Abstract: Reflectivity Color Correction in Gabor Deconvolution; #90211 (2015)

Carlos Montana and Gary Margrave

Search and Discovery.com

... relies on the fulfillment of a set of assumptions on which the convolutional model is based: stationarity, minimum phase wavelet, white reflectivity...

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...

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...

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...

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...

ABSTRACT: Shaping the Wavelet, by Wang, Yuchun E.; Huo, Shoudong; #90141 (2012)

Search and Discovery.com

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...

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...

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...

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...

A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data

Hamzeh Alimohammadi, Hamid Rahmanifard, and Shengnan Nancy Chen

AAPG Bulletin

... at a batch size of 20. Figure 5. Optimum number of batch size (A) and dropout rate (B) for hybrid convolutional neural networks–long short-term memory model...

Seismic Facies Segmentation Using Deep Learning; #42286 (2018)

Daniel Chevitarese, Daniela Szwarcman, Reinaldo Mozart D. Silva, Emilio Vital Brazil

Search and Discovery.com

... selected a trained convolutional neural network (CNN) with the highest accuracy on the classification task. Then, we modified the final part...

Bayesian variational auto-encoder for seismic wavelet extraction

Ammar Ghanim, Ricard Durall, Norman Ettrich

International Meeting for Applied Geoscience and Energy (IMAGE)

... approaches is the convolutional model. In the well-tie process, the wavelet is convolved with the reflectivity series along the well path to produce...

Multi-realization seismic data processing with deep variational preconditioners

Matteo Ravasi

International Meeting for Applied Geoscience and Energy (IMAGE)

... problems of the form, d = Gx + ε, where the model x and the observed data d are connected via a linear operator G. Moreover, ε represents a combination...

A Deep Learning-Based Surrogate Model for Rapid Assessment of Geomechanical Risks in Geologic CO2 Storage

Fangning Zheng, Birendra Jha, Behnam Jafarpour

Carbon Capture, Utilization and Storage (CCUS)

... storage. Using simulated data, we train a U-Net convolutional neural network to learn a mapping between well locations s and spatially distributed model...

An Introduction to Deep Learning: Part III

Lasse Amundsen, Hongbo Zhou, Martin Landrø

GEO ExPro Magazine

... computer model that learns to perform classification tasks directly from images. The one that started it all was the 2012 publication ‘ImageNet...

Multiscenario-based deep learning workflow for high-resolution seismic inversion on Brazil presalt 4D

Yang Xue, Dan Clarke, Kanglin Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... model and 1D convolutional modeling. The training datasets are generated from scenario-based modeling with each group trained separately with a DL...

Rock Thin-section Analysis and Mineral Detection Utilizing Deep Learning Approach

Fatick Nath, Sarker Asish, Shaon Sutradhar, Zhiyang Li, Nazmul Shahadat, Happy R. Debi, S M Shamsul Hoque

Unconventional Resources Technology Conference (URTEC)

... of rock thin sections. In a similar objective, Nanjo et al. (2019) implemented convolutional neural network-based model to classify four types of rock...

HIGH-PRECISION ALGORITHM FOR GRAIN SEGMENTATION OF THIN SECTIONS BY MULTI-ANGLE OPTICAL-MICROSCOPIC IMAGES

Timur Murtazin, Zufar Kayumov, Vladimir Morozov, Radik Akhmetov, Anton Kolchugin, Dmitrii Tumakov, Danis Nurgaliev, Vladislav Sudakov

Journal of Sedimentary Research (SEPM)

.... (2020) for semantic segmentation of the porosity of petrographic thin sections. The U-Net model is a fully connected convolutional neural network...

Internal multiple elimination with an inverse-scattering theory guided deep neural network

Zhiwei Gu, Liurong Tao, Haoran Ren, Ru-Shan Wu, Jianhua Geng

International Meeting for Applied Geoscience and Energy (IMAGE)

... with the convolutional operation. Combining the CNN with the autoencoder can improve the feature extraction ability of the network model and have higher computational...

Abstract: Short-time Wavelet Estimation in the Homomorphic Domain; #90174 (2014)

Roberto H. Herrera and Mirko van der Baan

Search and Discovery.com

... phases in both the wavelet and the reflectivity. Theory The seismic signal is described by the convolutional model (Ulrych, 1971): s(t) = w(t) ⋆ r...

Embedding Physical Flow Functions into Deep Learning Predictive Models for Improved Production Forecasting

Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour

Unconventional Resources Technology Conference (URTEC)

...trained model is composed of several fully-connected regression layers and one- URTeC 3702606 6 dimensional (1D) convolutional layers. A fully-co...

Machine learning and seismic attributes for prospect identification and risking: An example from offshore Australia

Mohammed Farfour, Douglas Foster

International Meeting for Applied Geoscience and Energy (IMAGE)

... and convert them to Gas chimney probability cube, and to Gamma Ray cube. Next, pre-trained Convolutional Neural Network (CNN) is trained using...

3D velocity model building based upon hybrid neural network

Herurisa Rusmanugroho, Junxiao Li, M. Daniel Davis Muhammed, Jian Sun

International Meeting for Applied Geoscience and Energy (IMAGE)

... as an image are passed through some convolutional layers to estimate P-velocity model. This network is expected to learn from the features obtained from...