Matrix-Fracture Interaction Dynamics in Carbonates and Tight Sandstones when Different Chemicals are Used as Fracturing Fluids and Oil Recovery Additives
Bibliographic record
Abstract
We evaluated the influence of boundary conditions on matrix-fracture interaction dynamics and compared the effectiveness of various chemical agents under different reservoir environments. Chemical selection was investigated for different applications, i.e., hydraulic fracturing in gas and oil reservoirs and enhanced oil recovery (EOR) for the latter. Results showed significant differences in the performance of different chemicals when they were applied in carbonate and sandstone cores. Notably, the cationic surfactant CTAB, which was unsuitable for EOR in sandstone cores, exhibited high efficacy in carbonate cores. In contrast, the anionic surfactant O342, effective in sandstone cores, was found to be detrimental in carbonate cores. While most chemicals displayed consistent results between co- and counter-current imbibition, O342 showed notable discrepancies. This highlights the necessity of carefully considering boundary conditions when applying these chemicals in field applications. When comparing different rock types, the tight sandstone results showed a similar trend with those in the conventional sandstone cores in terms of the effectiveness of chemicals in enhancing the final oil recovery. On the contrary, tight sandstone and carbonate cores had similar time responses (the durations to reach certain oil recovery levels) due to their lower permeability compared to conventional sandstone cores.
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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".