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Record W2991297297 · doi:10.1007/s11242-019-01368-x

Detailed Assessment of Compositional and Interfacial Tension Effects on the Fluid Behaviour During Immiscible and Near-Miscible CO2 Continuous and WAG Displacements

2019· article· en· W2991297297 on OpenAlexfundno aff
G. Wang, Gillian Elizabeth Pickup, K. S. Sorbie, Eric Mackay

Bibliographic record

VenueTransport in Porous Media · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsnot available
FundersEnergi SimulationCMG Reservoir Simulation FoundationHeriot-Watt University
KeywordsViscous fingeringEnhanced oil recoveryPorous mediumSurface tensionMultiphase flowRelative permeabilityMechanicsFlow (mathematics)Work (physics)Materials scienceWettingTwo-phase flowPetroleum engineeringPermeability (electromagnetism)PorosityThermodynamicsGeologyGeotechnical engineeringChemistryPhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract This study seeks to improve numerical simulations of the key physics occurring in CO 2 enhanced oil recovery (CO 2 -EOR) processes, with a particular focus on the transition from immiscible to miscible displacements. In the previous work, we have investigated interactions between compositional effects and the underlying heterogeneities of the flow field in near-miscible floods (Wang et al. in Transp Porous Media 129(3):743–759, 2019a). In this current study, we have further analysed the effects of reduction in interfacial tension (IFT) on the flow behaviour, as motivated by the study on the film-flow mechanism previously presented by Sorbie and van Dijke (SPE improved oil recovery symposium, Society of Petroleum Engineers, 2010). We identify two clear mechanisms of oil recovery that may occur in near-miscible CO 2 (or other gas) injection processes, which we denote, M CE , as oil stripping or conventional compositional effects, and M IFT as lower IFT oil film-flow effects. The latter M IFT effects are described by an enhanced hydrocarbon relative permeability in the near-miscible three-phase relative permeabilities (3PRP). Various combinations between the M CE and M IFT mechanisms were tested by numerical simulations to evaluate the impact of each mechanism on the flow behaviour, i.e. their separate and joint effects on quantities such as the local oil displacement efficiency, phase flow vectors and the ultimate oil recovery. When acting in combination, the oil stripping and IFT effects can greatly improve the local displacement performance even when viscous fingering flow occurs. Viscous fingering is well known to lead to bypassed oil in the “non-preferential” flow paths between the main fingers. We show that the remaining oil in these non-preferential flow paths (i.e. bypassed oil) can be efficiently recovered by the combined M CE and M IFT mechanisms, but only with the application of water alternating gas (WAG). In contrast to oil stripping effects, the IFT effect is not dependent on continuous contact between oil and CO 2 . Instead, the remaining oil is mobilized by gas as the IFT is reduced and can be efficiently produced by subsequent water injection. This M IFT mechanism has much less impact in cases with continuous CO 2 injection compared to its efficiency in WAG. This is because during continuous injection, gas fingers are dominant in the preferential flow paths, and therefore the local displacement efficiency is very good, but only in these preferential routes. On the other hand, WAG is able to make full use of the IFT effects because of its relatively stable displacing front, which allows the M IFT mechanism to contribute. In this study, the effects of using different three-phase relative permeability methods were investigated and, as expected, different methods yielded different results. However, an important observation is that when IFT effects ( M IFT ) were included, there was much less difference in the final oil recovery using the different 3PRP models; our analysis shows why this is the case.

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.

How this classification was reachedexpand

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.628

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)0.0010.000

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 head0.006
GPT teacher head0.235
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2019
Admission routes1
Has abstractyes

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