Field experience and numerical investigations of minifrac tests with flowback in low-permeability formations
Bibliographic record
Abstract
In this study, flowback-assisted minifrac tests were conducted in low-permeability shale and salt formations to measure the in situ stress. An injection/flowback testing protocol was implemented in each test to achieve accuracy and efficiency. Accurate and efficient injection/flowback testing is very important, given the impermeable nature of these formations and the need to complete each test as quickly as possible. Each flowback cycle yields a distinct and repeatable fracture closure signature, simplifying the interpretation of the fracture closure pressure. The objective of this paper is to share our field experience and to present a numerical analysis of the flowback test pressure responses, fracture closure behaviors, and fracture closure diagnostic methods. Examples from open-hole and cased-hole minifrac tests are used to demonstrate site operation procedures. Then, two numerical models are presented for simulating the fracture closure behavior during a flowback test. Field evidence is provided to demonstrate that the fracture closure pressures from the flowback tests are identical to those from tests without flowback. The fracture closure diagnostic methods for flowback tests are discussed, and it is found that the G-function diagnostic method yields a distinct fracture closure signal during the flowback tests. This study is intended to provide additional insights regarding flowback tests by sharing our successes, experience, and knowledge, thereby benefiting the industry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".