An unsupervised deep-learning method for porosity estimation based on poststack seismic data
We propose to invert reservoir porosity from poststack seismic data using an innovative
approach based on deep-learning methods. We develop an unsupervised approach to …
approach based on deep-learning methods. We develop an unsupervised approach to …
Gas channels and chimneys prediction using artificial neural networks and multi-seismic attributes, offshore West Nile Delta, Egypt
Abstract Machine learning techniques combined with multi-seismic attributes and well logs
datasets have been successfully used in reducing the risk of drilling operations and …
datasets have been successfully used in reducing the risk of drilling operations and …
Uncertainty estimation in AVO inversion using Bayesian dropout based deep learning
C Junhwan, O Seokmin, B Joongmoo - Journal of Petroleum Science and …, 2022 - Elsevier
Amplitude versus offset (AVO) inversion is the process of transforming seismic reflection into
elastic properties such as P-and S-impedance to estimate the interval properties and …
elastic properties such as P-and S-impedance to estimate the interval properties and …
Multi-parameter pre-stack seismic inversion based on deep learning with sparse reflection coefficient constraints
D Cao, Y Su, R Cui - Journal of Petroleum Science and Engineering, 2022 - Elsevier
In the field of seismic inversion, Convolutional Neural Network (CNN) has been extensively
applied for their powerful capability of feature extraction and nonlinear fitting. However, the …
applied for their powerful capability of feature extraction and nonlinear fitting. However, the …
Review of machine learning methods applied to enhanced geothermal systems
L Wang, Z Yu, Y Zhang, P Yao - Environmental Earth Sciences, 2023 - Springer
The objective of this study was to summarize the progress in the application of machine
learning (ML) to enhanced geothermal systems (EGSs), including the entire process of EGS …
learning (ML) to enhanced geothermal systems (EGSs), including the entire process of EGS …
AVO inversion based on closed-loop multitask conditional Wasserstein generative adversarial network
Z Wang, S Wang, C Zhou… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Neural networks are commonly used for poststack and prestack seismic inversion. With
sufficient labeled data, the neural network-based seismic inversion results are more …
sufficient labeled data, the neural network-based seismic inversion results are more …
Semi-supervised learning seismic inversion based on spatio-temporal sequence residual modeling neural network
The Spatio-temporal sequence residual modeling neural network (STSRM-net) is built to
address the geophysical problem of obtaining P-impedance of the subsurface from the zero …
address the geophysical problem of obtaining P-impedance of the subsurface from the zero …
Seismic impedance inversion based on deep learning with geophysical constraints
Seismic inversion plays an essential role in the exploration and development of oil and gas
reservoirs. With the development of neural networks, deep learning has achieved a wide …
reservoirs. With the development of neural networks, deep learning has achieved a wide …
Spatial Pattern Learning: Dip Structure Constraint Multi-view Convolutional Neural Network for Pre-stacked Seismic Inversion
Seismic elastic parameters inversion is a method to predict the geophysical reservoir
parameters, including P-wave velocity, S-wave velocity, and density, by using pre-stacked …
parameters, including P-wave velocity, S-wave velocity, and density, by using pre-stacked …
A dynamic time warping loss-based closed-loop CNN for seismic impedance inversion
Deep learning (DL) methods have been widely applied in seismic inversion. However, one
of the major challenges for DL-based seismic inversion is the time-shifted well-logging …
of the major challenges for DL-based seismic inversion is the time-shifted well-logging …