Pre-Post Frac Test Data Analysis for Hydraulically Fractured Vertical Tight Gas Well-Field Case Study
Bibliographic record
Abstract
Worldwide there are vast reserves of natural gas trapped in tight sandstone formation, and due to the low viscosity of natural gas, it can be easily recovered. To produce this huge amount of reserve from low permeability formation economically, hydraulic fracturing can be applied. Therefore, the objective of hydraulic fracturing for well stimulation is to increase well productivity by creating a highly conductive path (compared to reservoir permeability) a distance away from the wellbore into the formation. The post-treatment performance provides a good indication of stimulation success, whereas, pressure transient (PTA) and production data analysis for hydraulically fractured vertical well remains the most applied method to determine the reservoir and fracture parameters. Therefore, this analysis is a key element for optimization of hydraulic fracturing process and forecasting well performance. This article discuses the analysis of pressure and production data from hydraulically fractured vertical well in low permeability sandstone reservoir. Whereas, pressure transient analysis is used to evaluate the effective fracture parameters such as fracture half-length, fracture conductivity, and reservoir properties. Field example of application of production data analysis for vertical fractured well are presented. The aim of this study is to evaluate the gas well productivity as a result of hydraulic fracturing treatments compared to the pre-fracturing productivity and to estimate the petrophysical properties of the gas well from MIT testing data. Moreover, a discussion of how significant the increment in gas productivity was achieved with a very high propped fracture treatment success rate is also presented. Furthermore, a view of how the correct design of fracture treatments can enhance reservoir performance and the recovery rate is discussed in detail.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".