Experimental study on the drainage gas recovery of an environmentally friendly nanofluid in tight gas reservoirs
Bibliographic record
Abstract
Abstract The efficiency of gas recovery in tight gas reservoirs has been a challenge in the oil and gas industry for the past decade because conventional drainage or water‐controlled gas recovery technologies typically have poor performance in low‐permeability reservoirs. To solve this problem, self‐made nanofluid was introduced to enhance drainage gas recovery in a tight gas reservoir. In this paper, nanofluid was prepared by phase reversal technology for application in a tight gas reservoir. Its thermal, acidic, alkaline, and salty stabilities were systematically investigated by using light transmittance as a shortcut index. At the same time, its biodegradability and biotoxicity were evaluated based on the industry/national standard, and the effectiveness of its drainage gas recovery was studied by dynamic gas–water percolation. The results showed that the self‐made nanofluid can be effectively used for drainage gas recovery in tight gas reservoirs. The nanofluid has chemical stability and is environmentally friendly, which fully conforms to the contemporary development trend of the oil and gas industry. The nanofluid can shift the isotonic point of the gas–water relative permeability to the left (its minimum is 1.83% at 6.25 wt.% and maximum is 4.78% at 100 wt.%) and reduce the irreducible water saturation (its minimum is 8.84% at 6.25 wt.% and maximum is 4.28% at 100 wt.%), achieving the purpose of the enhancement of drainage gas recovery. The research results provide technical support for the application of nanofluid to improve the gas production in tight gas 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 | 0.001 | 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 | 0.001 | 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".