Search

Shear wave velocity is an essential parameter in reservoir characterization and evaluation, fluid identification and prestack inversion. However, conventional data‐driven or model‐driven shear wave velocity prediction methods exhibit several limitations, such as lack of training data sets, poor model generalization and weak model robustness. In this study, a model‐ and data‐driven approach is presented to facilitate the solution of these problems. We develop a theoretical rock physics model for fractured limestone reservoirs and then use the model to generate synthetic data that incorporates geological and geophysical knowledge. The synthetic data with random noise is utilized as the training data set for the artificial neural network, and a well‐trained shear wave velocity prediction model, random noise shear wave velocity prediction neural network, is established by parameter tuning, which fits the synthetic data with noise well. The neural network is applied directly to the real field area. Compared with conventional shear wave prediction methods, such as empirical formulas and the improved Xu–White model, the prediction results show that the random noise shear wave velocity prediction neural network has better prediction performance and generalization. Furthermore, the prediction results demonstrate the efficacy of the proposed approach, and the approach has the potential to perform shear wave velocity prediction in real areas where training data sets are unavailable. [ABSTRACT FROM AUTHOR]