Attention-based self-calibrated convolution neural network for efficient facies classification

Motaz Alfarraj

International Meeting for Applied Geoscience and Energy (IMAGE)

... and production operations. Deep convolutional neural networks have been widely used for seismic interpretation tasks including detection, classification...

Enhancing seismic data quality: A machine learning approach to denoising and signal damage reduction

Mark Roberts, Olga Brusova, Leandro Gabioli, Alejandro Valenciano

International Meeting for Applied Geoscience and Energy (IMAGE)

..., Leandro Gabioli, and Alejandro Valenciano, TGS Summary We propose a denoise workflow comprising a supervised ML (Machine Learning) model applied...

Feasibility Study Methodology for Fracture Analysis Studies Using Seismic Azimuthal Amplitude Variation: Application in Southern Mexico

Alexis Ferrer Balas, Nahum Campos, Jesus Garcia Hernandez

GCAGS Transactions

... be provided by a geometrical feasibility study, followed by convolutional modeling and azimuthal amplitude vs. offset (or angle) analysis (often abbreviated...

Abstract: Utilizing Seismic Attributes for Machine Assisted Fault Detection and Extraction; #91204 (2023)

Muhammad Khan, Yasir Bashir, Saleh Dossary, Syed Ali

Search and Discovery.com

... labelled data as transfer learning to update the foundation Convolutional Neural Network (CNN) model that was initially trained on synthetic data...

Efficient Bayesian full-waveform inversion using a deep convolutional autoencoder prior

Shuhua Hu, Mrinal K Sen, Zeyu Zhao, Abdelrahman Elmeliegy, Shuo Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... that model reparametrization using deep convolutional neural networks (CNN) naturally introduces regularization to FWI. To leverage the benefits of DNN...

CNN for image super-resolution of airborne magnetic data in Ontario, Canada

Rafael Pires de Lima

International Meeting for Applied Geoscience and Energy (IMAGE)

... the two first convolutional blocks of a VGG model (Liu and Deng, 2015) pretrained on ImageNet (Russakovsky et al., 2015). RESULTS AND DISCUSSION We begin...

Elastic-AdjointNet: A physics-guided deep autoencoder to overcome crosstalk effects in multiparameter full-waveform inversion

Arnab Dhara, Mrinal Sen

International Meeting for Applied Geoscience and Energy (IMAGE)

... of the decoder is fed into a convolutional layer which reduces the channel dimension to 1. The intermediate output is added to a starting model and given...

Retrieving high-resolution acoustic impedance using full-waveform inversion in presalt reservoir setting, offshore Brazil

Akshat Abhishek, Alok K. Soni, Rene-Edouard Plessix, Martijn Blaauw, Jaydip Guha, Gautam Kumar

International Meeting for Applied Geoscience and Energy (IMAGE)

... impedance inversion using our approach and perform a comparative study against the model parameters obtained from a convolutional model-based inversion work...

Time-lapse matching of OBN seismic data using 2D convolutional neural networks

Ramon C. F. Araújo, Gilberto Corso, Samuel Xavier-de-Souza, João M. de Araújo, Tiago Barros

International Meeting for Applied Geoscience and Energy (IMAGE)

...Time-lapse matching of OBN seismic data using 2D convolutional neural networks Ramon C. F. Araújo, Gilberto Corso, Samuel Xavier-de-Souza, João M. de...

APPLICATION OF MACHINE LEARNING IN COORDINATION NUMBER ESTIMATION FOR RESERVOIR ROCK EXTRACTION

Dinanti Syafirani Zahra, Maharani Arisandy, Shafa Maura Fidela, Aldenia Alexandra, and Irwan Ary Dharmawan

Indonesian Petroleum Association

..., relying on experimental data or manual image analysis. This study explores a machine learning approach using a custom- developed Convolutional Neural...

ABSTRACT: Seismic Heterogeneity Cubes and Corresponding Equiprobable Simulations; #90013 (2003)

Matthias Imhof, William Kempner

Search and Discovery.com

... attributes. Instead, model statistics with only six parameters are fitted to the raw statistics. These six parameters include three orthogonal...

