Optimizing Deep Learning Models for Shear Wave Velocity Estimation Utilizing Petrophysical Logs: A Case Study on an Oil Reservoir in Southern Iran

Full identification and understanding of hydrocarbon reservoirs depends on knowing the mechanical properties. One of the main parameters that indicates mechanical properties is shear wave velocity. Bipolar sound recorder is among thee best tools for measuring shear wave velocity. This tool is not very popular due to the high costs of driving in the well despite the high accuracy. Shear wave velocity estimation methods include three main branches of experimental methods, regression and the use of machine learning algorithms or in other words artificial neural networks. The studied formation in this research is Sarvak in one of the oil fields in the south of Iran. The input data of the estimator model is the usual petrophysical logs that are driven and measured in many wells, and the output data is obtained from the DSI tool. In this research, data are pre-processed by removing noise effects. Then, to improve the estimation effectiveness, data with a high correlation coefficient are selected as input data. After that, shear wave velocity is estimated from petrophysical data with three types of multi-layer perceptron (MLP), multi-layer perceptron optimized by particle swarm optimization (MLP-PSO), and the introduction of a relatively new method of multi-layer perceptron-social ski drive (MLP-SSD). To compare the efficiency of the neural network method, two traditional experimental and regression methods used. The validation results show the better performance of the MLP-SSD method.