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Record W2922786992 · doi:10.2523/iptc-19066-ms

Multiscale Water Ion Interactions at Interfaces for Enhanced Understanding of SmartWater Flooding in Carbonates

2019· article· en· W2922786992 on OpenAlexfundno aff
Subhash Ayirala, Salah Saleh, Sultan Enezi, Ali Yousef

Bibliographic record

VenueInternational Petroleum Technology Conference · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersUniversity of California, Santa BarbaraUniversity of Alberta
KeywordsBrineSalinityEnhanced oil recoveryIonChemical engineeringRheologyCoalescence (physics)Scanning electron microscopeChemistryMaterials scienceMineralogyGeologyPetroleum engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, we summarize and discuss the data reported from a series of multiscale experiments to explore the interactions of salinity and water ions at both fluid-fluid and rock-fluid interfaces to understand the pore scale mechanisms responsible for oil recovery in SmartWater flooding. These experimental data on various crude oil/brine/carbonate and crude oil-brine physicochemical changes/effects at elevated temperatures were obtained using a variety of static and dynamic techniques at different scales ranging from atomic-molecular-macroscopic scales. The techniques include surface force apparatus (SFA), cryo-broad ion beam scanning electron microscope (BIB-SEM), zeta potentials, microscope based oil liberation, interfacial shear rheology, and integrated thin film drainage apparatus (ITFDA). The salinities of brines were varied from zero salinity deionized (DI) water to higher salinity injection water in addition to changing the individual ion compositions. The integration of results obtained from these different multiscale experiments showed that both salinity and individual water ions play a major role not only to determine the oil release from rock surface due to the interactions at rock-fluids interface, but also to impact released oil ganglion dynamics for efficient oil mobilization through the interactions at fluid-fluid interface. The key findings can be summarized as the following: (1) At zero salinity, unfavorably much higher adhesion as well as stronger rigid films to adversely impact crude oil droplets coalescence were observed at rock-fluids, and fluid-fluid interfaces, respectively; (2) An optimal lower salinity containing sufficient amount of sulfate ions is necessary to cause nano-scale ion exchange at the rock-fluids interface that changes the surface charge/potential to favorably alter adhesion and microscopic contact angles for efficient oil release from rock surface; (3) An adequate salinity containing higher amounts of magnesium and calcium ions is desired to form less rigid films at the fluid-fluid interface that promote the coalescence of released oil ganglia for effective mobilization. Based on these novel findings, SmartWater can be defined as a tailored water containing certain salinity and selective composition of three key ions including sulfates, magnesium, and calcium. It must contain lower amounts of monovalent ions and should have the right balance of the three key ions to enable favorable interactions at both fluid-fluid and rock-fluids interfaces and result in faster as well as higher oil recoveries in carbonates. The analysis on multiscale water ion interactions at both the interfaces performed in this study also sheds the important learning point that not every low salinity water can become a SmartWater for carbonates. These new learnings and the novel knowledge gained would provide useful practical guidelines on how to design optimal injection water chemistries for SmartWater flooding projects in the field.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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 designBench or experimental
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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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