Method and application of fracture width calculation based on formation contrast variation index
Bibliographic record
Abstract
ABSTRACT Accurate calculation of fracture width is essential for characterizing fractured hydrocarbon reservoirs. However, this remains challenging when relying on geophysical logging alone. The results of fracture width calculations vary considerably when the same fracture is evaluated using electrical imaging logging under different formation contrasts, which are defined by the ratio of mud resistivity to flushed zone resistivity. This makes it difficult to perform multi-well comparisons accurately for the evaluated fracture width in practical applications. A logging evaluation method for fracture width was proposed based on physical experiment studies. First, we constructed a physical model of a granite formation containing micrometer-scale irregular fractures that can be used to capture data with the electrical imaging logging instrument and measured the true fracture widths in the physical model. Due to experimental constraints, the fractures of the physical model are open fractures with a certain degree of roughness. Formation microscanner image logging data were acquired in the physical model under five different mud salinity conditions and were subsequently processed and analyzed. Finally, the fracture width evaluation method was proposed by introducing the formation contrast variation index, a new parameter that accounts for resistivity contrast effects. This proposed method can accurately calculate fracture width when drilling in different mud salinity conditions. Validation through experimental data and case studies demonstrates its robustness and practical applicability. This approach provides a reliable foundation for the objective calculation of fracture porosity and fracture permeability, thereby supporting more effective exploration and development of fractured hydrocarbon 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".