Organic alkalis as potential additives in hot water flooding for enhancing heavy oil recovery
Bibliographic record
Abstract
Abstract Alkali–surfactant–polymer (ASP) flooding is a chemical enhanced oil recovery method; its working mechanisms include mobility control, wettability alteration, and in‐situ emulsification. However, conventional alkalis used in the ASP flooding could cause some serious issues, including scaling, formation damage, and pipe corrosion. In this study, we investigated the feasibility of incorporating organic alkalis as chemical additives in hot water flooding to enhance heavy oil recovery. The focus was on studying the interfacial properties, that is, interfacial tension (IFT) and wettability, emulsification mechanisms, thermal stability of alkalis, and recovery efficiency through coreflooding experiments. Three organic alkalis, including diethylamine (DEA), ethanolamine (ETA), and triethlymine (TEA), were selected to evaluate their potentials in enhancing heavy oil recovery and investigate their recovery mechanisms. Coreflooding experiments were conducted at 50°C and 2 MPa with sandpacks saturated with a high total‐acid‐number (TAN) heavy oil sample. The measurement results demonstrated that all three organic alkalis could effectively reduce the IFT between water and heavy oil from 22.7 to 1.6 mN/m and favourably alter the wettability of rock surface. ETA exhibited good thermal stability up to 100°C in hot water applications. The interaction between organic alkalis and heavy oil could generate either water‐in‐oil or oil‐in‐water emulsions, depending on the water–oil ratio (WOR). The coreflooding experiments showed a significant increase (16%–17.5%) in heavy oil recovery using ETA for two distinct injection strategies. IFT reduction, wettability alteration, and emulsification were found to be the main recovery mechanisms when organic alkalis are utilized as chemical additives for hot water flooding.
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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) | 0.000 | 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 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".