Effects of Hydraulic Fractures on the Treatment of Condensate by Huff-n-Puff Gas Injection in Eagle Ford Shale
Bibliographic record
Abstract
Abstract With the production of a gas condensate tight reservoir, condensate in fracture can significantly decrease the productivity of gas condensate wells. Previous work shows that the negative effect of condensate bank on gas production is more seriously in tighter reservoir. The implement of hydraulic fracture and multi-stages fracturing can mitigate the negative effects on the formation of condensate bank, but the effects cannot be completely removed. A huff-n-puff methane injection has been proven to be an effective method to reduce the effect of condensate by maintaining the reservoir pressure above the dew-point pressure and revaporizing the formed condensate. However, the previous work neglects the effect of hydraulic fractures on the treatment of condensate by gas injection. Non-aqueous components are lumped into fourteen pseudo components. Dual permeability, logarithmically spaced, locally refined grids are implemented to model natural and hydraulic fractures in a simulation model. The laboratory measured relative permeability taking account of condensate bank is used to accurately represent two-phase flow in a shale reservoir. Furthermore, two different settings of cyclic schedule are implemented to test their advantages and disadvantages. The results show that the huff-n-puff dry gas injection can effectively improve gas and condensate recovery. A fracture conductivity has significant influences on well production during the injection, while during the primary recovery its impact is not that obvious. In addition, the huff-n-puff schedules also plays a crucial role in both gas and condensate recovery. Our study highlights gas injection mitigates the effect of condensate bank on gas production in a shale reservoir, and provides an extensive insight on optimal design of Huff-n-Puff gas injection.
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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".