Experimental Investigation of Electrokinetic Assisted Hybrid Smartwater EOR in Carbonate Reservoirs
Bibliographic record
Abstract
Abstract Smartwater flooding is a promising oil recovery technique, demonstrating positive results in many laboratory and field tests, by altering salinity and ionic composition of injection water. Injection of smartwater in carbonate reservoirs has gained interest due to its potential feasible application, taking advantage of improved oil recovery. Electrokinetic enhanced oil recovery (EK-EOR) is another rising technology that involves passing low D.C. current through the reservoir between a subsurface anode and cathode in the producing well. It has demonstrated a number of advantages including fluid viscosity reduction, permeability enhancement and reduced water cut. Our formulation aimed at advancing their combined mechanisms through a novel hybrid EOR. This study is the first attempt to present the experimental work on Smartwater Flooding integrated with the application of Electrokinetics (EK). The effects of total salinity, reducing monovalent ions (Na+ and Cl-) and spiking of multivalent anions (SO42-) on the crude oil/brine/rock interaction were studied. Zeta potential tests were integrated with core flooding experiments systematically designed to identify the optimum ionic concentration and current density. Optimization of current density was essential for controlling both Cl2 gas generation and formation damage. EK showed positive effects with our designed Smartwater, when optimum currentdensity was induced, allowing earlier production and incremental displacement efficiency of 2.4-6%.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".