SHEAR WAVE MODELLING FROM CONVENTIONAL WELL LOGS USING INTEGRATED DEEP LEARNING INTEGRATED CONVOLUTIONAL NEURAL NETWORK (I-CNN)

Abstract

Shear-wave velocity (Vs), together with compressional wave velocity, provides a crucial source of information for both geomechanical and geophysical studies. Vs data are often unavailable. Moreover, direct measurement of Vs remains relatively costly. Four machine learning algorithms were created to predict Vs from traditional well logs in order to get around these restrictions: Probability Neural Network (PNN), Multilayer Feed-Forward Neural Network (MLFFNN), Deep Feed-Forward Neural Network (DFFNN), one-dimensional Convolutional Neural Network (1D-CNN), and an Integrated Convolutional Neural Network (I-CNN). The dataset consists of two wells (19,121 data points were gathered) of authentic industrial wireline logs from two anonymized wells, provided with formal authorization from the data owner exclusively for academic research purposes, including model training, testing, and validation. There were three primary parts in the methodology: (1) pre-processing the data to get rid of noise and change it into the right format; (2) using domain knowledge to drive feature engineering and selection; and (3) training, testing, and optimizing the model. The results demonstrated that the I-CNN model in RCW-1 well achieved the best performance, with an R2 value of 0.971. When applied to the blind well (RCW-2), the I-CNN model maintained strong generalization capability, achieving an average R2 value of 0.956. These findings indicate that the I-CNN outperforms other methods in handling complex, nonlinear relationships in Vs prediction. Overall, this study contributes to the growing body of literature on machine learning applications in petrophysical analysis by introducing an integrated deep learning framework that surpasses traditional approaches.