Bibliographic record
Abstract
Academia Pore-scale transport phenomena in reservoirs is of critical importance to the oil and gas industry, and the study of fluid transport at small scales has a long history that predates the term “microfluidics.” The term is predominantly associated with microfluidic chip-based technology that emerged in the 1990s and grew in response to health and life science applications. Oil and gas professionals are realizing the potential of this technology and tapping into various sectors. For example, OndaVia, founded in 2009, has patented microfluidics-based analysis and separation tools for applications in the chemical industry. There are exciting opportunities for industry giants not only to improve their business but also to encourage research at academic institutions. Researchers and entrepreneurs can collaborate in order to make microfluidics applications more accessible and soon become the go-to solutions to inform operations and perform fluid analysis. There are also tremendous opportunities through startup companies and technology acquisitions to provide faster and better fluid property measurements as well as screening and assessment of reservoir processes at the pore scale. The biggest challenges here are investment capital and standardization, both of which need to be addressed with more awareness among the industry pioneers. Microfluidics for oil and gas startups can grow in response to revenue and may be acquired by an established service company to reach global markets. The central advantages that motivated the microfluidic approach in life sciences were rapid analysis; low reagent volumes; low cost; excellent control of conditions, particularly on the scale of biological cells; and opportunities for separations and multiplexing. Several of these advantages can translate directly to oil and gas applications, both above and below ground, as highlighted in Fig. 1. In addition, suitably fabricated glass and silicon microfluidic chips can readily accommodate reservoir pressures. For instance, in contrast to conventional large pressure/volume/temperature cells, high pressures are easily handled with microfluidics owing to the very small volumes (and small areas) involved. The challenge is leveraging our unique microfluidic tools to maximize the contribution to the energy sector. We need to find challenges that are both important and uniquely well-suited to a microfluidic approach. Outlined below are two such approaches: microfluidics for oil and gas fluid analysis, and microfluidics/micromodels to understand pore-scale processes in reservoirs.
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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".