Multifocusing 3D diffraction imaging for detection of fractured zones in mudstone reservoirs: Case history
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Diffracted waves are generated by a wave incident on a subsurface obstruction (of a size less than a seismic wavelength) that acts as a point source, scattering the wave in all directions. In the most general terms, diffractors are points and edges and they typically appear in the subsurface as faults, steep reef edges, karsts, or extensive systems of well-developed fractures. Diffracted waves are rarely imaged in sufficient detail for interpretation because they have low amplitudes compared to the reflectivity data, and standard processing flows are not optimized for them. Diffraction imaging, in the form of diffraction multifocusing, is a seismic processing technique that separates the recorded diffractions from the specular reflections (waves reflected from a smooth surface that obey Snell’s law). A 3D volume of the semblance of the diffracted energy can be created and interpreted to indicate the presence of the diffractors. We applied diffraction imaging to seismic data acquired above a fractured mudstone oil reservoir. In an unconventional reservoir, there may be additional hydrocarbon storage or permeability in the fractures that could affect drilling, completions, and production. We mapped the diffraction energy at the reservoir level and correlated it with rates of initial production of the wells. In addition, the diffraction imaging amplitudes were qualitatively related to gas shows encountered during drilling and may be used to predict the relative increase or decrease in gas shows in this reservoir. The ability to predict the presence of natural fractures allowed us to spatially locate well trajectories and may impact decisions regarding well operations and completions.
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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