Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model-Reference-Cited by-同舟云学术

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Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model

Published:2024-09 Issue: Volume:240 Page:213028
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Fang Zhijian,Ba Jing,Guo Qiang^ORCID,Xiong Fansheng

Publisher

Elsevier BV

Reference83 articles.

1. Semisupervised sequence modeling for elastic impedance inversion;Alfarraj;Interpretation,2019

2. Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: a case study from Ab-Teymour Oilfield;Anemangely;J. Nat. Gas Sci. Eng.,2017

3. Forecasting of COVID-19 using deep layer recurrent neural networks (RNNs) with gated recurrent units (GRUs) and long short-term memory (LSTM) cells;ArunKumar;Chaos, Solit. Fractals,2021

4. Rock physics model-based prediction of shear wave velocity utilizing machine learning technique for a carbonate reservoir, southwest Iran;Azadpour;J. Petrol. Sci. Eng.,2020

5. Biot-Rayleigh theory of wave propagation in double-porosity media;Ba;J. Geophys. Res. Solid Earth,2011

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Permeability prediction using logging data from tight reservoirs based on deep neural networks;Journal of Applied Geophysics;2024-10

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