The Effect of Solvent based Nanofluid Flooding on Heavy Oil Recovery
Bibliographic record
Abstract
Abstract The objective of this study is to investigate the effect of solvent based nanofluid flooding followed by waterflooding on heavy oil recovery performance and sweep efficiency improvement in unconsolidated oil-wet porous media. The sweep efficiency improvement is correlated with in-situ emulsion generation during such displacement and is monitored with a CT scanner. Partially hydrophobic silica NPs with proper interface affinity was firstly synthesized, and subsequently dispersed in toluene along with a surfactant to prepare the solvent based nanofluid. The solvent based nanofluid was then injected into an oil saturated sandpack at different slug sizes and further chased by brine to investigate the sweep efficiency improvement through possible emulsification. To investigate the beneficial effect of NPs, the aforementioned flooding was compared against conventional solvent flooding followed by waterflooding. The displacement patterns were monitored with a CT-scanner to assess the heavy oil recovery performance in the absence or presence of the solvent based nanofluid. The fluid density profiles during solvent based NF flooding followed by waterflooding were extracted from CT-scan images and compared against conventional solvent flooding followed by waterflooding to assess potential sweep efficiency improvement. The results revealed an increase in sweep efficiency during solvent based NF flooding followed by waterflooding. This was related to the creation of in-situ emulsion at the water-diluted oil interface due to the presence of NPs and surfactant in diluted oil. Unlike conventional solvent flooding where post water created several fingers through porous media, the water phase in solvent based nanofluid flooding was able to displace all the lower resistance paths created by the solvent based nanofluid. Moreover, chase water was able to recover almost all of the solvent in solvent based NF flooding, which is essential for the process to be economically viable. In general, the presence of solvent based NF was found to be effective in re- distributing the preferential water flow paths in to a more uniform front, which suppressed the fingering in turn and resulted in later water breakthrough, better sweep efficiency, and higher oil recovery. The proposed solvent based nanofluid flooding has not been tested so far as a heavy oil recovery technique. Unlike conventional solvent flooding which can suffer from high residual solvent to waterflooding, this technique revealed negligible residual solvent upon waterflooding. Moreover, it was found to be effective in sweep efficiency improvement and heavy oil recovery compared to conventional solvent 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) | 0.001 | 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".