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Record W4412452784 · doi:10.2118/223984-pa

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

2025· article· en· W4412452784 on OpenAlexaff
Lixing Lin, Tayfun Babadagli, Huazhou Li

Bibliographic record

VenueSPE Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFracture (geology)Matrix (chemical analysis)Petroleum engineeringGeologyMaterials scienceGeotechnical engineeringComposite material

Abstract

fetched live from OpenAlex

Summary Matrix-fracture fluid interactions occur during both the fracturing and recovery stages in naturally and hydraulically fractured reservoirs. Understanding the physics and the mechanisms of these interactions (co- or countercurrent manners) is vital for selecting the proper chemicals as fracturing or enhanced oil recovery (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 countercurrent imbibition processes. In this study, co- and countercurrent imbibition experiments were visualized using Hele-Shaw cells and glass-etched micromodels. Selected chemicals were tested to evaluate their impact on the imbibition behavior under different boundary conditions and forces. A 17.1 cp crude oil sample was used to saturate these models. To simulate countercurrent 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 cocurrent 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 addition, a micromodel was used to conduct oil recovery experiments for further validating the findings from core and Hele-Shaw experiments. Results from Hele-Shaw experiments showed that the absence of chemicals resulted in more fingering due to increased interface instability at a high interfacial tension (IFT). Conversely, the introduction of chemical additives reduced the IFT, promoted wettability alteration toward water-wet, and improved displacement efficiency. The nonionic surfactant Polysorbate 80 and organic alkali ethanolamine (ETA) and high-pH sodium metaborate (NaBO2) demonstrated enhanced imbibition rates and more uniform displacement fronts in countercurrent 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 were consistent with observations from our previous core experiments. Chemical additives also influenced the displacement geometry, producing shorter imbibition lengths but wider swept areas compared with water alone. This enhanced areal displacement efficiency and recovery factors. In horizontal countercurrent experiments, chemical additives shifted flow dynamics toward countercurrent dominance in the absence of gravity effects. Oil recovery experiments conducted in a micromodel further confirmed the effectiveness of chemical additives in suppressing viscous fingering and improving sweep efficiency. In particular, Polysorbate 80 had a higher recovery rate at the early stage, while O342 achieved a higher final recovery. The imbibition front in the O342 experiment was smooth with minimal fingering. Additionally, the presence of Polysorbate 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 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 score0.187
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.296
Teacher spread0.288 · 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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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