Investigation of gas condensate drop‐out effect on gas relative permeability by Lattice Boltzmann modelling
Bibliographic record
Abstract
Abstract In a gas condensate reservoir, a drastic pressure drop in the vicinity of the wellbore makes it subject to gas condensate drop‐out. This phenomenon can adversely affect the productivity of the well and reduce gas recovery. The objective of this paper is to conduct a two‐phase fluid flow simulation on two‐dimensional porous media to understand the effect of the gas condensate drop‐out on the gas relative permeability values. In order to do so, lattice Boltzmann (LB) modelling was applied as a computational fluid dynamics (CFD) approach to perform the simulations in homogeneous and heterogeneous porous structures. The developed model was constrained by periodic boundary conditions at inlet and outlet and bounce‐back boundary condition at fluid‐solid interfaces. It was shown that the model can appropriately monitor formation and movement of the condensate droplets as a result of the pressure drop as well as blockages due to the entrance of the droplets into the throats. A consistent decrease in gas relative permeability values with condensate saturation was observed. It was also indicated that the condensate droplets become mobile at higher critical saturations in the heterogeneous system due to the dominance of capillary forces over viscous forces in the less permeable areas. Such dominance results in more severe blockages in the heterogeneous systems, as the simulation results confirmed.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".