Application of PC-SAFT Equation of State for CO2 Minimum Miscibility Pressure Prediction in Nanopores
Bibliographic record
Abstract
Abstract CO2 miscible injection is generally one of the most efficient enhanced oil recovery (EOR) methods and widely used in the conventional oil reservoirs. The applicability of CO2 EOR technology for unlocking the resources from unconventional tight and shale formations and the mechanisms of miscible flooding in these reservoirs still remain unclear. An important parameter used to evaluate the feasibility of CO2 miscible flooding is the minimum miscibility pressure (MMP). Even though experimental approaches, empirical correlations and theoretical methods have performed well in measuring or predicting MMP between CO2 and crude oil in conventional reservoirs, they may not be suitable for unconventional formations as phase behavior and MMP can be significantly affected by confinement effect in small pores (e.g., nanopores) in such formations. In this study, a new MMP prediction model based on the modified Parachor Model associated with the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) is developed to determine CO2 MMP both in the bulk phase and nanopores. The Parachor Model is modified to account for the confinement effect of nanopore walls on the equilibrium interfacial tension (IFT). The Equilibrium IFT reduction in nanopores is related to a temperature-dependent and slit pore width-dependent modification term. The parameters of the new Parachor Model are determined by matching the vapor-liquid surface tension values for CH4, C2H6, C3H8, n-C4H10, and n-C8H18 in nanopores, respectively. The prediction ability of the new model is verified by comparing the predicted MMP in the bulk phase with the results of other theoretical approaches and slim-tube experimental data. Finally, the new model is applied to estimate the MMP between Bakken oil and CO2 stream. The effect of temperature, slit pore width, and impure components in the injected CO2 on the MMP are also studied. The newly developed model successfully reproduces MMP in bulk phase as compared with both other methods and experimental data. The overall average absolute relative deviation (AARD) for MMP is within 8 %. The calculated equilibrium IFT for liquid-vapor phase has a good agreement with molecular simulation results. For Bakken oil-CO2 system, if the slit pore width is larger than 10 nm, MMP is independent on pore width; otherwise, it decreases significantly with the decrease of the pore width. If pore width decreases to 3 nm, 67.5 % decrease in the IFT is observed and 23.5% reduction is achieved for MMP between Bakken oil and CO2 stream, indicating that it is easier to reach miscibility in nanopores, and CO2 miscible flooding might be a promising enhanced oil recovery (EOR) technology for tight oil and shale oil reservoirs. Furthermore, MMP increases with an increase of temperature in bulk phase, whereas IFT and MMP decrease with an increase of temperature in nanopores.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".