Characterization of Hydraulically-Induced Fracture Initial Water Saturation Distribution Using Arp's Correlation
Bibliographic record
Abstract
Abstract The application of coupled horizontal wells and the multistage fracturing technology to shale formations makes it viable to develop such hydrocarbon resources. In this paper, we focus on the water saturation distribution within hydraulic fractures and the uncertainty of water saturation distribution of the matrix around the hydraulic fractures resulted from fluid injection during a fracking process. Arp's decline curve is introduced to depict initial water saturation distribution within the hydraulic fractures. Through modifying the decline index, it is easy to obtain a range of initial water saturation decline patterns which will lead to different water production profiles. Typical decline patterns such as linear, exponential, harmonic, and hyperbolic declines are used. As for the initial water saturation distribution of the matrix around the hydraulic fractures, the Stimulated Reservoir Volume (SRV) concept is implemented to identify the matrix region. Through reservoir simulations, we find that the initial water saturation within the hydraulic fractures mainly contributes to the early water production. Various saturation distribution models result in about 25% differences in the total water production. On the other hand, a leak-off effect, which increases the initial water saturation of the matrix around the hydraulic fractures, contributes to the long term water production. This study provides insights about the uncertainty of water production during the development of shale gas reservoirs and guidelines for the history matching of water production rates.
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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".