Predicting Uniaxial Compressive Strength From Empirical Relationships Between Ultrasonic P-Wave Velocities, Porosity, and Core Measurements in a Potential Geothermal Reservoir, Snake River Plain, Idaho
Bibliographic record
Abstract
Empirical, core-based, predictive correlations for the calculation of uniaxial compressive strength (UCS) were developed from compressive sonic velocity measurements (55 whole core samples) and porosity, density, and unconfined compressive tests (110 whole core samples). The samples were collected at 55 different depths in a 550 m (1800 ft) interval of core from the MH-2 borehole located in southern Idaho, USA. The western Snake River Plain is a known region of high heat flow and the borehole was drilled into a potential geothermal reservoir characterized by artesian flow of high-temperature (~140°C) fluids from fractured basalt. UCS was measured in unconfined compressive tests, density and porosity were measured using a He-pycnometer, and p-wave velocities were measured in a pressure vessel under variable confining pressures. We use correlations between density and p-wave velocity (R2 = 0.91), UCS and porosity (R2 = 0.78), and UCS and p-wave velocity (R2 = 0.79) in a method to calculate calibrated UCS from wireline logs. Here, the impact of this predictive correlation is that UCS can be calculated from as little as a bulk density log when sonic logs and core are not available, greatly increasing the number of wells in which we can obtain a local UCS estimate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".