Discussion of the Effect of Shut-In After Fracturing on Oil Recovery
Bibliographic record
Abstract
Abstract Water is accumulated near the fracture surface after fracturing, which will block oil flow out. The water blockage can be mitigated through the immediate well flow back or through shutting in the well before flow back. Which method is more effective? There are mixed results in the literature from field reports and experimental or simulation studies. This paper discussed the literature results and simulation data obtained from this study. It is found that the oil recovery mainly depends on the magnitude of pressure drawdown and the strength of imbibition. When the pressure drawdown is high, immediate flow back may lead to higher oil recovery than shutting in a well before flow back. When imbibition is strong, shutting in may be beneficial to enhance oil recovery through counter-current flow. Although many parameters of reservoir properties and operations may affect the shut-in effect, those parameters may be grouped into the pressure drawdown and imbibition strength. The parameters of matrix permeability, wettability, initial water saturation, and formation compressibility are discussed. Analysis and discussion of simulation data also suggest that the oil recovery is a linear function of pressure drawdown, but the relationship between oil recovery and capillary pressure is non-linear and more complex. The results and discussion from this study suggest that the immediate flow back may outperform the shut-in if a large pressure drawdown is applied. If a reservoir provides a strong imbibition condition, the shut-in may be beneficial. Surfactants may be chosen to enhance imbibition. The surfactants which alter the reservoir from oil-wet to water-wet may be preferred.
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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 | 0.001 |
| 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) | 0.003 | 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".