NUMERICAL STUDY OF PORE STRUCTURE EFFECTS ON ACOUSTIC LOGGING DATA IN THE BOREHOLE ENVIRONMENT
Bibliographic record
Abstract
Existing methods of well-logging interpretation often contain errors in the exploration and evaluation of carbonate reservoirs due to the complex pore structures. The differences in frequency ranges and measurement methods deviated between the acoustic well logs and indoor ultrasonic tests cause inconsistent results. Based on the elastic wave equation and the principle of the control variable method, a 2D axisymmetric borehole model with complex pore structures was developed, and the numerical simulation method for acoustic log was constructed. The modeling results show that the power function can well describe the effects of pore structure on the acoustic waves, while the velocity of the Stoneley wave is not sensitive to the pore structure. Crack-like pores with pore aspect ratio (AR) less than 0.1 significantly affect the velocities of P- and S-waves, whereas “spherical” pores have fewer effects. The models with larger pore sizes have high velocities of P- and S-waves. The velocities calculated by the equivalent medium theory are always higher than the numerical simulation results. The velocity deviation caused by the difference in frequency is much smaller than the pore structure. A fractal approach to quantify the effects of pore structures is applied in the acoustic logging data. The fractal dimension increases with the pore AR or size when the porosity is constant, which can be described by a simple power function. This gives us new ideas and methods for pore structure evaluation in the lower frequency range than the conventional petrophysical 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".