Wettability Alteration of Reservoir Rock by Nonionic, Anionic and Cationic Surfactant in Water-Based Drilling Fluid
Bibliographic record
Abstract
Abstract The interaction between clay minerals in formations and drilling fluids was analyzed through a study of four core plugs in different types of fluid, including gas oil, anionic surfactant (SDS), non-ionic surfactant (PEG), and cationic surfactant (CTAB). The core plugs were cut for petrophysical tests, including permeability, saturation, X-ray diffraction, and petrographical analyses. The original samples contained clay minerals such as illite and smectite. A static immersion test revealed that swelling and dispersing changed the original petrophysical rock properties of the samples. The addition of nanoparticles of Ca, K, Na, Cl at low, high, and saturated salinity in sodium chloride (NaCl), potassium chloride (KCl), and calcium chloride (CaCl2) was used to reduce active shale and increase mud density from 8.33 to 11.8 ppg, improving petrophysical rock properties by reducing filtration and swelling. The permeability and water saturation were measured before and after core injection of the drilling fluids. The results showed that surfactants (PEG) > (SDS) > (CTAB) in a water-based drilling fluid improved fluid loss and viscosity and reduced the interfacial tension, shifting the reservoir wettability towards a more water-wet state in low, high, and saturation salinity. The use of surfactants in water-based mud reduced formation damage and increased well productivity.
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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 | 0.000 | 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) | 0.000 | 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".