Investigating the Effect of Capillary Pressure on DFIT and DFIT-FBA Analysis
Bibliographic record
Abstract
Abstract The diagnostic fracture injection test (DFIT), and the new variation DFIT-FBA (FBA = flowback analysis), are well-testing methods performed before the main hydraulic fracturing operations to obtain several key parameters used in hydraulic fracture design and for reservoir simulation input. The classic (conventional) DFIT includes the sequence of pump-in, followed by a long shut-in (hours to days), while DFIT-FBA utilizes the sequence of pump-in, followed by a brief (minutes) shut-in, and then flowback to accelerate the time to reach closure and obtain reservoir pressure. While DFITs are still widely implemented, DFIT-FBA has the advantage that key properties can be derived in a matter of 2-3 hours versus (typically) more than a day for a conventional DFIT. The current models used to estimate reservoir parameters from DFIT and DFIT-FBA require calculating the fracturing fluid leakoff volume into the reservoir. While mechanisms affecting leakoff include viscous, capillary, and osmotic forces, current DFIT models only consider viscous forces. While the effect of the capillary pressure on fluid leakoff has been explored and confirmed by multiple researchers, it has not been incorporated into models and software for hydraulic fracture modelling or DFIT/DFIT-FBA analysis. An important question addressed in this work is whether the capillary pressure effect is significant over the short timeframe of the DFIT/DFIT-FBA test. Simulation results generated herein demonstrate that capillary pressure plays a significant role in the leakoff of hydraulic fracturing fluid into the reservoir during DFIT/DFIT-FBA tests; therefore, neglecting the effect of capillary pressure in the analyses can lead to substantial errors in reservoir parameter determination. Numerical simulation results also demonstrate that the presence of capillary forces accelerates leakoff and consequently the time of closure. For a simulated DFIT-FBA executed in an unconventional gas reservoir, approximately 25% of the total leakoff volume is attributable to capillary forces. Ignoring the effect of capillary pressure results in ~77% overestimation of reservoir permeability. Similarly, for a simulated DFIT case, this contribution is up to 26.5%, leading to ~77% overestimation of reservoir permeability. A sensitivity analysis performed herein underscores the significance of considering interfacial tension and contact angle, while reservoir permeability has a relative influence on the outcomes. Consequently, the early-time leakoff permeability estimated from DFIT/DFIT-FBA tests with current models is more precise for situations where there is reduced interfacial tension between the rock and fracturing fluid, and/or when the rock demonstrates mixed wettability.
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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 | 0.000 |
| 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) | 0.000 | 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 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".