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The AAPG/Datapages Combined Publications Database
Showing 324 Results. Searched 195,354 documents.
Facies Classification Based on Well Logs by Using an Convolutional Neural Network
Search and Discovery.com
N/A
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...
2023
ABSTRACT: Maximum Likelihood Deconvolution: a New Perspective, by Jerry M. Mendel; #91035 (2010)
Search and Discovery.com
2010
Abstract: Grain Segmentation and Region Mask Generation in Digital Rock Images Using Convolutional Neural Networks;
Rengarajan Pelapur, Arash Aghaei, Connor Burt, Bidur Bohara
Search and Discovery.com
... neural networks. This model is trained on a database of rock models generated using a 3D process-based modeling technique. Convolutional Neural Network...
Unknown
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...
2018
Abstract: AI- Assisted Palynological Analysis Using an Expert-Trained Convolutional Neural Network: A Case Study form the Jurassic in the North Sea; #91204 (2023)
Rader Abdul Fattah, Merijn de Bakker, Alexander Houben, Roel Verreussel, Robert Williams
Search and Discovery.com
...Abstract: AI- Assisted Palynological Analysis Using an Expert-Trained Convolutional Neural Network: A Case Study form the Jurassic in the North Sea...
2023
AI to Improve the Reliability and Reproducibility of Descriptive Data: A Case Study Using Convolutional Neural Networks to Recognize Carbonate Facies in Cores
Search and Discovery.com
N/A
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
...Horizontal Stresses Prediction Using Sonic Transition Time Based on Convolutional Neural Network; #42587 (2023) Esmael Makarian, Ayub Elyasi, Fatemeh...
2023
Abstract: GAN-Based Multipoint Geostatistical Inversion Method and Application;
Pengfei Xie, Jiagen Hou
Search and Discovery.com
... technology. Multi-point statistics (MPS) generate model realizations by training image (TI) that are consistent with prior information. This method often...
Unknown
Multichannel seismic deconvolution via 2D K-SVD and convolutional sparse coding
Guiqian Zhang, Xiayu Gao, Bangli Zou, Yaojun Wang, Yingzhu Chen
International Meeting for Applied Geoscience and Energy (IMAGE)
... to the deconvolution objective function in the form of regularization. Frequency Decomposition of Seismic Profile According to the convolutional model...
2023
Abstract: Machine Learning Assisted Fracture Characterization with Borehole Image Logs in Geothermal Wells; #91204 (2023)
Chicheng Xu
Search and Discovery.com
... from multiple sources of data, we build a convolutional neural network model and train it with the labeled results from borehole image log. The model...
2023
Detect Oil Spill in Offshore Facility Using Convolutional Neural Network and Transfer Learning
Dharmawan Raharjo, Muhamad Solehudin
Indonesian Petroleum Association
...Detect Oil Spill in Offshore Facility Using Convolutional Neural Network and Transfer Learning Dharmawan Raharjo, Muhamad Solehudin This paper has...
2021
Abstract: Deep Learning Inversion on Seismic Cubes; #91204 (2023)
Aleksandr Koriagin, Alexey Kozhevin, Stepan Goriachev, Roman Khudorozhkov
Search and Discovery.com
... show how one can perform inference on full seismic cubes using convolutional neural networks and specific prediction aggregation techniques...
2023
Automated detection of ferromagnetic pipelines from magnetic total-field anomaly data using convolutional neural networks
Brett Bernstein, Yaoguo Li, Richard Hammack
International Meeting for Applied Geoscience and Energy (IMAGE)
...Automated detection of ferromagnetic pipelines from magnetic total-field anomaly data using convolutional neural networks Brett Bernstein, Yaoguo Li...
2023
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...
2015
ABSTRACT: Selected Topics in Seismic Dispersion
Christopher L. Liner
Houston Geological Society Bulletin
..., reflection and transmission coefficients, head waves, etc. The convolutional reflection models we use to model thick and thin bed thin response...
2012
Abstract: Modeling of Seismic Signatures of Carbonate Rock Types, by B. Jan and Y. Sun, #90188 (2014)
Search and Discovery.com
2014
Representation Learning in Seismic Interpretation
Search and Discovery.com
N/A
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...
2023
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...
2018
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...
2023
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...
2019
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...
2023
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...
2021
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...
2002