Dual‐Porosity Flow Diagnostics for Spontaneous Imbibition in Naturally Fractured Reservoirs
Bibliographic record
Abstract
Abstract We extend single‐porosity flow diagnostics to dual‐porosity systems using a novel retardation factor R to account for the effect of fracture‐matrix transfer on breakthrough times and displacement efficiency during two‐phase flow in fractured reservoirs. R is based on an analytical solution for capillary‐driven fluid exchange between the fractures and rock matrix. By linearizing R the time‐of‐flight τ⋆ is adjusted to include fracture‐matrix transfer and derive new metrics, the dynamic Lorenz coefficient to quantify the dynamic heterogeneity, and the dual‐porosity sweep efficiency Ev⋆ to estimate how efficiently the injected fluid displaces the reservoir fluid over time. We have tested different formulations of R across three case studies with increasing complexity to analyze the applicability and limitations of dual‐porosity flow diagnostics. This analysis reveals that as long as flow in the fractures is faster than fracture‐matrix transfer, dual‐porosity flow diagnostics provide useful approximations when assessing displacement efficiencies and identifying the wells that are at most and least likely to experience early breakthrough. We show that and Ev⋆ can be combined with stochastic optimization algorithms to improve the displacement efficiency in a 3D reservoir case study. Since a single dual‐porosity flow diagnostics calculation requires less than 1 min while a full‐physics simulation takes 2 h, we can now quickly screen a large parameter space to identify scenarios that need to be studied in more detail using full‐physics simulations. Hence, our new dual‐porosity flow diagnostics complement and accelerate state‐of‐the‐art uncertainty quantification and optimization workflows for fractured reservoirs.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".