Investigation of Non-Ideal Diagnostic Fracture Injection Tests Behavior in Unconventional Reservoirs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it