Fast Screening of LSW Brines Using QCM-D and Crude Oil-Brine Interface Analogs
Bibliographic record
Abstract
Abstract This work focuses on a potentially economic incremental oil-recovery process, where a brine amended with inexpensive salts (in contrast to expensive surfactants and other chemicals) is injected into a reservoir to increase oil production. Historically, this process received the name of low salinity waterflooding (LSW) although the salinity is not always low(er). Nevertheless, we keep using this terminology for historical reasons. The idea of LSW has been known for three decades, but to the best of our knowledge no specific brine recipes that guarantee success have been presented so far. The reasons hide in the problem's complexity, disagreements in the scientific community, and a race to publish rather than to understand the fundamental principles behind the process. In this paper, we present an experimental model system that captures many of the important fundamental features of the natural process of crude oil attachment to mineral surfaces, but at the same time decomposes this complex process into simpler parts that can be more precisely controlled and understood. We systematically investigate the first-order chemical interactions contributing to the well-known strong attachment of crude oil to minerals using SiO2 as a mineral for its surface chemistry simplicity. Our preliminary results suggest that magnesium and sulfate ions are potent in detaching amino/ammonium-based linkages of crude oil with a SiO2 surface. However, when used together in the form of MgSO4, they lose part of their activity to the formation of a MgSO4 ion pairs. We also find that sulfate-detachment propensity stems not from the interaction with prototype mineral surface, but rather from the interactions with the crude oil-brine interface analog. We continue the systematic study of the ion effects on crude oil detachment, with and more results following in the future.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".