Water Flowback RTA Analysis to Estimate Fracture Geometry and Rank the Shale Quality
Bibliographic record
Abstract
Abstract Recently, flowback data analysis enabled us to evaluate important fracture parameters including fracture conductivity and volume in unconventional reservoirs. To perform the analysis, diagnostic plots, straight-line techniques, and history-matching techniques have been used. Immediate water and gas production usually occurs on flowback in shale gas wells. In this paper, a novel workflow is developed for the analysis of water flowback data and early-time production of shale gas wells. This analysis then helps to define the movable water and the applicability of the soaking process on the shale gas well. Rate transient analysis (RTA) combined with decline curve analysis (DCA) was used to analyze different shale gas wells. Effective fracture volume and geometry were calculated from the RTA analysis. Estimated ultimate water recovery was calculated from DCA. The calculated water-in-place and the estimated ultimate water recover (EURw) will be compared against the injected fracturing fluid. Water RTA result show that in the case of shale wells with no movable formation water, gas kick off early, and boundary dominated flow (BDF) was observed. In addition, these wells performance improved with soaking process. On the other hand, if initial formation water saturation is higher than the connate water, water production will be from the frac fluid and formation water. As a result, gas kick off delays and transient flow regimes are expected. Soaking process can have a negative impact on the well performance if the movable water saturation is high. Honoring the flowback data can help to estimate the fracture geometry and to judge the quality of the shale formation quality and its validity for soaking process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".