Pre-Post Frac Test Data Analysis for Hydraulically Fractured Vertical Tight Gas Well-Field Case Study
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Bibliographic record
Abstract
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. Keywords: hydraulic fracturingMITpre-post fracpressure transient analysisproductivity indextight reservoir
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it