Ranking Shale Performance from Water Flowback Analysis, Field Case
Bibliographic record
Abstract
Abstract In recent years, in unconventional reservoirs, main fracture parameters including fracture permeability and fracture volume can be early evaluated using flowback data analysis. For analysis purposes, diagnostic plots, straight-line methods, and simulation model history-matching techniques are utilized. Usually, immediate gas and water production occur during flowback in shale gas wells. In this paper, solution of water diffusivity equation for different flow regimes during the early time of well life was used to analyze water performance. These flow regimes were determined based on the diagnostic plot of water rate vs. time. The analysis from Water RTA was used to calculate initial water in place (OWIP) and fracture parameters. The difference between the OWIP and the injected fracturing fluid was correlated against the formation water saturation. The main conclusions from this analysis are; 1) High quality shale if the OWIP equal to the total injected water volume, and water-production data usually do not show the transient period and in some cases, boundary dominated flow (BDF) is present. 2) Low quality shale if the OWIP is greater than the total injected fracture fluid volume, and transient flow regimes are expected. High quality shale is good candidate for soaking process. The well performance increases after soaking process in the case of low formation water comparing to the high-water saturation wells. Honoring the flowback data can help to estimate the fracture geometry and indicate the quality of the shale formation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".