Strong Foundations, Deep Integration, Infinite Possibilities

James Lowell, Peter Szafian, Nicola Tessen

GEO ExPro Magazine

... input increases. As artificial neural networks similarly learn by example and can solve problems with diverse, unstructured and interconnected data...

AI Seismic Interpretation for a Better, More Sustainable Tomorrow

Ryan Williams, Chris Han, Peter Szafian, Mark Brownless, James Lowell, Geoteric

GEO ExPro Magazine

..., advanced neural networks have demonstrated their ability to surpass traditional attribute analysis in identifying, delineating, and extracting faults...

Multi-Detector, Pulsed Neutron-Based Synthetic Openhole Logs- An Unconventional Gas Reservoir Case Study

Yonghwee Kim, David Chace

Unconventional Resources Technology Conference (URTEC)

... of neural network or multidimensional histogram models to emulate openhole log data based on the pulsed neutron responses. The emulated openhole data...

The Pematang Group Sand Analysis Using Growing Neural Network Machine Learning

Rizky Hidayat, Duddy Lastawan, Fadhli Ruzi, Roby Oksuanandi, L.T.Hardanto

Indonesian Petroleum Association

.... An investigation of deep neural networks for noise robust speech recognition: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing...

Conditioning Stratigraphic, Rule-Based Models with Generative Adversarial Networks: A Deepwater Lobe, Deep Learning Example; #42402 (2019)

Honggeun Jo, Javier E. Santos, Michael J. Pyrcz

Search and Discovery.com

... Using a Laplacian Pyramid of Adversarial Networks: Advances in Neural Information Processing Systems, p. 1486-1494. Deptuck, M.E., D.J.W. Piper, B...

Using Machine Learning for Geosteering During In-Seam Drilling

Ruizhi Zhong, Ray L. Johnson Jr, Zhongwei Chen

Unconventional Resources Technology Conference (URTEC)

...org/10.1088/1742-2140/aaac5d. Ashrafi, S.B., Anemangely, M., Sabah, M. et al. 2019. Application of hybrid artificial neural networks for predicting rate of penetrat...

Unsupervised deep learning for seismic data reconstruction

Gui Chen, Yang Liu, Mi Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... for reconstructing missing traces in observed seismic data. While many DL-based reconstruction methods employ convolutional neural networks (CNNs...

Relative geologic time generation based on 3D-CNNs and domain adaptation

Xin He, Bangli Zou, Yifeng Fei, Gang Yu, Dajun Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... (Wu et al., 2019). The above methods are predicated on the training of neural networks using synthetic seismic data, which are subsequently utilized...

Generating Missing Unconventional Oilfield Data using a Generative Adversarial Imputation Network (GAIN)

Justin Andrews, Sheldon Gorell

Unconventional Resources Technology Conference (URTEC)

... and was not uniformly recorded. Overview of GAN and GAIN A GAN is a system comprised of two primary neural networks, a generator and a discriminator...

JAX acceleration of implicit FWI and field data application

H. V. Nguyen, A. Sekar, K. Wang, T. Zhang, T. Nemeth, U. Albertin, M. Eaid, K. Nihei, E. Wang, K. Innanen

International Meeting for Applied Geoscience and Energy (IMAGE)

... Inversion (IFWI), originally introduced in Sun (2023) and Zhang (2022). IFWI replaces the gridded earth model representation with a continuous neural...

Using Gas Chimneys in Seal Integrity Analysis: A Discussion Based on Case Histories

Roar Heggland

AAPG Special Volumes

... reservoir characterisation using artificial neural networks: Proceedings of the 19th Mintrop Seminar, May 1618, 1999, Munster, Germany, p. 7185.de Groot, P...

Deep convolutional neural networks for generating grain-size logs from core photographs

Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott Cole, and Ishtar Barranco

AAPG Bulletin

...Deep convolutional neural networks for generating grain-size logs from core photographs Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott...

