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Record W2323495553 · doi:10.2118/179535-ms

Application of PC-SAFT Equation of State for CO2 Minimum Miscibility Pressure Prediction in Nanopores

2016· article· en· W2323495553 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSPE Improved Oil Recovery Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanoporeMiscibilityEquation of stateSurface tensionEnhanced oil recoveryThermodynamicsPhase (matter)Materials sciencePetroleum engineeringTight oilChemistryOil shalePolymerNanotechnologyGeologyComposite materialPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.227
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it