Abstract: A Transfer Learning Approach to Rock Property Estimation Workflows;

Ahmad Mustafa, Motaz Alfarraj, Ghassan Alregib

Search and Discovery.com

.... This results in vertical discontinuities in the computed property volumes using such a model, since it becomes sensitive to lateral changes...

Research on fault-karst reservoir identification method based on deep convolutional network

Zhipeng Gui, Junhua Zhang, Hong Zhang, Dong Chen, Pengbo Yin

International Meeting for Applied Geoscience and Energy (IMAGE)

... model adopted in this paper is shown in Figure 1a. Following the model input, it is connected to an MFEB module, followed by two convolutional layers...

Application of transfer learning and multi-scale feature fusion in intelligent suppression of seismic random noise

Xin Xu, Wuyang Yang, Xinjian Wei, Haishan Li, Nang Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Key Laboratory of lnternet of Things,CNPC Summary Denoising Convolutional Neural Networks (DnCNN), a data-driven learning algorithm, has been widely...

Seismic image-to-image translation using a conditional GAN with Bayesian inference

Xiaolei Song, Muhong Zhou, Petr Jilek, Rodney Johnston, Sean Cardinez, Kareem Vincent

International Meeting for Applied Geoscience and Energy (IMAGE)

... uncertainties. We take a similar approach by adopting two convolutional Bayesian layers as the network output layers to capture the model...

Unlocking Lithium Potential from Oilfield Brines: A Deep Learning-Driven Resource Assessment

Rajkanwar Singh, Saaksshi Jilhewar, Audrey Der, Ryan Mercer, Vikram Jayaram

Unconventional Resources Technology Conference (URTEC)

... structured chemical data. • CNN-Bidirectional GRU (BiGRU) Model: Combining convolutional layers with bidirectional Gated Recurrent Units (GRUs) to enhance...

Transfer Learning Applied to Seismic Images Classification

Search and Discovery.com

Abstract: CO2 Distribution Prediction Using Machine Learning Based Proxy Model in Geological Carbon Sequestration;

Zhi Zhong, Alexander Sun

Search and Discovery.com

...Abstract: CO2 Distribution Prediction Using Machine Learning Based Proxy Model in Geological Carbon Sequestration; Zhi Zhong, Alexander Sun CO2...

A geophysical prior knowledge guided semisupervised deep learning framework for AVA inversion

Lei Zhu

International Meeting for Applied Geoscience and Energy (IMAGE)

... forward model. This reduces the dependence of the framework on training data. This GPKGS framework preserves the physical process of AVA inversion, making...

RNN-based seismic velocity model building: Improving generalization using hybrid training data

Hani Alzahrani, Jeffrey Shragge

International Meeting for Applied Geoscience and Energy (IMAGE)

...RNN-based seismic velocity model building: Improving generalization using hybrid training data Hani Alzahrani, Jeffrey Shragge RNN-based Seismic...

Earthquake Detection and Focal Mechanism Calculation Using Artificial Intelligence

Shane Quimby, Yanwei Zhao, Jie Zhang, GeoTomo

Unconventional Resources Technology Conference (URTEC)

... network (FCN). FCNs are supervised deep learning networks based on convolutional layers, without being fully connected. This necessitates fewer model...

Using machine learning to interpret 3D airborne electromagnetic inversions

Eldad Haber, Jen Fohring, Mike McMillan, Justin Granek

Petroleum Exploration Society of Australia (PESA)

... types of regularization and constraints to the model, but another approach is to learn what underlying structures or boundaries these smooth...

3D real-time imaging for electromagnetic fracturing monitoring based on deep learning

Zhigang Wang, Yao Lu, Ying Hu, Yinchu Li, Ke Wang, Dikun Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

... an improved supervised deep fully convolutional network (FCN) to learn the relationship between surface electromagnetic data patterns...

Deep-learning application of salt geometry detection in deep water Brazil

Ruichao Ye, Anatoly Baumstein, Kirk A. Wagenvelt, Erik R. Neumann

International Meeting for Applied Geoscience and Energy (IMAGE)

... a novel workflow based on a deep convolutional neural network for automatically detecting salt geometry from a seismic image. By developing...

Reservoir Modeling With Deep Learning

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