Orthorhombic Velocity Model Building for
Bibliographic record
Abstract
Seismic anisotropy of shales play a crucial role in microseismic processing, which can influence the location of microseismic event, the inversion and interpretation of source mechanism and further affect the estimates of fracture geometry and simulated volume. The primary source of shale anisotropy are the bedding-parallel alignments of clay mineral as well as kerogen particles in organic-rich shales, which is usually known as intrinsic or fabric anisotropy and represented by TI model. Another important source of shale anisotropy are preferred-oriented fractures in macroscopic scale, which are generally induced by local stress field and result in azimuthal anisotropy. These two types of shale anisotropy can be represented by orthorhombic velocity model systematically. As revealed in rock physics experiments, intrinsic or fabric anisotropy of shale are closely related to mineral compositions. Clay mineral and organic material with platy particles and softer elastic properties significantly increase the intrinsic anisotropy of shale, whereas quartz content with non-platy and stiffer grains largely weaken the degree of shale anisotropy. According to fracture mechanics, preferred orientations of fracture are parallel to the maximum horizontal stress. So the symmetry planes of orthorhombic model can be determined approximately by regional stress distribution. With the constraints of rock physics studies and geological setting, reasonable initial model is built and further optimization is implemented by simultaneous inversion of microseismic data. This approach is applied to a dataset from Horn River shale gas reservoir, Northeastern British Columbia, Canada. The optimized orthorhombic velocity model is consistent with rock physics studies and remarkably reduces the time misfit compared to originally provided anisotropic velocity model. As a quality control, the locations of perforation shots are well restore with the optimized orthorhombic velocity model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".