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Record W2930195251 · doi:10.2118/193909-ms

Modeling the Effect of Reaction Kinetics and Dispersion during Low-Salinity Waterflooding

2019· article· en· W2930195251 on OpenAlexfundno aff
Daulet Magzymov, Prakash Purswani, Zuleima T. Karpyn, Russell T. Johns

Bibliographic record

VenueSPE Reservoir Simulation Conference · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersEnergi SimulationJohn and Willie Leone Family Department of Energy and Mineral Engineering, College of Earth and Mineral Sciences, Pennsylvania State UniversityPennsylvania State University
KeywordsWettingSalinityDispersion (optics)KineticsChemical kineticsReaction rateChemistryChemical engineeringChemical reactionThermodynamicsMaterials scienceGeologyOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract The objective of this paper is to model low-salinity waterflooding by honoring physico-chemical complexity, namely, the effects of reaction kinetics and dispersion. Recent literature provides evidence for the potential of low-salinity water injection to improve oil recovery through wettability alteration through a complex network of reactions. However, there is lack of consensus with respect to the exact chemical species that are responsible for the alteration process. Therefore, in this study, we develop a a simplified binary multiphase reactive transport model that honors the general surface reaction for wettability alteration, but at the same time includes effects of reaction kinetics and dispersion in the governing equations. We lump the reactants, such as sodium, calcium, and petroleum acids, into two characteristic components based on their contribution to oil or water wetness. The wettability alteration process is modelled as a competition between these primary characteristic components to occupy the rock surface as described by reaction kinetics. The simulation results show a significant impact of reaction kinetics on the rate of wettability alteration compared to assuming instantaneous equilibrium. In the limiting case of a very slow reaction rate (Da ~ 0), low-salinity injection does not alter the wettability. Particularly, no effect on ultimate oil recovery is observed, regardless of the injected salinity level. For the case of an instantaneous reaction the ultimate oil recovery is sensitive to the injected fluid salinity. Moreover, during fast reactions (Da ~ 10-4), the wettability alteration front moves slower than the injected fluid front caused by excess salt in the solution that desorbs from the rock surface. The delay in wettability alteration is crucial to consider for an appropriate slug size design of low-salinity injection. Lastly, we observe that dispersion does not affect the ultimate oil recovery during wettability alteration as compared to reaction kinetics.

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: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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