Investigation of Non-Ideal Diagnostic Fracture Injection Tests Behavior in Unconventional Reservoirs
Bibliographic record
Abstract
Abstract Diagnostic fracture injection test (DFIT) has become a valuable tool to quantify reservoir properties and hydraulic fracture characteristics. The pressure decline response of DFIT test reflects the process of fracture closure and the flow capacity of the reservoir. Previous literature provided simplifying assumptions to analysis the DFIT. However, operating companies often face challenges in the DFIT data interpretation due to several complex factors that result in non-ideal DFIT behavior and inconsistent results that lead to significant incorrect estimation of reservoir properties and fracturing parameters, including interaction with natural fractures, heterogeneous rock properties, variable storage, etc. The objective of this paper is to investigate the non-ideal DFIT behavior and factors that affect DFIT data and interpretations. The paper explained the flow regimes observed before closure and after closure during DFIT under complex reservoir conditions of natural fracture activation and fracture tip extension for reliable estimation of reservoir properties and fracture characteristic from actual field DFIT data. The overall fall-off period is analyzed using pressure transient analysis diagnostic plots and leak-off modeling. The transient pressure during the fall-off period is highly affected by the residual leak-off and continuing after flow that could disturb formation flow regimes during the test, affecting the ability to get correct pore pressure or formation permeability. The paper explains the various mechanisms affecting the pressure transient behavior during DFIT and adapts the wellbore and leak-off process to be able to observe reservoir response and get more realistic fracture characteristics and reservoir properties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".