Rapid Formation Evaluation in the Permian Basin Using Different DFIT-FBA/Rebound Approaches: Design, Field Execution, and Validation Against Traditional DFIT
Bibliographic record
Abstract
Abstract The traditional Diagnostic Fracture Injection Testing (DFIT) method is widely used in low-permeability unconventional reservoirs to measure key formation properties such as minimum in-situ stress and pore pressure. However, the DFIT test typically takes two to four weeks, limiting its application due to rig time, equipment costs, and interference with the fracturing schedule. This paper utilizes the combination of DFIT-FBA (flowback analysis) and post-flowback shut-in (rebound) data as an alternative technique applicable in the Permian Basin. The combined DFIT-FBA/Rebound analysis method involves fluid injection, a brief controlled flowback, and a shut-in period of a few hours. This combined approach yields key formation parameters such as minimum in-situ stress and pore pressure within just a few hours, at a significantly lower cost than DFIT while maintaining accuracy. Two sets of rigorous pilots were designed and carefully executed to verify and modify the DFIT-FBA/Rebound approach. The first pilot included both traditional DFIT and DFIT-FBA/Rebound tests conducted at the toe section of the same well, separated by a plug, allowing direct comparison between the two methods. The DFIT-FBA portion of the test was designed to obtain minimum in-situ stress and pore pressure during the flowback period, while the rebound test was used to obtain an independent estimate of pore pressure. The results of the DFIT-FBA/Rebound analysis provided minimum in-situ stress and pore pressure estimates that are quite similar to the conventional DFIT, thus validating the DFIT-FBA/Rebound method accuracy. However, obtaining a reliable pore pressure estimate from rebound data required between 1 to 4 days. Based on learnings from the first pilot, the procedure for the second pilot increased the flowback rate and shortened the flowback period to accelerate the time to obtain a pore pressure estimate from the rebound test. Results from the second pilot demonstrated that minimum in-situ stress could be determined within 1 hour from the start of flowback, and pore pressure could be estimated after an additional 6 hours. While optimization of the test design is ongoing, the proposed rapid, cost-effective, and easy-to-implement DFIT-FBA/Rebound analysis approach has great potential to enhance testing capabilities across all unconventional assets.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".