Executing Minifrac Tests and Interpreting After-Closure Data for Determining Reservoir Characteristics in Unconventional Reservoirs
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".