Impacts From Fractures on Oil-Recovery Mechanisms in Carbonate Rocks at Oil-Wet and Water-Wet Conditions—Visualizing Fluid Flow Across Fractures With MRI
Bibliographic record
Abstract
Abstract The fracture/matrix transfer and fluid flow behavior in fractured carbonate rock was experimentally investigated using magnetic resonance imaging (MRI). Viscous oil-water displacements in stacked carbonate core plugs were investigated at wettability conditions ranging from strongly water-wet to moderately oil-wet. The impact of wettability and was investigated in a series of flooding experiments. The objective was to determine the impacts on fluid flow from different types of fractures at various wettability conditions. A general-purpose commercial core analysis simulator was used to simulate the flood experiments and to perform a parameter sensitivity study. The results demonstrated how capillary continuity across open fractures may be obtained when wetting phase bridges were established. A viscous component over the open fractures was provided when the wetting preference between the injected fluid and the rock surface allowed the formation of stable wetting phase bridges. The combination of high spatial resolution imaging and rapid data acquisition revealed how the transport mechanisms for oil and water were governed by the wetting affinity between the rock surface and the fluids in the fracture; both at moderately water wet conditions and at moderately oil wet conditions.
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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 | 0.000 |
| 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) | 0.001 | 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".