Calculating Equivalent Fracture Network Permeability of Multi-Layer-Complex Naturally Fractured Reservoirs
Bibliographic record
Abstract
Modeling naturally fractured reservoirs (NFRs) requires an accurate representation of fracture network permeability. Conventionally, logs, cores, seismic, and pressure transient tests are used as data base for this. Our previous attempts showed that a strong correlation exits between the fractal parameters of 2-D fracture networks and their permeability (Jafari and Babadagli, 2008, 2009a). We also showed that 1-D well (cores-logs) and 3-D reservoir data (well test) may not be sufficient in fracture network permeability (FNP) mapping and 2-D (outcrop) characteristics are needed (Jafari and Babadagli, 2009b). This paper is an extension of these studies where only 2-D (single layer, uniform fracture characteristics in z-direction) representations were used.In this paper, we considered a more complex and realistic 3-D network system. 2-D random fractures with known fractal and statistical characteristics were distributed in the x-, and y-directions. Variation of fracture network characteristics in the z-direction was presented by a multi layer system representing three different facieses with different fracture properties. Wells were placed in different locations of the model to collect 1-D fracture density and pressure transient data. In addition, five different fractal and statistical properties of the network of each layer were measured.The equivalent fracture network permeability (FNP) was calculated using a commercial software package as the base case. Using available 1-D, 2-D, and 3-D data, multivariable regression analyses were performed to obtain equivalent FNP correlations for many different fracture network realizations. The derived equations were validated against a new set of synthetic fracture networks and conditions at which 1-D, 2-D and 3-D are sufficient to map fracture network permeability were determined. The importance of the inclusion of each data type i.e. 1-D, 2-D and 3-D, in the correlations was discussed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".