Hydraulic fracture-hit detection system using low-frequency DAS data

Xiaoyu Zhu, Ge Jin, Richard Hammack

International Meeting for Applied Geoscience and Energy (IMAGE)

.... The random forest classifier achieved the best performance among neural networks and bagging support vector machine classifier. The workflow was applied...

Broadband reconstruction of seismic signal with generative recurrent adversarial network

Zhijun Zhang, Wei Song, Wei Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... generative neural networks: Presented at the 80th Conference and Exhibition, EAGE, Expanded Abstracts. Richardson, A., 2018, Generative adversarial networks...

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...

Microsoft Word - image2023_final (10).docx

J0381057

International Meeting for Applied Geoscience and Energy (IMAGE)

...., 2020). Neural networks, as the backbone of deep learning, are usually composed of convolutional layers that are designed to be trained on large datasets...

Transfer learning for cement evaluation: An image classification approach using VDL time series

Amirhossein Abdollahian, Hua Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... isolation potential. The cornerstone of transfer learning is the utilization and fine-tuning of pre-trained Convolutional Neural Networks (CNNs), which...

Uncertainty quantification of single and multi-parameter full-waveform inversion through a variational autoencoder

Abdelrahman Elmeliegy, Mrinal Sen, Jennifer Harding, Hongkyu Yoon

International Meeting for Applied Geoscience and Energy (IMAGE)

...; Zhang and Curtis, 2021; Zhu et al. 2022). By leveraging the power of neural networks, deep generative models can learn complex patterns...

Artificial Intelligence … Its Use in Exploration and Production. Part 1: What is it really and what are its limitations?

Barrie Wells

GEO ExPro Magazine

... conductivity, and fluid saturation of rocks from inputs. Neural Networks CREDIT: (HTTPS://XKCD.COM/1834/), LICENSED UNDER A CREATIVE COMMONS...

Leveraging self-supervised deep learning to address cross-talks in multi-parameter inversions

Wenlong Wang, Yulang Wu, Yanfei Wang, George A. McMechan

International Meeting for Applied Geoscience and Energy (IMAGE)

... using deep neural networks: Geophysics, 86, no. 1, V1–V13, doi: https://doi .org/10.1190/geo2019-0382.1. Lin, Y., and Y. Wu, 2018, Inversionnet: A real...

ABSTRACT: Fluid and Petrophysical Prediction in The Elastic Domain Using Neural Network Method, by Hermana, Maman; Najmi, Muhammad; Tuan Harith, Zuhar; Sum, Chow W.; #90155 (2012)

Search and Discovery.com

Facies Classification Based on Well Logs by Using an Convolutional Neural Network

Search and Discovery.com

ABSTRACT: Learning the Relationship between Seismic Attributes and Lithofacies by Back-Propagation Neural Network; #90049 (2005)

P. Siripitayananon, H-C. Chen, B. S. Hart

Search and Discovery.com

...ABSTRACT: Learning the Relationship between Seismic Attributes and Lithofacies by Back-Propagation Neural Network; #90049 (2005) P. Siripitayananon...

Abstract: Case Study of a Cadomin Gas Reservoir (Leland) in the Deep Basin: From Deterministic Inversion to Neural Network Analysis; #90211 (2015)

Carmen Dumitrescu and Fred Mayer

Search and Discovery.com

...Abstract: Case Study of a Cadomin Gas Reservoir (Leland) in the Deep Basin: From Deterministic Inversion to Neural Network Analysis; #90211 (2015...

Abstract: Prediction of Porosity and Fluid Saturation from Full Stack Seismic Data Using Seismic Inversion and Neural Network Analysis; #90319 (2018)

Ahmed S. Ali

Search and Discovery.com

...Abstract: Prediction of Porosity and Fluid Saturation from Full Stack Seismic Data Using Seismic Inversion and Neural Network Analysis; #90319 (2018...