Tertiary-Recovery Improvement of Steam Injection Using Chemical Additives: Pore-Scale Understanding of Challenges and Solutions Through Visual Experiments
Bibliographic record
Abstract
Summary Our previous research, honoring interfacial properties, revealed that the wettability state is predominantly caused by phase change—transforming liquid phase to steam phase—with the potential to affect the recovery performance of heavy oil. Mainly, the system was able to maintain its water-wetness in the liquid (hot-water) phase but attained a completely and irrevocably oil-wet state after the steam-injection process. Although a more favorable water-wetness was presented at the hot-water condition, the heavy-oil-recovery process was challenging because of the mobility contrast between heavy oil and water. Correspondingly, we substantiated that the use of thermally stable chemicals, including alkalis, ionic liquids, solvents, and nanofluids, could propitiously restore the wettability. Two types of heavy oil (450 and 111,600 cp at 25°C) were used in a glass-bead-pack model at steam temperatures up to 200°C. Initially, the glass-bead-pack model was saturated with synthesized formation water and then displaced by heavy oils. The process was then followed by steam injection generated by escalating the temperature to steam temperature and maintaining a pressure lower than saturation pressure. Subsequently, the previously selected chemical additives were injected into the glass-bead-pack model as a tertiary-recovery application to further evaluate their performance. We observed that phase change (in addition to the capillary forces) was substantial in affecting both the phase distribution/residual oil in the porous media and wettability state. A more oil-wet state was evidenced in the steam case rather than in the liquid (hot-water) case. Despite the conditions, auspicious wettability alteration was achievable with thermally stable surfactants, nanofluids, water-soluble solvent [dimethyl ether (DME)], and switchable-hydrophilicity tertiary amines (SHTAs), improving the capillary number. The residual oil in the porous media yielded after injections could be favorably diminished post-chemical injection (for example, in the case of DME). In addition, more than 80% of the remaining oil was recovered after adding this chemical to steam. Analyses of wettability alteration and phase distribution/residual oil in the porous media through glass-bead-pack-model visualization on thermal applications present valuable perspectives in the phase-entrapment mechanism and the performance of heavy-oil recovery. This research also provides evidence and validations for tertiary recovery beneficial to mature fields under steam applications.
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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 | 0.000 | 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 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".