Study on Variability Production Characteristics of Fracturing Fluid Imbibition Displacement for Typical Shale
Bibliographic record
Abstract
Abstract CO2 injection is a prominent measurement to enhance the recovery of shale reservoirs. The imbibition of fracturing fluid + CO2 additive is considered important for promoting the enhancement and stabilization of shale oil production to ensure energy supply. The imbibition experiments of fracturing fluid + CO2 additive were carried out in shale reservoirs with a nuclear magnetic resonance (NMR) test. The imbibition displacement characteristics at different pore scales of shale were quantitatively evaluated. The variability of the imbibition effects in shale reservoirs was clarified in terms of shale type, fracturing fluid type, and CO2 additive (pressure). This finding indicates that the effectiveness of the fracturing fluid + CO2 additive imbibition on shale reservoirs is stronger, and the imbibition displacement efficiency ranges from 33.38 % to 41.56 %. The imbibition contribution rate is considerably higher for small pores than for large pores, and the difference between them is more than 10%. Therefore, the imbibition of fracturing fluid + CO2 additive mainly extracted crude oil from the small pores of shale reservoirs. CNI nano variable-viscous slippery water is more effective for imbibition displacement in laminar type shale. For laminated type shale, EM30+ + guanidine gum mixed water has a better imbibition effect than CNI nano variable-viscous slippery water. Under 16 MPa CO2 + fracturing fluid (miscible states), the imbibition displacement efficiency of the shale is significantly enhanced. The imbibition displacement degree at different pore scales is also increased. For different types of shale reservoirs, the imbibition displacement degree at different pore scales would be improved by methods, including the optimization of the fracturing fluid or the change of imbibition conditions. This study presents a theoretical underpinning for the high-efficiency exploitation in shale 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".