Miscible Interaction Between Matrix and Fracture: A Visualization and Simulation Study
Bibliographic record
Abstract
Summary CO2 injection has been applied in naturally fractured reservoirs (NFRs) for the purpose of enhanced oil recovery (i.e., the Wey-burn and Midale fields, Canada; the Wasson and Slaughter fields, USA; and the Bati Raman field, Turkey). The matrix part of these types of reservoirs could potentially be a good storage medium as well. Understanding the matrix/fracture interaction during this process and the dynamics of the flow in this dual-porosity system requires visual analyses. We mimicked fully miscible CO2 injection in NFRs using 2D models with a single fracture and oil (solute)/hydrocarbon solvent pairs. The focus was on the visual pore-scale analysis of miscibility interaction, breakthrough of solvent through fracture, transfer between matrix and fracture, and the dynamics of miscible displacement inside the matrix. First, matrix/fracture interaction was studied intensively using 2D glass-bead models experimentally. The model was prepared using acrylic sheets and glass beads saturated with oil as a porous medium while a narrow gap of 1-mm size containing filter paper served as a fracture. The first contact miscible solvent (pentane) was injected into the fracture, and the flow distribution was monitored using an image-acquisition and -processing system. The produced solvent and solute were continuously analyzed for compositional study. The effects of several parameters, such as flow rate, viscosity ratio (oil/solvent), and gravity, were studied. Next, the process was modeled numerically using a commercial compositional simulator, and the saturation distribution in the matrix was matched to experimental data. The key parameters in the matching process were the effective diffusion coefficients and the longitudinal and the transverse dispersivities. The diffusion coefficients were specified for each fluid, and dispersivities were assigned into gridblocks separately for the fracture and the matrix.
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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 | 0.001 | 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".