Evaluation of Surfactants for Oil Recovery Potential in Shale Reservoirs
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Most shale reservoirs (e.g., Bakken Shale and Eagle Ford) have a low permeability, low porosity, and oil-wet character with natural fractures. As a result, the oil recovery factors are very low, only a few percent of original oil in place. Injection of water into oil-wet reservoirs (i.e., water flooding) is not effective due to small or negative capillary pressure. In this study, various surfactants (non-ionic, cationic, anionic, and amphoteric) were studied for spontaneous imbibition into oil-wet shale cores. Surfactant imbibition into Eagle Ford shale outcrop cores and Bakken reservoir cores increased oil recovery compared to brine only. Oil recovery can be seen for surfactants that alter the reservoir from oil-wet to water-wet. For example, the incremental oil recovery was about 24% % for 0.1% cationic surfactant and 57% for 0.1% nonionic surfactant. The goal of this work is to investigate the effect of salinity, surfactant concentration, electrolyte concentration, and temperature on the wettability alteration and provide mechanisms. Contact angles and interfacial tensions (IFT) were measured and correlated with spontaneous imbibition. Wettability alteration from oil-wet to water-wet (i.e., low contact angle) appeared to be more important than a low interfacial tension in increasing the oil recovery rate from fractured oil-wet reservoirs, especially for nonionic surfactants and amphoteric surfactants. Wettability alteration is maximum and IFT is minimum for anionic and cationic surfactants at an optimal salinity. However, as the reservoir salinity increases, the maximum wettability alteration decreases and IFT increases.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it