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Record W2586982273 · doi:10.2118/182655-ms

Equation-of-State Approach to Model Relative Permeability Including Hysteresis and Wettability Alteration

2017· article· en· W2586982273 on OpenAlexfundno aff
Saeid Khorsandi, Liwei Li, Russell T. Johns

Bibliographic record

VenueSPE Reservoir Simulation Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersAbu Dhabi National Oil CompanyCMG Reservoir Simulation FoundationPennsylvania State University
KeywordsRelative permeabilityWettingHysteresisPermeability (electromagnetism)Materials scienceChemistryComposite materialPhysicsPorosity

Abstract

fetched live from OpenAlex

Abstract Commercial compositional simulators commonly apply correlations or empirical relations based on tuned experimental data to calculate phase relative permeabilities. These relations cannot adequately capture effects of hysteresis, fluid compositional variations and rock wettability alteration. Furthermore, these relations require phases to be labeled, which is not accurate for complex miscible or near miscible displacements with multiple hydrocarbon phases. Therefore, these relations can be discontinuous for miscible and near-miscible displacements causing inaccuracies and numerical problems in simulation. This paper develops an equation-of-state (EoS) to model robustly and continuously the relative permeability as functions of phase saturations and distributions, fluid compositions, rock surface properties, and rock structure. Phases are not labelled; instead, the phases in each grid block are ordered based on their compositional similarity. Phase compositions and rock surface properties are used to calculate wettability and contact angles. The model is tuned to measured two-phase relative permeability curves with few tuning parameters and then used to predict relative permeability away from the measured experimental data. The model is applicable to all flow in porous media processes, but is especially important for low salinity polymer, surfactant, miscible gas and water-alternating-gas flooding. The results show excellent ability to match measured data, and to predict observed trends in hysteresis and oil saturation trapping including those from Land's model and for a wide range in wettability. The results also show that relative permeabilities are continuous at critical points and yields a physically correct numerical solution when incorporated within a compositional simulator (PennSim). The model has very few tuning parameters, and the parameters are directly related to physical properties of rock and fluid, which can be measured. The new model also offers the potential for incorporating results from CT-scans and pore-network models to determine some input parameters for the new EoS.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.127
GPT teacher head0.341
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2017
Admission routes1
Has abstractyes

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