Static Adsorption Evaluation for Anionic-Nonionic Surfactant Mixture on Sandstone in the Presence of Crude Oil at High Reservoir Temperature Condition
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Bibliographic record
Abstract
Summary The application of surfactants in enhanced oil recovery (EOR) has revealed over the years various challenges that impose limitations on the successful implementation of surfactant flooding. Surfactant adsorption is one of the most important aspects that strongly dictates the feasibility of surfactant-based EOR. The effect of the presence of crude oil on surfactant adsorption and the influence of surfactant partitioning on the adsorption quantification are presented in this paper. Static adsorption experiments were conducted in this study for a surfactant mixture [alkyl ether carboxylate (AEC):alkylpolyglucoside (APG)] on sandstone rock samples in the absence and presence of crude oil. Partitioning experiments were carried out to evaluate the surfactant partitioning between the aqueous surfactant solution and the crude oil to determine the partitioning influence on the adsorption results in the presence of crude oil. The mixture’s adsorption and partitioning behaviors were studied at a fixed salinity of 32 k ppm and temperatures of 80 and 106°C. High-performance liquid chromatography (HPLC) was used in measuring the surfactant concentration throughout adsorption and partitioning tests. Rock characterization was also performed in this study using X-ray diffraction (XRD) as well as X-ray photoelectron spectroscopy (XPS) before and after adsorption with and without crude oil being present. Static adsorption outcomes displayed the adsorption of APG, AEC, and the overall mixture with and without crude oil being present, because all are having a similar increasing trend when concentration increases. However, the adsorption values were much higher when crude oil was present as compared with the adsorption values when crude oil was absent; this is because of not considering the impact of surfactant partitioning. The adsorption values (i.e., at 0.2 wt%) for both temperatures were below 2.5 mg/g in the absence of crude oil and rose to around 3.5 mg/g in the presence of crude oil. A significant amount of what was adsorbed belongs to AEC because of its increased chain-chain interactions with APG, which was evidenced experimentally in our previous work; hence, AEC is the greatest contributor to the overall surfactant mixture’s adsorption. Also, temperature had an impact on the adsorption capacity of the AEC:APG mixture, showing that APG has a greater sensitivity to temperature in comparison to AEC. The adsorption behavior of APG was found to be the opposite of AEC, where the adsorption capacity at 106°C was lower for AEC than its adsorption capacity at 80°C and vice versa for APG. The surfactant partitioning results were used to validate the surfactant adsorption outcomes in the presence of crude oil. After eliminating the partitioning effect, the surfactant adsorption isotherms in both cases of the presence and the absence of crude oil were almost identical. The results highlighted the importance of measuring surfactant partitioning, and the impact that partitioning has on the total surfactant losses during the surfactant flooding process. XRD and XPS results indicated that the change of the rock structure after adsorption when crude oil was present was attributed to the rock dissolution phenomena. It was concluded that adsorption and partitioning take place in the water/oil/rock system simultaneously and taking that into account allows for the improved and proper designing of the surfactant flooding.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it