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Record W4408266508 · doi:10.2118/223984-ms

Using Visual Aids to Clarify the Matrix-Fracture Fluid Interaction in Enhanced Unconventional Oil and Gas Recovery with Chemical Additives

2025· article· en· W4408266508 on OpenAlexaff
Lixing Lin, Tayfun Babadagli, Huazhou Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPetroleum engineeringMatrix (chemical analysis)Unconventional oilFracture (geology)Fossil fuelComputer scienceChemistryMaterials scienceGeologyChromatographyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Matrix-fracture fluid interactions occur during both the fracturing and recovery stages in naturally and hydraulically fractured oil and gas reservoirs. Understanding the physics and the mechanisms of these interactions (co- or counter-current manners) is vital for selecting the proper chemicals as fracturing or EOR fluid additives. While traditional core experiments often treat the core as a black box, microscopic visualization offers direct observations of key phenomena such as interfacial instability, wettability alteration, and emulsification, particularly in counter-current imbibition processes. In this study, co- and counter-current imbibition experiments were visualized using Hele-Shaw cells and glass etched micromodels. Selected chemicals were tested to evaluate their impact on the imbibition behaviour under different boundary conditions and forces. A 17.1 cP crude oil sample and air were used to saturate these models to mimic oil and gas reservoirs, respectively. To simulate counter-current imbibition, a Hele-Shaw cell sealed on all sides except the bottom was placed vertically in a transparent container filled with water or chemical solutions. Condition of co-current imbibition was developed when two ends of the Hele-Shaw cell were open. Both vertical and horizontal experiments were conducted with two ends being open. In the gas recovery experiments using a micromodel, its one side was in contact with water or a chemical solution while the opposite side was open for outflow. In addition, another micromodel was employed to conduct oil recovery experiments for further validating the findings from core and Hele-Shaw experiments. Results revealed that the absence of chemicals resulted in more finger channels due to increased interface instability at high interfacial tension (IFT). Conversely, the introduction of chemical additives reduced IFT, promoted wettability alteration toward water-wet conditions, and improved displacement efficiency. The nonionic surfactant Tween 80, and organic alkali ethanolamine (ETA) and high-pH sodium metaborate (NaBO2) demonstrated enhanced imbibition rates and more uniform displacement fronts in counter-current imbibition, making them promising EOR agents. While the anionic surfactant O342 exhibited a slower oil recovery rate during the initial stages, its ability to alter wettability could contribute to improved final recovery. These findings are consistent with observations from our previous core experiments. Chemical additives also influenced the displacement geometry, producing shorter imbibition lengths but wider swept areas compared to water alone. This enhanced areal displacement efficiency and recovery factors. In horizontal counter-current experiments, chemical additives shifted flow dynamics toward counter-current dominance in the absence of gravity effects. In the gas recovery experiments, O342 effectively reduced water invasion in gas-saturated matrix at the early stage with reduced swept area and high residual gas retention. This suggested its potential for mitigating water-blocking effects in hydraulic fracturing operations. Oil recovery experiments conducted in a micromodel further confirmed the effectiveness of Tween 80 in suppressing viscous fingering and improving sweep efficiency. Additionally, the presence of Tween 80 led to the formation of emulsions characterized by small oil droplets. The results would be useful for both theoreticians, who develop new mathematical models and simulators to model the imbibition processes, and practitioners who select proper chemicals in field applications.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.007
GPT teacher head0.280
Teacher spread0.274 · 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 designNot applicable
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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