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The AAPG/Datapages Combined Publications Database
Showing 5,753 Results. Searched 195,364 documents.
Machine Learning Multi-Attribute Analysis for Gas Hydrate Identification
Search and Discovery.com
N/A
Abstract: Seismic Attribute Analysis and the use of Unsupervised Neural Networks and Principal Component Analysis in Unconventional and Conventional Reservoirs; #90227 (2015)
Deborah Sacrey
Search and Discovery.com
... attributes. In turn, these attributes are input to self-organizing map (SOM) training. SOM, a form of unsupervised neural networks, has proven...
2015
Abstract: Seismic Attribute Analysis and the Use of Unsupervised Neural Networks and Principal Component Analysis in Unconventional and Conventional Reservoirs; #90239 (2015)
Deborah Sacrey
Search and Discovery.com
... attributes. In turn, these attributes are input to self-organizing map (SOM) training. SOM, a form of unsupervised neural networks, has proven...
2015
Abstract: Seismic Attribute Analysis and the Use of Unsupervised Neural Networks and Principal Component Analysis in Unconventional and Conventional Reservoirs; #90245 (2016)
Deborah Sacrey
Search and Discovery.com
... help determine meaningful seismic attributes. In turn, these attributes are input to self-organizing map (SOM) training. SOM, a form of unsupervised...
2016
Cluster Assisted 3D Unsupervised Seismic Facies Analysis - An Example from Osage County, Oklahoma; #40832 (2011)
Atish Roy and Kurt J. Marfurt
Search and Discovery.com
... Introduction to waveform classification Overview of Kohonen Self-organizing maps (SOM) – Training Process 3D multi-attribute SOM clustering analysis 3D...
2011
Seismic Interpretation with Machine Learning
Rocky Roden, Deborah Sacrey
GEO ExPro Magazine
... principal component analysis (PCA) and self-organizing maps are components of a machine-learning interpretation workflow (Figure 1) that involves...
2016
Abstract: Low Saturation Gas Reservoir Discrimination Using Self-Organizing Maps, Deep Water Gulf of Mexico;
Julian Chenin, Heather Bedle
Search and Discovery.com
... learning multi-attribute analysis is performed on 3D post-stack seismic data within Green Canyon, deepwater Gulf of Mexico, over several blocks...
Unknown
From Unconventional Reservoir Characterization, 3D Seismic multi-attribute analysis and machine learning guided geocellular modeling to Well Performance (EUR) Simulation: Woodford Shale Case Study in North of Oklahoma, USA
Search and Discovery.com
N/A
Characterizing a Mississippian Tripolitic Chert Reservoir Using 3D Unsupervised Multi-attribute Seismic Facies Analysis
Atish Roy, Benjamin L. Dowdell, Kurt J. Marfurt
Oklahoma City Geological Society
..., each having advantages and disadvantages. Our 3D multi-attribute analysis is based on Kohonen self-organizing maps (SOM), which are one of the most...
2013
Extended Abstract: Sub-seismic Resolution in the Eagle Ford Enabled by Multi-Attribute Analysis Using Instantaneous, Geometric, and Spectral Decomposition Self Organizing Maps
Patricia A. Santogrossi
Houston Geological Society Bulletin
... Decomposition Self Organizing Maps Patricia A. Santogrossi 2016 33 35 Vol. 58 (2016) No. 6. (February) Figure 1. Multi-Attribute Analysis’ Sub-seismic...
2016
Extended Abstract: Sub-seismic Resolution in the Eagle Ford Enabled by Multi-Attribute Analysis Using Instantaneous, Geometric, and Spectral Decomposition Self Organizing Maps
Patricia A. Santogrossi
Houston Geological Society Bulletin
...Extended Abstract: Sub-seismic Resolution in the Eagle Ford Enabled by Multi-Attribute Analysis Using Instantaneous, Geometric, and Spectral...
2016
Unsupervised seismic facies classification applied to a presalt carbonate reservoir, Santos Basin, offshore Brazil
Danilo Jotta Ariza Ferreira, Wagner Moreira Lupinacci, Igor de Andrade Neves, João Paulo Rodrigues Zambrini, André Luiz Ferrari, Luiz Antonio Pierantoni Gamboa, and Maria Olho Azul
AAPG Bulletin
... multi-attribute analysis using an unsupervised classification algorithm to map the carbonate facies of an exploratory presalt area located in the Outer...
