Hydraulic-Fracture Production Forecast in Tight-Gas Reservoirs Using Wireline Formation Testers
Bibliographic record
Abstract
Abstract By using pressure and fluid indetification data gathered with pump-out wireline formation testers, operators can gain a better understanding of the reservoir, resulting in quality decisions concerning the economic feasibility of stimulating particular zones with hydraulic fractures. Permeabilities and extrapolated reservoir pressures are calculated by performing pressure transient analysis (PTA) on the gathered data. These key parameters can be combined with parameters estimated from petrophysical logs and/or correlations from neighboring wells, resulting in a production forecast for the effectiveness of a potential hydraulic fracture. Input parameters for this hydraulic fracture model can be varied to generate multiple scenario production forecasts. Based on these forecasts, a well-informed decision can be made on whether or not to stimulate a zone with a hydraulic fracture. The pressure tests are performed in cased-hole environments where zones can be isolated with a dual packer tool assembly after perforating. With the pump-out capability of this tool, multiple drawdown and buildup cycles are performed.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".