Abstract: FaciesNet: Machine Learning Applications for Facies Classification in Well Logs;

Chayawan Jaikla, Pandu Devarakota, Neal Auchter, Mohamed Sidahmed, Irene Espejo

Search and Discovery.com

... information, facies stacking pattern, and geologic correlations, FaciesNet. Our proposed model incorporates decoding and encoding deep convolutional...

Seismic data augmentation for automatic faults picking using deep learning

Nam Pham, Sergey Fomel

International Meeting for Applied Geoscience and Energy (IMAGE)

... these newly generated data for training a convolutional neural network for faults picking, as the training data will resemble the field test data...

GeoMind: An intelligent earth model building tool

Saleh Al Saleh, Ewenet Gashawbeza, Mustafa Marzooq, Hussam Banaja, Husain Al Shakhs, Jianwu Jiao

International Meeting for Applied Geoscience and Energy (IMAGE)

...GeoMind: An intelligent earth model building tool Saleh Al Saleh, Ewenet Gashawbeza, Mustafa Marzooq, Hussam Banaja, Husain Al Shakhs, Jianwu Jiao...

Unconventional Reservoir Microstructural Analysis Using SEM and Machine Learning

Amanda S. Knaup, Jeremy D. Jernigen, Mark E. Curtis, John W. Sholeen, John J. Borer IV, Carl H. Sondergeld, Chandra S. Rai

Unconventional Resources Technology Conference (URTEC)

... specifically Convolutional Neural Networks (CNN), are being used for pixel labeling and feature identification using the CNN U-Net. This network...

Automatic microseismic event detection in downhole DAS data through convolutional neural networks: A comparison of events during and post-stimulation of the well

Paige Given, Fantine Huot, Ariel Lellouch, Bin Luo, Robert G. Clapp, Biondo L. Biondi, Tamas Nemeth, Kurt Nihei

International Meeting for Applied Geoscience and Energy (IMAGE)

... present a convolutional neural network (CNN) which takes inputted images from DAS arrays and accurately detects microseismic events. Our model is able...

3D seismic image-to-image translation

Xiaolei Song, Muhong Zhou, Lifeng Wang, Rodney Johnston

International Meeting for Applied Geoscience and Energy (IMAGE)

... by adopting two convolutional Bayesian layers as the network output layers to analyze the model uncertainties by calculating an uncertainty map from a local...

An integrated machine learning-based fault classification workflow

Jie Qi, Carolan Laudon, Kurt Marfurt

International Meeting for Applied Geoscience and Energy (IMAGE)

... on the human interpreter. We first compute a 3D fault probability volume from pre-conditioned seismic amplitude data using a 3D convolutional neural network...

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...

Abstract: Towards the Identification of Coal Macerals through Deep Learning

Na Xu, Qingfeng Wang, Pengfei Li, Mark A. Engle

The Society for Organic Petrology (TSOP)

... are compared with the other three existing image segmentation methods, including K-means [4], Gaussian mixture model (GMM), [5] and convolutional neural...

A simultaneous denoising and event picking approach using supervised machine learning

Salman Abbasi, Motaz Alfarraj, Dmitry Borisov, Vikram Jayaram, Iftekhar Alam, Bakhtawer Sarosh

International Meeting for Applied Geoscience and Energy (IMAGE)

... problems (i.e., denoising and event detection) using a single network. A convolutional neural network is used to capture the high frequency times series...

Separation of simultaneous source wavefields using convolutional neural network

Zhehao Li, Hua-Wei Zhou, Kang Fu

International Meeting for Applied Geoscience and Energy (IMAGE)

...Separation of simultaneous source wavefields using convolutional neural network Zhehao Li, Hua-Wei Zhou, Kang Fu Separation of simultaneous source...

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...

Identification of vehicles from seismic signals using machine learning

Xiaoxuan Zhu, Ji Zhang, Jie Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... the performance of Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) for identifying vehicles...

Transformer-based network for an efficient ground roll suppression

Randy Harsuko, Omar Saad, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

...). This is attributed to the new components introduced to the model, namely the learnable positional encoding, the 1D convolutional encoder-decoder...

Convolutional neural networks as an aid to biostratigraphy and micropaleontology: a test on late Paleozoic microfossils

Rafael Pires De Lima, Katie F. Welch, James E. Barrick, Kurt J. Marfurt, Roger Burkhalter, Murphy Cassel, Gerilyn S. Soreghan

PALAIOS

... and the input data to train the convolutional kernel weights. Cross Entropy Loss.—A measure of the difference between the model’s predictions are from...

Convolution Neural Networks … If They can Identify an Oncoming Car, can They Identify Lithofacies in Core?; #42312 (2018)

Rafael Pires de Lima, Fnu Suriamin, Kurt Marfurt, Matthew Pranter, Gerilyn Soreghan

Search and Discovery.com

... drive our cars but also taste our beer. Specifically, recent advances in the architecture of deep-learning convolutional neural networks (CNN) have...

A self-attention enhanced encoder-decoder network for seismic data denoising

Stefan Knispel, Jan Walda, Ruediger Zehn, Alexander Bauer, Dirk Gajewski

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutions (Bello et al., 2019), where attentional feature maps are generated and concatenated to the convolutional feature maps. This does not replace...

Abstract: Impedance Inversion of Blackfoot 3D Seismic Dataset; #90171 (2013)

A. Swisi and Igor B. Morozov

Search and Discovery.com

... by using the methods below. 2) Model-based inversion is also called blocky inversion. This method is based on the convolutional seismic model: S =W * R + n...

Seismic Facies Segmentation Using Deep Learning

Search and Discovery.com

Application of Machine Learning and Deep Learning for Complex Fault Network Characterizationon the North Slope, Alaska

Search and Discovery.com

Bridging the gap: Deep learning on seismic field data with synthetic training for building Gulf of Mexico velocity models

Stuart Farris, Robert Clapp

International Meeting for Applied Geoscience and Energy (IMAGE)

... Clapp, Stanford University SUMMARY This study employs Convolutional Neural Networks (CNNs) to predict low-wavenumber seismic velocity models to serve...

Interpretation of deep neural networks for carbonate thin section classification

Lukas Mosser, George Ghon, Gregor Baechle

International Meeting for Applied Geoscience and Energy (IMAGE)

... SUMMARY This study uses ImageNet pretrained convolutional neural networks (CNNs), VGG11 and ResNet18 models to predict carbonate rock and pore types...

Convolutional Neural Networks Forecasting for Unconventional Drilling Units in US Land

Francisco J. Parga Garcia, Jie Fang, Niven Shumaker

Unconventional Resources Technology Conference (URTEC)

...Convolutional Neural Networks Forecasting for Unconventional Drilling Units in US Land Francisco J. Parga Garcia, Jie Fang, Niven Shumaker URTeC...

An Overview of Reservoir Seismic Stratigraphy, Frontmatter

Tom Wittick

North Texas Geological Society

... Acoustic impedance Reflection coefficients Wavelets The convolutional model III. Preparation of seismic data for stratigraphic work Data...

ABSTRACT: Quantitative Integration of 4D Seismic for Field Development; #90007 (2002)

Garnham, Gail Riekie, Malu Jensen, Liz Pointing

Search and Discovery.com

... Figure 1. Schematic display of Nelson channels Figure 2. Forward convolutional model of moved OWC Figure 3. 4D forward convolutional model (Moved OWC...