A new rock physics model to estimate shear velocity log

Shear velocity log is an important input during the reservoir characterization. It is widely used during seismic reservoir characterization such as lithology, porosity and fluid estimation, four-dimensional seismic studies, geomechanical and wellbore stability studies. Shear velocity log is not necessarily acquired in all wells due to the costs. Therefore, different methods have been developed to estimate it using other logs. Methods such as empirical relations, multi-regression analysis, artificial neural networks, and rock physic modelling have been widely used for this purpose. In this study, the shear velocity log is estimated using empirical relations, multi-regression analysis, and artificial neural networks in one of the gas reservoirs in south of Iran. In addition, utilizing rock physics models, an equation is developed for this purpose. For empirical relations, Castagna and Brother's relations were employed. Empirical relations estimated the shear velocity log with significant error. Multi-regression analysis was carried out employing three logs: Compressional velocity, density and porosity. In the artificial neural network, the training is performed by the Levenberg-Marquardt algorithm with four input layers, ten hidden layers and one target layer. The outcome of multi-regression and ANN estimations are compared well with the measured shear velocity log. Finally, an equation is developed using rock physics concepts to estimate the shear velocity log. The linear form of Gassmann's relation is used on the first step. The small and negligible terms are eliminated to simplify the equation. This equation requires compressional velocity and density logs. It also needs bulk modulus of rock forming mineral and pore filling fluids as well as porosity that are commonly known in oil and gas reservoirs. Utilizing this equation, shear velocity log is estimated in our case study. The average difference between the measured and estimated shear velocity log using our method is 10 m/s and 0.33%, respectively. Since, this equation considers the impact of mineral and fluids, we predict that it could be applicable in most of geological environments.

Automated summary

Shear velocity log is crucial for reservoir characterization and various methods including empirical relations, multi-regression analysis, and artificial neural networks are used to estimate it. This study in a gas reservoir in Iran utilized empirical relations, multi-regression analysis, and ANN to estimate shear velocity log. Additionally, a rock physics-based equation was developed to estimate shear velocity log with good accuracy.