Innovating EOR Strategies: Unlocking the Potential of Streaming Potential (Electrokinetic) as Sustainable and Ecofriendly Surveillance Tools for Monitoring ASP Fluid Front
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".