Microfluidic devices, materials, and recent progress for petroleum applications: A review
Bibliographic record
Abstract
Abstract Microfluidic devices are miniaturized systems that manipulate fluids on a small scale, typically at microlitre or nanolitre volumes. They have received significant attention in various industries, including petroleum applications. The recent advancements in microfluidic devices for petroleum applications have the potential to improve oil recovery efficiency, reduce costs, and provide valuable insights into fluid behaviour and reservoir characterization. This paper aims to provide a comprehensive overview of microfluidic devices, their materials, and their applications in the petroleum industry. The first section of the paper focuses on describing the materials used in microfluidic devices specifically tailored for energy sector applications. In the second section, the paper highlights relevant applications and discoveries in petroleum research, showcasing the innovative techniques and special features enabled by microfluidic devices. These applications include but are not limited to asphaltenes characterization, enhanced oil recovery (EOR), and water treatment. The versatility and customization of microfluidic devices have allowed researchers to accurately represent fractures, ensure chemical conformance, simulate high pressure–high temperature conditions and reservoir heterogeneity, and study geochemical interaction. Additionally, molecular tagging and machine learning techniques have been employed for image analysis, further enhancing the capabilities of microfluidic devices. Throughout the paper, the advantages and disadvantages of implementing microfluidic devices and utilizing specific materials are thoroughly discussed. This analysis provides valuable insights into the challenges and potential limitations associated with this technology. To conclude, the paper offers suggestions, ideas, and highlights for future research paths, pointing toward promising directions for further exploration in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".