Executing Minifrac Tests and Interpreting After-Closure Data for Determining Reservoir Characteristics in Unconventional Reservoirs
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Pore pressure (pi) and flow capacity (kh) are difficult to ascertain in ultra-low permeability formations due to poor inflow prior to stimulation. Furthermore, radial flow does not develop in horizontal wells completed with massive multi-stage hydraulic fractures. As a result, industry is turning to alternate testing methods, conducted prior to the main hydraulic fracture treatments. Of these, minifrac tests are rapidly gaining acceptance as the most practical way to obtain good estimates of pore pressure and flow capacity in unconventional reservoirs. Unfortunately, these test objectives are often unrealized when design and execution of the minifrac test are conducted with other objectives in mind. Even after a mechanically successful test has been concluded, there can be confusion over how to interpret the after-closure data. This paper outlines recommended operational guidelines for conducting minifrac tests with the purpose of estimating pore pressure and flow capacity. In addition, various aspects of after-closure analysis are investigated and examples are used to show that all after-closure analysis techniques, when applied correctly, are applicable and give consistent estimates of pore pressure and flow capacity. The power of using analytical models to enhance after-closure analysis is demonstrated.
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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 | 0.000 | 0.000 |
| 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