TY - JOUR ID - 143633 TI - Prediction of Shear Wave Velocity by Extreme Learning Machine Technique from Well Log Data JO - نشریه علمی ژئومکانیک نفت JA - JPG LA - fa SN - 2538-4651 AU - Rajabi, Meysam AU - Ghorbani, Hamzeh AU - Khezerloo-ye Aghdam, Saeed AD - Departman of Mining Engineering, Birjand University of Technology, Birjand, Iran AD - Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran AD - Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran Y1 - 2022 PY - 2022 VL - 4 IS - 3 SP - 35 EP - 49 KW - Shear wave velocity KW - ELM KW - MLP KW - Machine Learning KW - Well Log Data DO - 10.22107/jpg.2022.298520.1151 N2 - Shear wave velocity (Vs) is one of the key geomechanical parameters effective in the drilling of hydrocarbon reservoirs. In this study, a novel machine learning (extra learning machine (ELM)) approach is developed to predict Vs based on four input variables obtained from well log, including neutron porosity (NPHI), bulk density (RHOB) and gamma-ray (GR). Two algorithms multi-layer perceptron (MLP) and ELM and various empirical equations (Brocher, Eskandari et al., Castagna et al. and Pickett) have been used to predict Vs in this paper. The results show that the performance accuracy for these models includes: ELM> MLP> Castagna et al. > Eskandari et al. > Pickett> Brocher. So, the result that shows the ELM model has higher accuracy than the other machine learning (MLP) approach and also other empirical equations (RMSE = 0.0444 km/s and R2 = 0.9809). Some advantages to the other artificial neural network approach include higher accuracy and performance characteristics, simple algorithm learning, improved performance, nonlinear conversion during training, no stuck in local optimal points, and it is over fitting. The novelty used in this paper is the type of newly implemented artificial model (ELM) and the number of input parameter. This approach possesses to the higher power, speed and accuracy than the methods used by other researchers to predict Vs. UR - http://www.irpga-journal.ir/article_143633.html L1 - http://www.irpga-journal.ir/article_143633_2dbef0e787a6bc0eb977da9a45fb2f87.pdf ER -