Wettability Alteration of Oil-Wet Calcite: A Mechanistic Study
Bibliographic record
Abstract
Abstract Enhancing oil recovery in naturally fractured reservoirs by injecting chemistry-optimized water has been widely investigated recently and has demonstrated its efficiency at both laboratory and field trials. There is an extensive ongoing efforts in the industry to characterize and understand the responsible mechanisms at scales ranging from nano-scale to field scale. The ionic formulation of the injected brine affects dramatically the crude oil/brine/rock interfaces, altering rock wettability and improving oil recovery efficiency. In this experimental work, a mechanistic study is performed utilizing analytical methods to study the effect of the ionic composition and ionic strength on the rock sample wettability. The combination of Thermogravimetric Analysis (TGA) and Fourier Transform Infrared (FTIR) spectroscopy is a time saving experimental approach, suitable for wettability alteration quantification of rock samples. The results indicate that stearic acid stretching vibrational bands decrease with the decrease of brine ionic strength indicating a partial release of adsorbed organic material from calcite surface. Single ion brines impacted the calcite wettability and sulfate ions were found to be the most effective in stearic acid release followed by sodium, calcium and magnesium. Thermogravimetric analysis confirmed the observed trend and the calcite weight loss due to stearic acid decomposition decreased with decreasing brine ionic strength and confirmed the fact that sulfates ions are the most effective in partial release of adsorbed stearic acid from calcite surface.
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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) | 0.001 | 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".