Representation Learning in Seismic Interpretation

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Depositional Facies Identification in Wireline Log Patterns Using 1D Convolutional Neural Network (CNN) Deep Learning Algorithms

Galatio Giovani Prabowo, Muhammad Fahmi Ramdani, Abiyyu Daffa Revanzha, Brian Muara Sianturi, Natalia Angel Momongan

Indonesian Petroleum Association

... to use Python, generating dummy data, training data, and model testing. The chosen tool for this research is the Convolutional Neural Network (CNN...

Seismic diffractions separation and imaging based on convolutional neural network

Jiaxing Sun, Jidong Yang, Zhenchun Li, Jianping Huang, Jie Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

...Seismic diffractions separation and imaging based on convolutional neural network Jiaxing Sun, Jidong Yang, Zhenchun Li, Jianping Huang, Jie Xu...

Looking for a simplified and generalized training set in ML applications for gravity modelling

Luigi Bianco, Ciro Messina, Maurizio Fedi

International Meeting for Applied Geoscience and Energy (IMAGE)

... be seen as the building blocks of each gravimetric anomaly. Here, we discuss preliminary results obtained with a Convolutional Neural Network (CNN...

Deep learning based wavefield separation method for VSP data

Gang Feng, Qin Su, Zhe Yang, Wei Yang, Jian-Hua Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... separation task is implemented using a convolutional neural network and synthetic VSP data, and a geological model construction method based...

Feature Detection for Digital Images Using Machine Learning Algorithms and Image Processing

Xiao Tian, Hugh Daigle, Han Jiang

Unconventional Resources Technology Conference (URTEC)

... is increased greatly. There are 16 weight layers in vgg16 model, including 13 convolutional layers and 3 fully-connected layers. There are 19 weight...

Fracture-cavity carbonate reservoir identification based on channel attention mechanisms

Liuxin Yang, Yongqiang Ma, Guangxiao Deng, Zhen Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... attention mechanisms in a semi-supervised learning framework. The architecture of our inversion model consists of several attention blocks, which combine...

Deep neural networks for 1D impedance inversion

Vladimir Puzyrev, Anton Egorov, Anastasia Pirogova, Chris Elders, Claus Otto

Petroleum Exploration Society of Australia (PESA)

... such as the 160-layer velocity model used as an example in this study require large synthetic datasets for training, which are not always possible...

Mitigating elastic effects of acoustic full-waveform inversion with deep learning and application to field data

Dimitri Voytan, Adriano Gomes, Debanjan Datta, Ren-douard Plessix, Anu Chandran, Ken Matson

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional network approach presented by Li et al. (2019) to a realistic 3D synthetic velocity model under a narrow-azimuth marine streamer acquisition...

Realistic synthetic data generation using neural style transfer: Application to automatic fault interpretation

Min Jun Park, Joseph Jennings, Bob Clapp, Biondo Biondi

International Meeting for Applied Geoscience and Energy (IMAGE)

... suitable to be used as training images than the original synthetic images. To verify the effectiveness of our workflow, we train a model on the synthetic...

Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study From an Unconventional Shale Reservoir

Ted Holden, Ruth Kurian, Mohammed Ibrahim, Daniel Hampson, Jonathan Downton

Unconventional Resources Technology Conference (URTEC)

...) Synthetic angle gathers are then generated for each pseudo-well using a convolutional model in which the P-wave reflection coefficients calculated using...

Unsupervised compensation of spiral-shaped drone magnetic survey using a recurrent convolutional autoencoder

Brett Bernstein, Yaoguo Li, Richard Hammack, Colton Kohnke

International Meeting for Applied Geoscience and Energy (IMAGE)

...Unsupervised compensation of spiral-shaped drone magnetic survey using a recurrent convolutional autoencoder Brett Bernstein, Yaoguo Li, Richard...

Increasing signal-to-noise ratio of borehole image logs using convolutional neural networks

Mustafa A. Al Ibrahim, Mokhles M. Mezghani

International Meeting for Applied Geoscience and Energy (IMAGE)

... using a convolutional neural network. Results are evaluated quantitatively and qualitatively. Finally, the model is applied on the image log...

