Enhanced regained permeability and fluid flowback from tight sandstone and carbonate oil reservoirs with unique flowback chemistry
Bibliographic record
Abstract
Abstract Large amounts of aqueous‐based fluids used in hydraulic fracturing of tight formations are not fully recovered immediately after treatment, resulting in increased water saturation, water blockage, clay swelling, reduced relative permeability, and long‐lasting formation damage that impedes production. To enhance flowback fluid recovery, nano‐emulsion based flowback aids were developed for oil‐bearing sandstone and carbonate formations. The flowback aids were formulated using a blend of high‐temperature stable ester‐based solvents, alcohols, and surfactants to form optically clear nano‐emulsions. All the developed flowback aids demonstrated low surface tension (22–30 mN/m) and interfacial tension (<6 mN/m), which is necessary for reducing capillary pressure. The particle size of the nano‐emulsions was found to be 5–15 nm. The flowback aids were able to prevent the formation of the emulsion with crude oil. It has been found that nano‐emulsions formulated using non‐ionic and anionic surfactants worked better for sandstone, whereas non‐ionic and cationic surfactant‐based formulations worked better for carbonate. These formulations not only provide quick aqueous fluid displacement in column tests but also greatly enhance the rate of oil flow in core flow experiments conducted with broken slickwater fracturing fluids. It was determined that in the absence of a flowback aid, the regained permeability was around 40%, whereas with flowback aids it was increased to 65%–75%. The paper demonstrates the effectiveness of flowback enhancers to quickly recover the injected aqueous fracturing fluid, thereby reducing water saturation, which in turn enhances productivity, and shows the benefit of applying chemistry for low permeability oil reservoirs.
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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".