Development of a generalized Richards equation for predicting spontaneous imbibition of highly shear-thinning liquids in gas recovery applications
Bibliographic record
Abstract
A new generalized Richards equation (GRE) valid for highly shear-thinning liquids obeying the power-law model is developed using the concept of the effective viscosity. The mathematical model developed this way is validated against experimental data reported recently for one-dimensional spontaneous imbibition of two pusher liquids by a tight sandstone. The GRE model was then used for evaluating the applicability of shear-thinning liquids for enhanced gas recovery. For a homogenous tight sandstone, it is shown that shear-thinning can dramatically shorten the time needed for the gas recovery to reach equilibrium. Based on the obtained numerical results, the mass of the gas recovered using spontaneous imbibition is increased if use is made of highly shear-thinning liquids. At prolonged times, however, it is predicted that gas recovery might slightly drop below its Newtonian counterpart even for highly shear-thinning fluids. The effect was attributed to the fact that, in spontaneous imbibition, the viscosity of power-law fluids increases with time and can eventually become larger than its Newtonian counterpart. For a two-layered non-homogeneous system, numerical results suggest that depending on the microstructure of the two layers, the liquid mass uptake can be smaller than that of the homogenous case. It is predicted that if the liquid is sufficiently shear-thinning, gas recovery can reach levels much above the homogeneous case.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".