A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir

Abstract

Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. The shear velocity prediction methods mainly include empirical formulas and theoretical rock physics models. When using the empirical formula method, calibration should be performed to fit the local data, and its accuracy is low. When using rock physics modeling, many parameters about the pure mineral must be optimized simultaneously. We present a deep learning method to predict shear velocity from several conventional logging curves in tight sandstone of the Sichuan Basin. The XGBoost algorithm has been used to automatically select the feature curves as the model’s input after quality control and cleaning of the input data. Then, we construct a deep-feed neuro network model (DFNN) and decompose the whole model training process into detailed steps. During the training process, parallel training and testing methods were used to control the reliability of the trained model. It was found that the prediction accuracy is higher than the empirical formula and the rock physics modeling method by well validation.

Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques

“…While these empirical relations have proved to be very useful, especially considering the cost of obtaining direct measurements of shear velocity, they are very often unreliable and inaccurate. Furthermore, they are very lacking when it comes to generalization, where several relations need to be developed to account for the various (Jiang et al, 2022). While these relations may have inherent limitations and varying applicability across geological settings, they serve as efficient shortcuts in the absence of direct shear velocity measurements, aiding in the interpretation and understanding of subsurface structures and rock properties during exploration and development endeavors (Fabricio et al, 2015).…”

“…Due to the potential improvement in the shear wave velocity prediction in complex reservoirs, data-driven models, analytical, rock physics and ML models have been used in several studies over the past two decades (Shawaf et al, 2022). One of the recent studies on slowness prediction using ML algorithms was conducted by (Jiang et al, 2022) who compared the shear wave velocity obtained from logging tools with the one obtained using empirical models. The authors then applied a novel method of deep learning (DL) to predict the shear wave velocity in a tight sandstone reservoir and found that the prediction accuracy is higher than the empirical equations (Zhang et al, 2020) tested theoretical, petrophysical, and machine learning models in a complex carbonate reservoir using conventional logs.…”

Exploring Shear Wave Velocity—NSPT Correlations for Geotechnical Site Characterization: A Review

“…The validation of these correlations involves field investigations where both N SPT data and direct measurements of V s are collected at specific sites [122]. These data are used to test the accuracy of the correlations by comparing predicted V s values with measured V s values.…”

Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism

“…In addition, the relationship between S-wave velocity and its influencing variables is often non-linear. This renders traditional empirical formulas inaccurate in real-world applications and unable to achieve the desired prediction accuracy of S-wave velocity even for full waveform inversion (Oh et al, 2018;Jiang et al, 2022;Rajabi et al, 2022).…”