Multifocusing 3D diffraction imaging for detection of fractured zones in mudstone reservoirs: Case history
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".