Diffraction imaging in fractured carbonates and unconventional shales
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Diffraction imaging is recognized as a new approach to image small-scale fractures in shale and carbonate reservoirs. By identifying the areas with increased natural fracture density, reservoir engineers can design an optimal well placement program that targets the sweet spots (areas with increased production), and minimizes the total number of wells used for a prospective area. High-resolution imaging of the small-scale fractures in shale reservoirs such as Eagle Ford, Bakken, Utica, and Woodbine in the US, and Horn River, Montney, and Utica in Canada improves the prospect characterization and predrill assessment of the geologic conditions, improves the production and recovery efficiency, reduces field development cost, and decreases the environmental impact of developing the field by using fewer wells to optimally produce the reservoir. We evaluated several field data examples using a method of obtaining images of diffractors using specularity filtering that could be performed in depth and time migration. Provided that a good migration velocity was available, we used the deviation of ray scattering from Snell’s law to attenuate reflection energy in the migrated image. The resulting diffraction images reveal much of the structural detail that was previously obscured by reflection energy.
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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 it