Effective Pressure Maintenance and Fluid Leak-Off Management Using Nanoparticle-Based Foam
Bibliographic record
Abstract
Abstract Hydraulic fracturing is the most effective stimulation process to maximize resource extraction in unconventional reservoirs. However, water leakoff into the matrix of unconventional reservoirs, whether from a frac stage or from a pad placed in a parent well and pressured up to prevent frac hits, results in relative permeability reduction, decrease in hydrocarbon production rate and possible formation damage. This paper reports the application of a foam designed with an innovative combination of nanoparticles and surfactants to create a highly stable fluid with a low leak-off rate and non-damaging characteristics. A series of laboratory tests were conducted on tight core samples with variable permeability ranging from micro-to millidarcies, using different fluid systems including gas, water, and foam. A uniquely designed coreflood setup was used to imitate the wellbore/fracture-matrix condition. A fracture/matrix pressure difference of 1500 psi was used to evaluate the performance of each fluid with respect to maintaining pressure over time and minimizing leak off at a temperature of 80 °C. The test results show that the laboratory-designed nanofoam can effectively maintain elevated pressure in the fracture sufficient to reduce frac hits. The pressure depleted to 50% of original pressure in less than 3 hours when using gas or water and less than 15 hours in case of surfactant foam. However, the nanofoam maintained a pressure higher than 50% of the original pressure for more than 70 hours. The leak-off volume of the foam was low, and the foam could be easily cleaned up with no formation damage (i.e., no change in core permeability). This study reveals the potential of a highly stable foam as a fast and reliable method to prevent frac hit problems, saving operational cost and reducing water usage without compromising the well productivity. This foam can be potentially used as a base fracture fluid due to its high viscosity, high stability, and non-damaging characteristics.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".