High-Fidelity Permeability and Porosity Prediction Using Deep Learning With the Self-Attention Mechanism

Yang, Liuqing; Wang, Shoudong; Chen, Xiaohong; Chen, Wei; Saad, Omar M; Zhou, Xu; Pham, Nam; Geng, Zhicheng; Fomel, Sergey; Chen, Yangkang

doi:10.1109/tnnls.2022.3157765

Cited by 18 publications

(2 citation statements)

References 47 publications

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“…In recent times, deep learning (DL) has demonstrated notable achievements in the domain of seismic data processing and interpretation [8][9][10][11][12]. Different from traditional model-driven methods, DL is a type of data-driven approach that can capture parameter characteristics and supplement additional constraints feasibly and adaptively through multiple processing layers with adjustable parameters based on a training set.…”

Section: Introductionmentioning

confidence: 99%

Seismic Resolution Enhancement Using a Cycle Generative Adversarial Neural Network with Pseudo-Well Data

Zhao,

Gao,

Guo

et al. 2023

Applied Sciences

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High-resolution seismic processing involves the recovery of high-frequency components from seismic data with lower resolution. Traditional methods typically impose prior knowledge or predefined subsurface structures when modeling seismic high-resolution processes, and they are usually model-driven. Nowadays, there has been a growing utilization of deep learning techniques to enhance seismic resolution. These approaches involve feature learning from extensive training datasets through multi-layered neural networks and are fundamentally data-driven. However, the reliance on labeled data has consistently posed a primary challenge for deploying these methods in practical applications. To address this issue, a novel approach for seismic high-resolution reconstruction is introduced, employing a Cycle Generative Adversarial Neural Network (CycleGAN) trained on authentic pseudo-well data. The application of the CycleGAN involves creating dual mappings connecting low-resolution and high-resolution data. This enables the model to comprehend both the forward and inverse processes, ensuring the stability of the inverse process, particularly in the context of high-resolution reconstruction. More importantly, statistical distributions are extracted from well logs and used to randomly generate extensive sets of low-resolution and high-resolution training pairs. This training set captures the structural characteristics of the actual subsurface and leads to significant improvement of the proposed method. The results from experiments conducted on both synthetic and field examples validate the effectiveness of the proposed approach in significantly enhancing seismic resolution and achieving superior recovery of thin layers when compared with the conventional method and the deep-learning-based method.

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Section: Introductionmentioning

confidence: 99%

Seismic Resolution Enhancement Using a Cycle Generative Adversarial Neural Network with Pseudo-Well Data

Zhao,

Gao,

Guo

et al. 2023

Applied Sciences

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Meng

Wang

Niu

et al. 2022

Geophysical Prospecting

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Deep learning has shown excellent performance in simulating complex nonlinear mappings from the seismic data to elastic parameters. However, seismic acoustic impedance estimated from a direct mapping from seismic waveform data to P‐wave impedance (single‐input network) is hampered by the limited frequency bands. In this paper, we propose to incorporate the low‐frequency impedance model to constrain the inversion (multi‐input network). We add a feature fusion layer to force the lateral smoothness. Besides, usually, a given seismic survey is likely to contain only a few well logs, which is insufficient for conventional deep‐learning ‐based methods to learn the complex mapping from seismic data to elastic parameters. The problem is compounded by the fact that a network trained with synthetic data (compensated for the lack of logs) cannot be directly used for field data. Therefore, we propose to use transfer learning to mitigate this issue. The multi‐input neural network is trained using synthetics and real data in two stages. We carry out experiments to demonstrate that the two‐step training multi‐input network approach has high accuracy in the time direction, excellent continuity in the lateral direction and favourable robustness. Synthetic and field data examples demonstrate that the proposed network can accurately predict impedance even with limited logging data, which provides a reference for oil and gas exploration in the actual production process.

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Deep learning in the advanced core sample porosity determination with XCT image

Wu,

Kuo,

Lin

et al. 2024

Earth Sci Inform

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High-Fidelity Permeability and Porosity Prediction Using Deep Learning With the Self-Attention Mechanism

Cited by 18 publications

References 47 publications

Seismic Resolution Enhancement Using a Cycle Generative Adversarial Neural Network with Pseudo-Well Data

Seismic Resolution Enhancement Using a Cycle Generative Adversarial Neural Network with Pseudo-Well Data

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

Deep learning in the advanced core sample porosity determination with XCT image

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