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A Laboratory Protocol to Evaluate Effective Permeability Alteration by Adding Surfactants in Fracturing Fluids

2024· article· en· W4398160381 on OpenAlexafffund
Lin Yuan, Mohammad Yousefi, Hassan Dehghanpour

Bibliographic record

VenueEnergy & Fuels · 2024
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsPetroleum engineeringHydraulic fracturingPermeability (electromagnetism)Protocol (science)ChemistryEnvironmental scienceGeologyChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Adding chemical additives such as surfactants and nanoparticles to the fracturing fluid is a common field practice for enhanced water and oil recovery. However, measuring the multiphase permeability of ultratight rocks is challenging, due to the extremely long time required to reach flow rate and pressure equilibration. This paper aims at understanding the effects of surfactant polarity on regained permeability of tight-rock samples, as functions of reservoir brine salinity and rock mineralogy, by utilizing a modified core-flooding device. We propose a laboratory protocol to screen different surfactants used in hydraulic fracturing operations to reduce interfacial tension (IFT) and alter wettability from oil-wet to water-wet conditions. The effects of different surfactants on the relative permeability shift of rock samples are also investigated. We used tight-core plugs from the Montney Formation and surfactants with different polarities for conducting experiments. First, we measured the physical properties of surfactant solutions, including surface tension, IFT, viscosity, and particle size. Then, we assessed the effectiveness of different surfactants for wettability alteration and quantify their adsorption on the rock surface. Next, we simulated the leak-off, soaking, and flowback processes under reservoir conditions using a modified core-flooding apparatus designed for ultralow permeability samples. The results show that, for Montney cores, although nonionic surfactants show higher adsorption, their regained liquid permeability ( k L ) is relatively higher, compared with anionic surfactants. The measured regained k L for nonionic and anionic surfactants were equal to the initial permeability before the leak-off stage, suggesting that the surfactant adsorption was not detrimental to the surfactant’s functionality in maintaining the rock permeability. This phenomenon suggests that adsorption of some surfactants may be reversible. However, all the anionic surfactants reduced the regained k L . The results show that if a reservoir is at subirreducible water saturation conditions, the leak-off of surfactant solutions may reduce the regained permeability by increasing the water saturation near the fracture face after leak-off and flowback processes. Combining the effects of IFT and wettability alterations in the dimensionless parameter of capillary number ( N ca ) shows that, above a threshold N ca value, the regained permeability remains unchanged, indicating no fracture-face damage.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.260
Teacher spread0.254 · 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 designBench or experimental
Domainnot available
GenreMethods

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
Published2024
Admission routes2
Has abstractyes

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