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".