Seismic inversion for reservoir facies under geologically realistic prior uncertainty with 3D convolutional neural networks

Pradhan, Anshuman; Mukerji, Tapan

doi:10.1190/segam2020-3426944.1

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Three convolutional deep neural network models were featured in this study. All three models showed good results when applied to seismic data [13,21,22]; however, they have different numbers of parameters and costs of training. To understand advantages and disadvantages of the architectures when applied to different datasets, three deep learning architectures were compared in this study, and their descriptions are given in this section.…”

Section: Architecturesmentioning

confidence: 99%

Section: Fully Convolutional 2d Network With Dilated Convolutional Layersmentioning

confidence: 99%

“…The fully convolutional 2D network with dilated convolutional layers architecture was based on [21]. In [21], the authors employ a 3D model, while in this work we used a 2D version of this model. The network consisted of two parts: the first part was based on ordinary 2D convolutional layers and the second part was built with 2D convolutional layers with a dilation factor [23] that essentially controlled the increase of the field of view of convolutional filters.…”

Section: Fully Convolutional 2d Network With Dilated Convolutional Layersmentioning

confidence: 99%

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Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning

et al. 2022

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The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural networks have started to gain momentum. Convolutional neural networks are particularly efficient at pattern recognition within images, and this is why they are suitable for seismic facies classification and interpretation tasks. We experimented with three different architectures based on convolutional layers and compared them with different synthetic and field datasets in terms of quality of the seismic interpretation results and computational efficiency. The architectures used in our study were three deep fully convolutional architectures: a 3D convolutional network with a fully connected head; a 2D fully convolutional network, and U-Net. We found the U-Net architecture to be both robust and the fastest when performing classification at the prediction stage. The 3D convolutional model with a fully connected head was the slowest, while a fully convolutional model was unstable in its predictions.

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Section: Architecturesmentioning

confidence: 99%

Section: Fully Convolutional 2d Network With Dilated Convolutional Layersmentioning

confidence: 99%

Section: Fully Convolutional 2d Network With Dilated Convolutional Layersmentioning

confidence: 99%

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Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning

et al. 2022

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A comprehensive review of seismic inversion based on neural networks

Li,

Yan,

Zhang

2023

Earth Sci Inform

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Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network Regression

Pradhan,

Adams,

Chandrasekaran

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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Modeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low‐quality well data which is sparsely and noisily sampled across time and space. The lack of consistent well data makes it difficult to evaluate the impact of 2017 and 2019 wet years on CV groundwater following a severe drought during 2012–2015. A novel machine learning method is formulated for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). The hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non‐parametric regression with GP is performed. We demonstrate the efficacy of GP‐DNN regression for modeling non‐stationary features in the well data with fast and reliable uncertainty quantification, as validated to be statistically consistent with the empirical data distribution from 90 blind wells across CV. We show how the model predictions may be used to supplement hydrological understanding of aquifer responses in basins with irregular well data. Our results indicate that on average the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.

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Seismic inversion for reservoir facies under geologically realistic prior uncertainty with 3D convolutional neural networks

Cited by 5 publications

References 9 publications

Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning

Shape Carving Methods of Geologic Body Interpretation from Seismic Data Based on Deep Learning

A comprehensive review of seismic inversion based on neural networks

Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network Regression

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