Evaluation of Shear Wave Velocity Prediction Models at Norne Field, Norwegian Sea



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Several shear-wave velocity (Vs) prediction models have been tested on wireline log data at Norne Field in the Norwegian Sea. A genetic algorithm was used to invert P-wave velocity (Vp) for the elastic parameters using the Krief, Self-consistent (SC), and Differential Effective Medium (DEM) models. The inverted shear moduli were then used to predict Vs. Using this method, the Krief method provided the best match of the effective medium models to the measured Vs. Error analysis shows that the predicted Vs is largely correlated with Vp, density, and porosity. Higher Vp, higher density, and lower porosity tend to produce the largest prediction error. These predictions were compared to other well-established Vs prediction models and the effect of these predictions on AVO modeling was investigated. It is shown that the AVO response begins to show noticeable difference at small Vs errors. For example, the DEM prediction at the oil saturated well had a 6.8% error from the measured Vs at the AVO modeled interval, and AVO mismatch begins at around 15 degrees offset. At the brine saturated well, the Krief, Greenberg-Castagna, and Raymer-Hunt-Gardner (RHG) Vs predictions provided the closest match to the true AVO model while at the oil saturated well, the Krief, RHG, and Han Vs predictions provided the best match to the true AVO model.



Shear wave velocity, Sonic logs, Genetic algorithms