Nanoparticle-Enhanced Surfactant Floods to Unlock Heavy Oil
Bibliographic record
Abstract
Abstract Thermal and solvent-based EOR methods are not applicable in many of thin post-CHOPS heavy oil reservoirs in Western Canada. Alkaline-surfactant flooding has been suggested as an alternative to develop these reservoirs. The main mechanism behind these processes has been attributed to emulsion-assisted conformance control due to the effect of synthetic and/or natural surfactants. Because nanoparticles (NPs) offer some advantages in emulsion stabilization, here we combine surface-modified silica NPs and anionic surfactants to enhance the efficiency of heavy oil chemical floods. Based on the results of bulk fluid screening experiments, in the absence of surface-modified silica NP surfactant concentration should be tuned at the CMC (between 1 and 1.5 wt. %) to achieve the highest amount of emulsion. These emulsions are much less viscous than the originating heavy oil. However, at surfactant concentrations far from the CMC, complete phase separation occurs 24 hours after preparation. In the presence of surface-modified silica NP this emulsification was achieved at much lower surfactant concentration. The mixture of 0.1 wt. % anionic surfactant and 2 wt. % surface-modified silica NP produce a homogeneous emulsion of heavy oil in an aqueous phase. This observation was not observed when aqueous phase contains only either 0.1 wt. % anionic surfactant or 2 wt. % silica NP. Preliminary tertiary chemical floods with water containing 0.1 wt. % surfactant and 2 wt. % surface-modified silica NP yielded an incremental oil recovery of 48 % OOIP, which is remarkably higher than that of either surfactant or NP floods with incremental recoveries of 16 and 36 % OOIP, respectively. Tertiary recovery efficiency, defined as ratio of incremental recovery factor to maximum pressure gradient during the tertiary flood, is six times greater for the surfactant/NP mixture than for the surfactant-only flood. This enhancement in recovery efficiency is of great interest for field applications where high EOR and large injectivity are desired.
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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".