Semi‐supervised deep autoencoder for seismic facies classification

Abstract

Facies boundaries are critical for flow performance in a reservoir and are significant for lithofacies identification in well interpretation and reservoir prediction. Facies identification based on supervised machine learning methods usually requires a large amount of labelled data, which are sometimes difficult to obtain. Here, we introduce the deep autoencoder to learn the hidden features and conduct facies classification from elastic attributes. Both labelled and unlabelled data are involved in the training process. Then, we develop a semi‐supervised deep autoencoder by taking the mean of intra‐class and the whole population of facies into account to construct a classification regularization term, thereby improving the classification accuracy and reducing the uncertainty. The new method inherits the profits of deep autoencoder and absorbs the information provided by labelled data. The proposed method performs well and produces promising results when it is used to address problems of reservoir prediction and facies identification. The new method is evaluated on both well and seismic data and compared with the conventional deep autoencoder method, which demonstrates its feasibility and superiority with respect to classification accuracy.

Building initial model for seismic inversion based on semi‐supervised learning

“…Deep learning is a data‐driven algorithm that can learn implicit nonlinear relations from label data and use it to solve nonlinear problems. Deep learning has been widely used in geophysical problems such as seismic facies analysis (Liu et al., 2021; Nishitsuji & Exley, 2019), first‐break picking (Wang et al., 2019; Yuan et al., 2018; Zwartjes & Yoo, 2022), fault identification (Huang et al., 2017; Wu et al., 2019; Zhou et al., 2021) and model building (Araya‐Polo et al., 2017; Fabien‐Ouellet & Sarkar, 2019; Ovcharenko et al., 2022). Recently, the application of deep neural networks in reservoir characterization has also been investigated (Chen & Saygin, 2021; Dhara et al., 2023; Di & Abubakar, 2021; Sun et al., 2021; Wu et al., 2021).…”

Advanced Seismic Data Analysis: Comparative study of Machine Learning and Deep Learning for Data Prediction and Understanding

“…Many existing studies have focused on the application of individual methods without fully exploring the synergistic potential of combining various computational algorithms (X. Liu et al, 2021). This research aims to address this gap by providing an integrated, multifaceted approach that leverages the strengths of various computational techniques in unison.…”

Seismic impedance inversion using a multi‐input neural network with a two‐step training strategy

“…Over a period of several decades, deep learning (DL) has been developing rapidly (LeCun et al ., 2015), which has brought increased attention from geophysicists to apply the DL strategy to problems related to seismic exploration (Yu and Ma, 2021), such as velocity model building (Yang and Ma, 2019; Li et al ., 2020; Sun et al ., 2021; bin Waheed et al ., 2021), fault detection (Wu et al ., 2019b), noise attenuation (Gao et al ., 2021; Sang et al ., 2021; Yang et al ., 2021a, 2021b), facies classification (Liu et al ., 2021), trace interpolation (Wang et al ., 2020a), lithofacies prediction (Zhao et al ., 2021), permeability and porosity prediction (Yang et al ., 2022), velocity picking (Wang et al ., 2021b) and full‐waveform inversion (Zhang and Alkhalifah, 2019; Huang and Zhu, 2020). Considerable research has been carried out previously for the development of DL‐based methods for seismic inversion; one of the simplest and most commonly adopted methods is to train a supervised network using a mass of seismic data and the corresponding impedance model and then input all the seismic data into the trained network architecture to predict the full impedance.…”