Performance Evaluation of Water Control with Nanoemulsion as Pre-pad Fluid in Hydraulically Fracturing Tight Gas Formations
Bibliographic record
Abstract
Experimental techniques have been developed to evaluate performance of nanoemulsions as pre-pad fluid for reducing water-cut in tight gas formations with multistage hydraulic fractures. More specifically, an aminopolysiloxane nanoemulsion was prepared and formulated in the laboratory for effective and efficient water control. The water-control mechanisms of nanoemulsions as pre-pad fluid in hydraulic fracturing were identified by performing the scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), spontaneous imbibition tests, and wettability measurements. The SEM and XPS tests confirm that certain changes occur in the element compositions and microstructure of the core surface before and after introducing the prepared nanoemulsions on the reservoir rock surface. Such changes irreversibly alter its wettability, indicated by the measured contact angle from 75° to 128°. During the displacement experiments, there exists a disproportionate permeability reduction on relative permeability of the gas–water systems. Gas relative permeability has only a small decline by less than 20% at different water saturations, while water relative permeability is reduced up to 60%. In addition, effective gas permeability for the core samples treated with nanoemulsions can be rapidly restored by the potential acid hydrolysis. This leads to not only creating new paths for gas flow in hydraulic fractures or invaded zones but also reducing the inertial resistance of gas flow in porous media by nanoemulsion retention.
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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".