Polyethylene Glycol PEG Functionalized Silane for Highly Efficient Silicate Scale Control
Bibliographic record
Abstract
Abstract Sandstone reservoirs flooded with alkaline surfactant polymer (ASP) are associated with silicate scaling of production wells. Silicate scaling has been a significant problem in ASP-flooded areas in China and Canada. This paper reports a series of highly effective silicate scale inhibitors comprised of polyethylene-glycol-functionalized silane. Surface chemistry of silicate was utilized to prevent scale growth. Silane/siloxide groups bond well with the surface of silica and silicates. The introduction of a silane/siloxide group into a scale inhibitor can greatly enhance the affinity of inhibitor molecules with the silicate surface. Conversely a silicate surface also has exposed OH groups which can form hydrogen bonds with the oxygen atoms in the backbone of PEG. The PEG polymer chain is likely to wrap around the surface of silicate nuclei, thus becoming a blocking layer and inhibiting further silicate growth. Both static and dynamic tests were utilized to evaluate the performance of the silicate scale inhibitors. The PEG-functionalized silane achieved excellent inhibition percentages at low or medium pH as compared with conventional inhibitors that are based on phosphonates or polycarboxylic acids. When brine solution pH is high, magnesium (Mg) and calcium (Ca) silicate form, grow larger, and precipitate out, before effective inhibition can occur. This research found the addition of Mg/Ca sequesters help mitigate this growth/precipitation problem by slowing the initial nucleation process. This novel study supports the introduction of a silane/siloxide group that specifically binds the inhibitor with silicate nuclei providing the advantages over conventional technologies where no specific binding between an inhibitor and precipitate is established.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".