Profile Ultrasonic Velocity Measurements Performed on Slabbed Core: Implications for High-Resolution Permeability Prediction in Low-Permeability Rocks
Bibliographic record
Abstract
Abstract Calibration of reservoir models for unconventional hydrocarbon reservoirs requires permeability data as input. Accurate permeability prediction from velocity data is desirable due to the relative abundance of velocity data that is typically available during exploration and development programs (e.g., through seismic imaging and well logging). Therefore, the development of fast and inexpensive ‘screening’ techniques tha can provide reliable estimation of permeability at high-resolution (cm-scale) using velocity data could be valuable to exploration/development programs in unconventional reservoirs. A new experimental apparatus is described herein for measuring ultrasonic velocities (P- and S-wave) along the length of slabbed cores of low-permeability rocks at high-resolution (cm-scale). A statistical approach that combines along-core (profile) ultrasonic velocity testing and non-destructive experimental techniques (X-ray fluorescence, mechanical hardness, and profile permeability) is employed to develop predictive models for estimating permeability. Two slabbed cores from the Canadian Montney and Bakken formations, covering multiple geological intervals (tight siltstones/sandstones units), were analyzed for validation purposes. Reasonable agreement is found between log- and lab-derived (ultra)sonic velocity data, indicating similar trends with depth. However, the exact log- and lab-derived (ultra)sonic velocity values are different due to the differences in stress conditions between the field and laboratory measurements and the direction of wave travel. A maximum variation of ±20 m/s is observed for both P- and S-wave velocities when measurements were repeated on the same points, providing evidence of experimental repeatability and reproducibility. Relationships exist between laboratory-measured profile ultrasonic velocities (S-wave), profile permeability, mechanical hardness, and clay content (inferred from elemental composition data). The profile S-wave velocities decrease with increasing permeability (R2 = 0.6, n = 230). Advanced statistical methods (e.g., genetic algorithms) are employed to improve the velocity-permeability relationship and develop models for indirect estimation of permeability from S-wave velocities. The performance of these models is dependent upon lithology and rock fabric (e.g., silt vs. sand, degree of cementation), with a better correlation achieved for intervals with lower porosity and permeability (<±15% maximum discrepancy between measured and predicted permeability values; R2 = 0.78, n = 230). This study introduces a new experimental apparatus, and a practical ‘screening’ workflow, that can be used for permeability prediction using S-wave velocities collected on slabbed cores. This predictive model can be used to estimate permeability below the lower limit (0.001 md) of pressure-decay profile permeability measurements. The findings are beneficial to operators developing tight siltstone/sandstone resources by allowing them to characterize permeability in low-permeability (<0.001 md) intervals for applications such as optimizing stimulation design and subsurface fluid injection.
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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) | 0.000 | 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 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".