Integrating U-net with full-waveform inversion for an efficient salt body construction

Abdullah Alaliand, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

..., T., 2016, Full-model wavenumber inversion: An emphasis on the appropriate wavenumber continuation: Geophysics, 81, no. 3, R89–R98, doi: https...

DL-fused elastic FWI: Application to marine streamer data

Pavel Plotnitskii, Oleg Ovcharenko, Vladimir Kazei, Daniel Peter, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... initial model and water taper are mapped into the target low-wavenumber update. captured by FusionNet convolutional architecture in order to recover smooth...

Abstract: Seismic Characterization of Complex Salt Dome Structures using Machine Learning; #91204 (2023)

Osama Alsalmi, Saleh Dossary, Gino Ananos

Search and Discovery.com

.... Convolutional Neural Networks (CNNs) gained popularity in image segmentation tasks. In this study, a machine learning model based on U-Net architecture is used...

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user1

Search and Discovery.com

... and optimization in seismic interpretation workflow. The existing workflow for seismic interpretation using Convolutional Neural Network (CNN) relies...

Application of interactive convolutional neural network micro-fracture prediction technology based on prestack depth migration data in deep shale gas reservoirs

Xiaolan Wang, Furong Wu, Junfeng Liu, Dianguang Zang, Xiao Yang, Yangjing Li, Xiaoyan Cheng

International Meeting for Applied Geoscience and Energy (IMAGE)

... Neural Prediction Technology Network (CNN) Fracture Convolutional neural networks are a type of deep learning model specifically designed...

Do We Really Need Deep Learning? A Study on Play Identification using SEM Images

Hanyan Zhang, Max T. Kasumov, Deepak Devegowda, Mark E. Curtis

Unconventional Resources Technology Conference (URTEC)

... classification. For all image resolutions considered, surprisingly, the simplest and shallowest one-convolutional layer model performs remarkably well...

A Siamese network-based full-wave inversion: Application on real data

Omar M. Saad, Randy Harsuko, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... two identical Convolutional Neural Networks (CNNs) with shared weights to ensure consistent feature extraction from observed and simulated data...

Abstract: Seismic Fault Detection by Denoising Diffusion Probabilistic Model; #91204 (2023)

Bingbing Sun, Ali Abdulmohsen, Nasher AlBinHassan

Search and Discovery.com

...Abstract: Seismic Fault Detection by Denoising Diffusion Probabilistic Model; #91204 (2023) Bingbing Sun, Ali Abdulmohsen, Nasher AlBinHassan Seismic...

Seismic simulations of experimental strata

Lincoln Pratson, Wences Gouveia

AAPG Bulletin

... of the resulting seismic section. Convolutional Model The convolutional seismic model convolves a seismic wavelet with a time series of reflection...

The Hybrid Theory-Guided Data Science-Based Method: Unlocking the Full Potential of Seismic Reservoirs Characterization

Rino Saputra, Akash Mathur, Awal Mandong

Indonesian Petroleum Association

... model. 5. Synthetic angle gathers are then generated for each pseudo-well using a convolutional model in which the P-wave reflection coefficients...

Automated velocity model building using Fourier neural operators

Guanghui Huang, Sean Crawley, Ramzi Djebbi, Jaime Ramos-Martinez, Nizar Chemingui

International Meeting for Applied Geoscience and Energy (IMAGE)

.../10.3997/2214-4609.202112777. Wang, W., F. Yang, and J. Ma, 2018, Velocity model building with a modified fully convolutional network: 88th Annual International Meeting...

MACHINE LEARNING UTILIZATION FOR ENHANCED SUCKER ROD PUMP DYNACARD RECOGNITION

Fadhila Tanjungsari, Hilman Lazuardi, Bonni Ariwibowo, Indra Sukmana, and Candra Kurniawan

Indonesian Petroleum Association

...% testing datasets, and 10% validation datasets. Different machine learning algorithms were evaluated, and it is found that the top performing model...