Bibliographic record
Abstract
Diagnostic Fracture Injection Tests (DFITs) have become commonplace in low-permeability (unconventional) reservoirs to obtain parameters used in hydraulic fracture stimulation design and reservoir characterization including minimum in-situ stress, initial reservoir pressure and reservoir permeability. The current understanding of the parameters that impact successful DFIT design and analysis is limited. A DFIT exhibits very complex physical behavior, with various mechanisms active at the same time, including those related to wellbore, fracture, leakoff and reservoir flow. Therefore, the observed trends in field data are not often predicted using existing analytical methods, and some common signatures cannot be interpreted. This underscores the need for a systematic simulation study of DFIT responses where all the active mechanisms are captured simultaneously. Furthermore, the required shut-in time to acquire reliable DFIT data for estimation of minimum in-situ stress and reservoir pressure may be excessive, ranging from days to weeks or months. In this study, a fit-for-purpose coupled reservoir-geomechanics model is used to simulate DFITs and generate synthetic pressure responses under various conditions. The validity of the simulation model is confirmed by comparison to field data. Progressive fracture closure is presented as an alternative closure mechanism, and the primary pressure derivative (PPD) is identified as a powerful tool to estimate fracture closure. The effect of wellbore storage, leakoff rate and dynamic fracture geometry on pressure response is investigated, and their signatures are identified. These findings are used to explain and analyze field data in major unconventional plays in western Canada. In order to accelerate the test and reduce shut-in time, a new DFIT procedure which combines the injection period with an ultra-low rate flowback is presented. Two successful field trials of this modified procedure are reported in this work. Finally, a conceptual method is presented for estimation of reservoir pressure in pump-in/flowback tests. This method utilizes rate transient analysis techniques to account for variations in pressure and flowback rate. This method is validated with numerical simulation and a field trial.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".