Pore-Scale Investigation of the Matrix−Fracture Interaction During CO<sub>2</sub>Injection in Naturally Fractured Oil Reservoirs
Bibliographic record
Abstract
Sequestration of CO 2 into oil and gas reservoirs gains respect as an economically and environmentally convenient way of reducing emissions of greenhouse gas and increasing hydrocarbon production at the same time. Because the naturally fractured reservoirs (NFRs) constitute a great portion of current and potential CO 2 injection applications, it is essential to understand the matrix−fracture interaction during such applications to maximize the efficiency of the process, maximizing incremental oil production with maximum CO 2 storage. Visualization of the phase behavior and flow patterns to/from the fracture and from/to the matrix is critical in understating the process and discovering ways to co-optimize the oil production−greenhouse gas storage process. Hence, pore-scale behavior of the CO 2 −oil interaction was investigated experimentally using homo- and heterogeneous fractured micromodels. Glass-etched microfluidic models were employed to investigate the pore-scale interaction between the matrix and fracture. Models were prepared by etching homo- and heterogeneous microscale pore patterns with a fracture in the middle of the model on glass sheets bonded together and then saturated with colored n -decane as the oleic phase. CO 2 was injected at miscible and immiscible conditions. The focus of the study was on visual pore-scale analysis of miscibility, breakthrough of CO 2, and oil/CO 2 transfer between the matrix and fracture under different miscibility conditions. More specifically, the CO 2 −oil interaction near the fracture region inside the matrix was visualized, and its impacts on the further transport of CO 2 inside the matrix by diffusion, transfer of oil from the matrix to the fracture and its flow in the fracture, and CO 2 storage inside the matrix during these processes were analyzed visually.
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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 | 0.001 |
| 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".