Impact of fluid property shift and capillarity on the recovery mechanisms of CO<sub>2</sub> injection in tight oil reservoirs
Bibliographic record
Abstract
Abstract The phase equilibria with the confinement effect could shift in nano‐pores, which could have a great impact on the recovery mechanisms of CO2 injection in tight oil reservoirs; this has not been systematically studied. In this paper, the confinement effect with property shift and capillarity effect is introduced into the flash calculation of confined fluids. The Soave modification of the Redlich–Kwong equation of state is extended by the molecular‐wall collision parameter to describe the shifted pressure–volume–temperature properties of confined fluid, and the Young–Laplace equation is applied to evaluate the capillary pressure. This developed model could effectively be applied for phase equilibrium calculation in tight porous media because of the verification of experimental results. A binary mixture is investigated to study the different effect of capillary pressure and property shift on phase equilibria. Subsequently, a typical hydrocarbon fluid from Middle Bakken tight oil reservoirs is studied with CO2 injection. Results illustrate that the confinement effect could play an increasingly important part in the phase equilibrium state. The CO2 solubility and mass transfer driving force in tiny pores would be greater than those in large pores under the same conditions. The gas phase saturation would be smaller with the same compositions, which could extend the single‐phase region of fluid flow in porous media. Furthermore, bubble‐point pressure, the minimum miscible pressure of CO2/hydrocarbon, and the viscosity of tight oil dissolved with CO2 both decrease with the pore size, which has a good influence on tight oil recovery. In general, the confinement effect could effectively reinforce the recovery mechanisms of CO2 injection, which is conducive to the enhancement of tight oil recovery. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.001 |
| 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".