2019
Machine Learning using Multiple Seismic Attributes could be the Paradigm Shift in the Interpretation Process
Deborah K. Sacrey
GCAGS Transactions
..., Natural clusters in multi-attribute seismics found with self-organizing maps: Extended Abstracts, Robinson-Treitel Spring Symposium, Geophysical...
2018
Machine Learning Applied to 3-D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara
Carolan Laudon, Sarah Stanley, Patricia Santogrossi
Unconventional Resources Technology Conference (URTEC)
... into interpretation and analysis. Recent developments with machine learning have added new capabilities to multi-attribute seismic analysis. In 2018...
2019
Seismic Facies Analysis Using Generative Topographic Mapping; #41717 (2015)
Satinder Chopra, Kurt Marfurt
Search and Discovery.com
... using Kohonen's self-organizing maps (SOM), and organizing it into clusters on a 2D map. Such methods are computationally fast and inexpensive. However...
2015
Abstract: Formation Evaluation of Barnett Shale by Kohonen Self Organizing Maps – An Example from North East Fort Worth Basin, by Atish Roy, Roderick Perez, and Kurt J. Marfurt; #90124 (2011)
Search and Discovery.com
2011
Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells
Carolan Laudon, Jie Qi, Yin-Kai Wang
Unconventional Resources Technology Conference (URTEC)
... the fault interpretation time from weeks/days to days/hours. Multi-attribute analysis accelerates the process of high grading reservoir sweet spots...
2022
Evaluation Method of Low Permeability Reservoirs Based on Logging Petrophysical Facies Identification: A Case Study of the Upper Member of Mengyin Formation in Gaoqing Area, Dongying Depression; #42448 (2019)
Ya Wang, Shaochun Yang, Yan Lu
Search and Discovery.com
.... 1) . ● The self-organizing map neural network algorithm has unique theoretical advantages in solving the pattern recognition problems with complex...
2019
Unsupervised Machine Learning Applications for Seismic Facies Classification
Satinder Chopra, Kurt J. Marfurt
Unconventional Resources Technology Conference (URTEC)
... anomalies using traditional interactive interpretation workflows. Specifically, we apply k-means, principal component analysis, self-organizing mapping...
2019
A Comparison of Popular Neural Network Facies Classification Schemes*
Christopher P. Ross, David M. Cole
GCAGS Transactions
... components analysis and self-organizing maps: Interpretation, v. 11, p. 59–83. Ronen, S., P. S. Schultz, M. Hattori, and C. Corbett, 1994, Seismic-guided...
2017
Application of LMR and Clustering Analysis in Unconventional Reservoirs; #40879 (2012)
Roderick Perez
Search and Discovery.com
... - International / October 11th, 2011 Monday, November 21, 11 37 Self Organizing Maps FACIES MAP GR 0 Cluster Assisted 3D and 2D unsupervised seismic...
Unknown
Lithostratigraphic Interpretation of Seismic Data for Reservoir Characterization; #90017 (2003)
Mahesh Chandra, A. K. Srivastava, V. Singh, D. N. Tiwari, P. K. Painuly
Search and Discovery.com
... for using Kohonen self organizing maps (K-SOM) for automated quantitative seismic facies analysis based on the various discriminating features...
2003
Identifying Flow Units of Low-permeability Sandstone Reservoirs Based on Self-organizing Map Neural Network Algorithm; #51610 (2019)
Yan Lu, Keyu Liu, Ya Wang
Search and Discovery.com
...Identifying Flow Units of Low-permeability Sandstone Reservoirs Based on Self-organizing Map Neural Network Algorithm; #51610 (2019) Yan Lu, Keyu Liu...
2019
Abstract: A Workflow for Selection of Stimulation Candidates in the Deep Basin; #90172 (2014)
Anne Valentine, Mahyar (Matt) Mohajer, Lynn Murphy
Search and Discovery.com
... © CSPG/CSEG/CWLS GeoConvention 2010, Calgary, Alberta, Canada, May 10-14, 2010 Figure 2: Self organizing map To test this hypothesis, we selected...
2014