Main controlling factors of fracturing fluid imbibition in shale fracture network
Bibliographic record
Abstract
After fracturing operations, a large amount of fracturing fluid is retained in shale fracture network, resulting in low flowback efficiency. This has been attributed to the imbibition of fracturing fluid into matrix pores. However, it is unclear how the imbibition mechanism is involved, what are its governing laws and controling parameters in fracture networks? Based on the three-dimensional water imbibition theory of matrix blocks, a fracture network model is established, and a number of dimensionless controling parameters are proposed and analyzed for flowback efficiency. The results show that the imbibition characteristics of fracturing fluid in fracture network are mainly determined by two dimensionless numbers; namely, dimensionless imbibition time, fracture width, and imbibition capacity. The dimensionless imbibition time characterizes the contact time between the fracturing fluid and shale formation, which negatively correlates to the flowback efficiency. The dimensionless fracture width is the ratio of the fracture width to the rock length, which is inversely proportional to the flowback efficiency. Smaller value of the dimensionless fracture width corresponds to larger contact area of fracturing fluid and shale, leading to a lower flowback efficiency. The dimensionless imbibition capacity depicts the capacity of shale reservoirs to imbibe fracturing fluid, which has a negative linear correlation with flowback efficiency. In addition, dimensionless time and fracture width are related to the fracturing operations, and are enhanced by increasing the shut-in periods and proppant concentration. Therefore, the flowback efficiency can be controlled by changing fracturing operations. The predictive method of the flowback efficiency established here is of great significance for reservoir damage analysis and flowback regime optimization. Cited as : Yang, L., Wang, S., Cai, J., Gan, Y., Salama, A. Main controlling factors of fracturing fluid imbibition in shale fracture network. Capillarity, 2018, 1(1): 1-10, doi: 10.26804/capi.2018.01.01
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".