Effect of engineered water and scale inhibitor concentration on the adsorption performance of gypsum inhibitors on multi‐walled carbon nanotubes and crushed sandstone
Bibliographic record
Abstract
Abstract Engineered waterflooding is an effective process for enhanced oil recovery, but it can lead to mineral scale deposition due to the incompatibility of brines. Previous studies on scale mitigation have mainly focused on high‐temperature baryte and anhydrite scales, neglecting gypsum precipitation and inhibitors adsorption at low temperatures. Additionally, earlier research has used simple brines that do not reflect the actual injection and formation water in reservoirs. In this study, the impact of temperature, various brine mixtures, and thermodynamic databases on saturation ratio and scale precipitation is explored using PHREEQC (pH‐REdox‐EQuilibrium‐C program). The study reveals that the copresence of calcium and magnesium ions improves the gypsum inhibition efficiency of scale inhibitors (SIs) at low concentrations to a maximum of 79%. However, this effect is reversed or neutral at higher SI concentrations. The study also shows that the presence of monovalent ions reduces the adsorption of SIs by multi‐walled carbon nanotubes (MWCNTs). Removing sodium ions from seawater while leaving calcium and magnesium ions intact increases MWCNTs' adsorption capability to 93%. This is because monovalent cations attach to the adsorbent surface and block the active sites, whereas divalent cations act as a bridge between MWCNT and SIs. The study establishes that the behaviour of SIs regarding adsorption on MWCNT and crushed sandstone depends on various factors, including molecule size, calcium toleration of the SIs, point of zero charge, and solution pH. Understanding these factors can improve the effectiveness of SIs, reduce chemical costs, and prolong the life of oil wells.
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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".