Pore-Scale Study on the Flow Dynamics of Coupled Low Salinity and Nanofluid Flooding in Carbonate Formations
Bibliographic record
Abstract
Abstract A significant volume of annual world oil production comes from carbonate reservoirs like the giant Middle East and Caspian Sea reservoirs. However, the production enhancement is complicated by geological heterogeneities of carbonate formations, such as a complicated network of natural fractures leading to highly permeable paths or shale streaks leading to discontinuous flow barriers. The primary objective of this paper is a feasibility study of coupled low salinity and nanofluid flooding for oil recovery enhancement from carbonate reservoirs. Accordingly, diluted seawater and two different types of nanoparticles (NPs) were exploited to prepare low-salinity nanosuspensions to understand the synergistic effects of low-salinity nanofluid (LSN) injection on oil droplet remobilization. As the multiphase flow experiments were performed using glass micromodels, surface wettability analysis was also conducted on flat glass plates to clarify the role of NPs at the interfaces. The fluid flow around shale barriers and fracture/matrix interactions were qualitatively scrutinized at the pore scale using multiphase flow tests on the oil-wet microfluidic chips inspired by the pore structures of rock samples of carbonate reservoirs. The results of contact angle experiments showed that the inclusion of NPs into low-salinity water can ameliorate the ability of the aqueous solution to reverse the surface wettability of the oil-wet samples to a more water-wet state due to the improved adsorption isotherm of NPs into the glass surface. Microscopic and macroscopic observations of the porous media flow tests also disclosed that the LSN injection could significantly improve breakthrough time as well as microscopic and macroscopic sweep efficiencies. In other words, a slight viscosity improvement of injected water due to the presence of NPs could relatively diminish the extension of fingering patterns in porous media and create a better displacement front, resulting in a higher breakthrough time of displacing fluid. Furthermore, due to surface wettability reversal, LSN injection reduced the amount of untouched oil behind the shale streaks and showed better intrusion into the matrix and a higher fluid exchange rate between the matrix and fractures. This study proves the effectiveness of LSN injection in improving the efficiency of enhanced oil recovery from carbonate formations. Besides, we highlighted the flow characteristics of LSN around the shale streaks and high permeable fractures.
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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".