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Record W4410932703 · doi:10.2118/225376-ms

Innovating EOR Strategies: Unlocking the Potential of Streaming Potential (Electrokinetic) as Sustainable and Ecofriendly Surveillance Tools for Monitoring ASP Fluid Front

2025· article· en· W4410932703 on OpenAlexaff
Dike Fitriansyah Putra, Mohd Zaidi Jaafar, Tengku Amran Tengku Mohd, MHaidar T. Putra, İbrahim Kocabaş, Ichsan Al Sabah Lukman, Vira T. Hafeni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectrokinetic phenomenaStreaming currentPetroleum engineeringMicrofluidicsComputer scienceEnvironmental scienceProcess engineeringEngineeringNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Enhanced Oil Recovery (EOR) techniques are critical for maximizing hydrocarbon recovery from mature reservoirs. Among the various EOR methods, Alkaline-Surfactant-Polymer (ASP) flooding has gained significant attention due to its ability to improve oil displacement efficiency by reducing interfacial tension, altering wettability, and enhancing viscosity control (Guo, Li, Wang, et al., 2017; D. F. Putra et al., 2023; Zhong et al., 2019). However, despite its effectiveness, ASP flooding presents a significant challenge: real-time monitoring of the injected fluid front to ensure optimal sweep efficiency and minimize production risks (Amran et al., 2017; Mohd et al., 2017; D. F. Putra et al., 2024). The Challenge of Monitoring ASP Fluid Fronts In conventional ASP flooding operations, tracers, resistivity measurements, and production data analysis are commonly used to infer fluid movement. While these methods provide valuable insights, they have inherent limitations: Tracers require continuous sampling and analysis, which can be time-consuming and expensive (D. Putra et al., 2021; Rahman & Putra, 2021). Resistivity logs can indicate fluid saturation changes but may not effectively differentiate between ASP fluids and formation water (Attia, 2007). Production data interpretation offers a delayed response, often reflecting historical rather than real-time fluid movement (Li et al., 2019; Liu, 2019) because ASP flooding involves a complex chemical interplay between reservoir minerals and injected fluids, a more direct, non-intrusive, and continuous surveillance method is required to improve process efficiency (Guo, Li, Wang, et al., 2017; Huang et al., 2019). Electrokinetic Phenomena as a Novel Monitoring Tool Electrokinetic effects, specifically streaming potential, arise when a fluid moves through a porous medium and induces a charge separation at the rock-fluid interface. This natural electrochemical response has been observed in various subsurface applications, including geothermal monitoring, water alternating gas process, intelligent wells, groundwater flow detection, and subsurface geophysics (Anuar et al., 2013; Revil, 2013). When applied to ASP flooding, streaming potential can serve as a real-time, eco-friendly surveillance tool for tracking the movement of injected fluids (Amran et al., 2017; Guichet et al., 2003; Jaafar et al., 2015; Mohd et al., 2017). The fundamental concept behind streaming potential monitoring in ASP flooding is that when ASP fluids move through porous reservoir rocks, they interact with mineral surfaces, creating an electrokinetic response that can be measured as a potential difference (Glover et al., 2012; Walker & Glover, 2018). This response varies based on fluid composition, rock mineralogy, and flow velocity, allowing it to be used as an indirect indicator of ASP fluid movement (Alizadeh et al., 2023; Wang et al., 2017). Unlike chemical tracers or geophysical surveys, streaming potential measurements can be conducted passively, requiring only well-placed electrodes in production or observation wells (Chen et al., 2006; Jaafar, 2013; Jaafar et al., 2009).

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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
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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Citations0
Published2025
Admission routes1
Has abstractyes

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