Attenuation and velocity estimation using rock physics and neural network methods for calibrating reflection seismograms
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Bibliographic record
Abstract
Abstract Velocity logs are the most important data used to evaluate rock, fluid, and geotechnical properties of hydrocarbon reservoirs. As a complementary physical property, P-wave attenuation (Q−1) can be used as an indicator of lithology and fluid saturation in oil and gas reservoir characterization. We implemented an inversion self-consistent rock physical model to predict P- and S-wave velocities in two old wells near a new well containing a complete suite of logs at the Waggoner Ranch oil reservoir in northeast Texas. We selected a training data set from the new well to test the algorithm that was subsequently applied to predict velocity data in the two old wells. We used an attenuation log from the new well to perform data analysis via the Gamma test, a mathematically nonparametric nonlinear smooth modeling tool, to choose the best input combination of well logs to train an artificial neural network (NN) for estimating Q−1. Then, the NN was applied to predict attenuation logs in the old wells. The Q−1 logs detected oil-saturated sand that was modeled with a rock physical model. This is a significant result that revealed for the first time that oil, gas, and water saturations of sand can be quantified from an attenuation anomaly estimated from full-waveform sonic data. In addition, water, oil, and gas saturations of the sand were determined from Q−1 anomalies observed in the old wells. This confirms the productivity of the Upper Milham oil-saturated sand intercepted by the three wells. The velocity, density, and Q−1 logs were used to generate synthetic seismograms to calibrate seismic data to verify and evaluate the work flow for predicting velocity and attenuation logs in older wells. This demonstrated that attenuation logs can discriminate between anomalies due to lithology and those due to oil and gas saturation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it