Introducing a Customized Workflow to Analyze DFIT-FBA Post-Flowback Shut-In (Rebound) Data
Bibliographic record
Abstract
Abstract DFIT-FBA (FBA = flowback analysis) offers a faster alternative to traditional diagnostic fracture injection tests (DFITs) for determining key parameters used in hydraulic fracturing design. While a method exists for estimating reservoir pressure from the flowback period of DFIT-FBA, this method could be inaccurate for certain cases. In this study, shut-in data (also called rebound) recorded after the flowback period of the DFIT-FBA test were analyzed to provide an independent estimate of reservoir pressure. An extensive DFIT-FBA post-flowback shut-in test was simulated and analyzed to interpret all pressure signatures. This detailed investigation provided valuable insights into physical mechanisms occurring during the post-flowback shut-in period of a DFIT-FBA test. Based on these findings, a customized analysis workflow was developed and then utilized to estimate reservoir pressure from post-flowback shut-in data for nine field cases. These results were then compared with the results obtained from the analysis of the flowback period of DFIT-FBA. Key observations from the simulation study include: 1) linear flow is the only reservoir flow regime observed during the post-DFIT-FBA shut-in data of the simulated case; 2) reservoir pressures estimated from the shut-in test are always higher than the actual reservoir pressure even after an extended shut-in period. The major takeaways from analyzing field examples include: 1) shut-in pressures recorded after a DFIT-FBA test reach a maximum, and will decline if pressure is monitored long enough; 2) more accurate reservoir pressure estimates will be obtained if the test is extended far beyond the point where maximum pressure is observed; 3) if the minimum horizontal stress and reservoir pressure are similar in magnitude, and/or occur close in time, the reservoir pressure signature during the flowback period of a DFIT-FBA could be masked by the fracture closure event. In such cases, the reservoir pressure estimate obtained from the analysis of DFIT-FBA flowback data should be used with caution, unless confirmed through independent estimates, such as DFIT-FBA post-flowback shut-in data.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".