Efficiency of Miscible Displacement in Fractured Porous Media
Bibliographic record
Abstract
Abstract During the injection of fluids that are miscible with oil for enhanced oil recovery, oil recovery and transport of the injectant are controlled by fracture and matrix properties in naturally fractured reservoirs (NFR). For such systems, the transfer between matrix and fracture due to diffusion is the main oil recovery mechanism. Similar processes can be encountered during the sequestration of greenhouse gases, and transport of contaminants in subsurface reservoirs. Understanding the effects of the parameters on the dynamics of the process is essential in modeling such processes. In fact, the description of matrix fracture interaction for dual-porosity dual-permeability models developed for NFRs is still a challenge. Experiments were performed to study the process of diffusion during flow in fracture. 2-inch diameter and 6-inch length Berea sandstone and Indiana limestone samples were cut cylindrically. An artificial fracture spanning between injection and production ends was created and the sample was coated with heat shrinkable teflon tube. A miscible solvent (heptane) was injected from one end of the core at a constant rate. The effects of (a) oil type (mineral oil and kerosene), (b) injection rates, (c) orientation of the core, (d) matrix wettability (changed by aging the cores), (e) core type (a sandstone and a limestone), and (f) amount of water in matrix on the recovery performance were examined. The oil recovery for different matrix sizes, wettabilities, permeabilities, orientations, oil viscosities, and oil-heptane diffusion coefficients were correlated to the injection rate. Then, the ratio of matrix recovery to heptane injected was correlated to the newly defined dimensionless group (fracture diffusion index, FDI). The FDI is the ratio of fracture flow parameters (viscous forces) to matrix diffusion parameters. A critical FDI that maximizes the oil recovery while minimizing the amount of the injected fluid was defined. The process efficiency in terms of the time required for the recovery instead of the amount of solvent injected was also investigated. It is expected that the experimental results and the dimensionless group, FDI, will be useful in deriving matrix-fracture transfer function for diffusion that is controlled by the flow rate, matrix and fluid properties.
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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 | 0.001 |
| 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".