An Approach in Caving Recognition by An Integrated Model of Computer Vision and Machine Learning for Any Drilling Environment, #42374 (2019).

Carlos A. Izurieta, Luis A. Rocha, Dan Sui,

Search and Discovery.com

... is being used for training and testing with nine different supervised Machine Learning algorithms and three different architectures of Neural Networks...

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

...Convolution Neural Networks … If They can Identify an Oncoming Car, can They Identify Lithofacies in Core?; #42312 (2018) Rafael Pires de Lima, Fnu...

Joint 3D inversion of gravity and magnetic data using deep learning neural networks

Nanyu Wei, Dikun Yang, Zhigang Wang, Yao Lu

International Meeting for Applied Geoscience and Energy (IMAGE)

...Joint 3D inversion of gravity and magnetic data using deep learning neural networks Nanyu Wei, Dikun Yang, Zhigang Wang, Yao Lu Joint 3D inversion...

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...

Azimuthal Attenuation Attributes Applied To Integrated Fractured Reservoir Characterization By Using 3-D Prestack Seismic Data

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Abstract: Unsupervised Segmentation of Rock MicroCT Scans Using Deep Learning;

Fernando Bordignon, Giovanni Formighieri, Eduardo Burgel, Bruno Rodrigues

Search and Discovery.com

... a costly process. Deep Neural Networks (DNN) have been extensively used to solve problems in various areas of knowledge. A particularly useful class...

Physics-directed unsupervised machine learning: Quantifying uncertainty in seismic inversion

Sagar Singh, Yu Zhang, David Thanoon, Pandu Devarakota, Long Jin, Ilya Tsvankin

International Meeting for Applied Geoscience and Energy (IMAGE)

... and fault picking (e.g., Di et al., 2018; Wu et al.,2018). However, deep-learning applications, such as those based on CNNs (convolutional neural networks...

Automated active learning for seismic facies classification

Haibin Di, Leigh Truelove, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks have been popularly implemented for seismic image interpretation including facies classification, the performance...

Convolution model theory-based intelligent AVO inversion method for VTI media

Yuhang Sun, Yang Liu, Hongli Dong

International Meeting for Applied Geoscience and Energy (IMAGE)

... the precision of inversion results (Wang et al., 2021; Sun et al., 2021). On the other hand, the VTI medium intelligent inversion method based on neural networks...

Research and application of Intelligent high resolution processing method based on ISTA-Net

Huahui Zeng, Qin Su, Sanyi Yuan, Lide Wang, Yanwu Xu, Huijie Meng, Deying Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

..., algorithms such as neural network are gradually applied to geophysical exploration. In 1994, Rö and Tarantola introduced neural th network...

Deep Dix: Enhancing interval velocity model estimation through adversarial regularization

Joseph Stitt, Robert Clapp, Biondo Biondi

International Meeting for Applied Geoscience and Energy (IMAGE)

... that Convolutional Neural Networks (CNNs) have successfully generated mappings from low-frequency shot gathers to low-wavenumber Earth model...

Abstract: Machine Learning and Deep Learning in Oil and Gas Industry: A Review Ofapplications, Opportunities and Challenges; #91204 (2023)

Tejas Balasaheb Sabale, Syed Aaquib Hussain, Mohd Zuhair, Mohammad Saud Afzal, Arnab Ghosh

Search and Discovery.com

...: It is a subset of ML, uses neural networks which passes data through layers for learning. The word ‘deep’ in DL refers to the number of layers...

A New Tool for Lower Brushy Canyon Completion Decisions

W. W. Weiss, B. A. Stubbs, R. S. Balch

West Texas Geological Society

... measured bulk volume oil (MS°). A neural network was trained and tested using density and neutron porosity plus shallow and deep resistivity logs...

Horizontal Stresses Prediction Using Sonic Transition Time Based on Convolutional Neural Network; #42587 (2023)

Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo

Search and Discovery.com

... new technology in data science fast and accurately called Convolutional Neural Networks (CNN) by two seismic data:  Delta – T Compressional (DTC...

Correlating Seismic Attributes to Reservoir Properties using Multi-variate Non-linear Regression

Robert S. Balch, William W. Weiss, Shaochang Wo

West Texas Geological Society

... Gradient Method for Fast Supervised Learning, Neural Networks, 6, 525–533. Schultz, P.S., S. Ronen, M. Hattori, and C. Corbett, 1994. Seismic-guided...

Determination of Porosity and Permeability in Reservoir Intervals by Artificial Neural Network Modelling, Offshore Eastern Canada

Zehui Huang, Mark A. Williamson

CSPG Special Publications

...Determination of Porosity and Permeability in Reservoir Intervals by Artificial Neural Network Modelling, Offshore Eastern Canada Zehui Huang, Mark...

Neural net generated seismic facies map and attribute facies map

Sunit K. Addy, Philip Neri

CSPG Special Publications

...Neural net generated seismic facies map and attribute facies map Sunit K. Addy, Philip Neri 1998 135 136...

Transfer Learning with Recurrent Neural Networks for Long-term Production Forecasting in Unconventional Reservoirs

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

Unconventional Resources Technology Conference (URTEC)

...Transfer Learning with Recurrent Neural Networks for Long-term Production Forecasting in Unconventional Reservoirs Syamil Mohd Razak, Jodel Cornelio...

Abstract: A Neural Network Application in Biostratigraphy, by J. Yang-Logan and J. M. Hornell; #90987 (1993).

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Detection of hydrocarbon reservoir boundaries using neural network analysis of surface geochemical data

Hari Doraisamy, Daniel H. Vice, Phillip M. Halleck

AAPG Bulletin

... conclude that application of neural networks to properly designed surface geochemical studies holds promise for use in defining the boundaries of known...

Integration of deep neural networks into seismic workflows for low-carbon energy

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

International Meeting for Applied Geoscience and Energy (IMAGE)

...Integration of deep neural networks into seismic workflows for low-carbon energy Biondo Biondi, Joseph Jennings, Min Jun Park, Stuart Farris, Bob...

Machine Learning and Deep Learning for Digitizing Scanned Images of Seismic Reflection Data

Agus Abdullah, Sigit Sukmono, João Constantino, Vladimir Machado

Indonesian Petroleum Association

..., Durrani T.S. (2003) Automated 3-D Horizon Tracking and Seismic Classification Using Artificial Neural Networks. In: Sandham W.A., Leggett M. (eds...

Using Advanced Seismic Attribute Analysis to Reduce Risk in Frontier Exploration … West Newfoundland Offshore, #40660 (2010)

Valentina V. Baranova, Azer Mustaqeem,

Search and Discovery.com

... within a formation triggers the dolomitization process, we used seismic attributes and neural networks to identify areas with karst morphology...

Bulk Gas Volume Estimation Using Multi-Attribute Regression and Probabilistic Neural Network (PNN): A Case Study in a Gas Field from East Coast of India, #41100 (2012)

Amit K. Ray, Samir Biswal

Search and Discovery.com

..., v. 66/5, p. 1349-1358. Liu, Z. and Liu, J., 1998, Seismic controlled nonlinear extrapolation of well parameters using neural networks: Geophysics, v...

Deep nonlinear seismic prior for seismic interpolation

Yuhan Sui, Xiaojing Wang, Jianwei Ma

International Meeting for Applied Geoscience and Energy (IMAGE)

..., most of deep neural networks are based on linear neurons, which is represented by a linear combination of the inputs and weights. However...