Estimating Reservoir Permeability and Fracture Surface Area Using the Flowback DFIT (DFIT-FBA)
Bibliographic record
Abstract
Summary The main parameters of interest derived from a diagnostic fracture injection test (DFIT) are minimum in-situ stress, reservoir pressure, and permeability. The latter two can only be obtained uniquely from the transient reservoir responses, often requiring days to weeks of test time. The DFIT flowback analysis (DFIT-FBA) method, a sequence of pump-in/flowback (PIFB), is a fast alternative to the pump-in/falloff (conventional) DFIT for estimating minimum in-situ stress and reservoir pressure. Because the properties of the fracture are unknown, reservoir permeability cannot be estimated directly and therefore well productivity index (PI) has been reported in previous DFIT-FBA studies. The goal of the current study is to develop a methodology for estimating reservoir permeability and fracture properties from a DFIT-FBA test. In this study, a fully coupled hydraulic fracturing, reservoir, and wellbore simulator was used as a first step to identify critical mechanisms operating during the flowback period of a DFIT-FBA test. Subsequently, findings from the simulator were used to develop an analytical solution to estimate reservoir permeability, fracture surface area, open fracture stiffness, and contact pressure. The analytical model relies on a new rate-transient analysis (RTA) technique that accounts for the dynamic behavior of the fracture and changing leakoff rate during the before-closure period. The proposed approach was validated against a simulation case, and its practical application was demonstrated using a field example performed in a tight reservoir. The reservoir permeability and fracture surface area, derived from the analytical model at the contact point, agree within 2% of the simulation model input. The field example examined herein exhibited flow regimes similar to the simulation case, and fracture surface area, open fracture stiffness, contact pressure, minimum in-situ stress, reservoir pressure, and permeability were all obtained in a fraction of the time required by conventional DFITs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".