Development and performance of CO2-responsive foam fracturing fluid
Bibliographic record
Abstract
CO 2 foam fracturing fluid can effectively integrate CCUS technology into oil and gas field development, and its core advantages include low water consumption, excellent flowback performance, and strong sand-carrying capacity. For these reasons, it has received increasing attention in the oil and gas field development field. However, this type of fracturing fluid still has some prominent problems: The residue from gel breaking can easily cause formation pollution, the system cost is relatively high, and the utilization rate is low, which forms a significant technical bottleneck. In response to these issues, this study, based on the theory of clean fracturing fluid gel breaking without residue and the reusability of CO 2 -responsive wormlike micelles, innovatively combines CO 2 -responsive wormlike micelles with different types of surfactant-based foaming agents to construct a new CO 2 -responsive foam fracturing fluid system. A systematic performance evaluation of the system was conducted to clarify its defoaming rules under different temperature conditions. Compared with the traditional guar gum CO 2 foam fracturing fluid, the new system has significant performance advantages. At 90 °C, its foam comprehensive value reached 19720 mL·min, 6150 mL·min higher than the guar gum fluid. After a 5400 s high-temperature and high-shear test at the same temperature, the residual viscosity of the new system was 67 mPa·s, which is higher than the guar gum fluid. This CO 2 -responsive foam fracturing fluid simultaneously possesses the application potential of both clean fracturing fluid and foam fracturing fluid. It can effectively solve key problems such as formation pollution and low system utilization rates, and laboratory evaluation experiments confirmed its excellent foaming and rheological properties. These results are of great significance for promoting CO 2 foam fracturing technology to reach an advanced international level and supporting the low-carbon and high-efficiency development of unconventional oil and gas resources in China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".