Welcome to the new Datapages Archives
Datapages has redesigned the Archives with new features. You can search from the home page or browse content from over 40 publishers and societies. Non-subscribers may now view abstracts on all items before purchasing full text. Please continue to send us your feedback at emailaddress.
AAPG Members: Your membership includes full access to the online archive of the AAPG Bulletin. Please login at Members Only. Access to full text from other collections requires a subscription or pay-per-view document purchase.
Welcome to the new Datapages Archives
Search Results > New Search > Revise Search
The AAPG/Datapages Combined Publications Database
Showing 57,453 Results. Searched 200,293 documents.
Abstract: A Novel Approach Combining Machine Learning & Classical Modeling Techniques to Characterize The Clastic Reservoirs In the Northern Area of Greater Burgan Field; #91204 (2023)
Reham Al-Houti, Jean-Michel Filak
Search and Discovery.com
... Generating geological models by using different methods where machine learning is combined with classical modelling techniques, has already been established...
2023
Abstract: Potential Applications of Deep Learning to Geosciences; #90306 (2017)
Pedro Mario Cruz e Silva
Search and Discovery.com
... revolution. Deep Learning (DL) is the Machine Learning (ML) technique enabling breakthroughs in several industrial, business, and scientific workflows...
2017
Machine learning applications to seismic structural interpretation: Philosophy, progress, pitfalls, and potential
Kellen L. Gunderson, Zhao Zhang, Barton Payne, Shuxing Cheng, Ziyu Jiang, and Atlas Wang
AAPG Bulletin
... or effective stress. Because of this, considerable effort has gone into improving the subsurface interpretation of salt bodies using machine learning approaches...
2022
Feature selection for seismic facies classification of a fluvial reservoir: Pushing the limits of spectral decomposition beyond the routine red-green-blue color blend
Ismailalwali Babikir, Mohamed Elsaadany, Maman Hermana, Abdul Halim Abdul Latiff, Muhammad Sajid, Carrie Laudon
International Meeting for Applied Geoscience and Energy (IMAGE)
... of their ability to express geologic patterns better than the original seismic, attributes are commonly used to input machine learning models for facies...
2022
Characterization of Depositional Facies Using Artificial Intelligence Method Based on Electrical Log Data
Muhammad Dhery Mahendra, Michella Ayu Pramesti, Vania Utami, Muhammad Rizqy Septyandy, Rezky Aditiyo
Indonesian Petroleum Association
...., Girelli, T. J., & Junior, F. C, 2020, Evaluation of machine learning methods for lithology classification using geophysical data. Computers...
2020
Supervised Learning Applied to Rock Type Classification in Sandstone Based on Wireline Formation Pressure Data; #42539 (2020)
Jose Victor Contreras
Search and Discovery.com
... with oral presentation given at 2019 AAPG Geoscience Technology Workshop, Boosting Reserves and Recovery Using Machine Learning and Analytics, Houston, Texas...
2020
Applying unsupervised multiattribute machine learning for 3D stratigraphic facies classification in a carbonate field, offshore Brazil
Gustavo Luan Cardoso, Alexandre Kolisnyk, Edgar Bronizeski, Elita de Abreu, Carolan Laudon
International Meeting for Applied Geoscience and Energy (IMAGE)
...Applying unsupervised multiattribute machine learning for 3D stratigraphic facies classification in a carbonate field, offshore Brazil Gustavo Luan...
2022
Reducing Uncertainty in Exploration and Development by Incorporating Targeted Machine Learning Interpretation Workflows in Jurassic Carbonate Reservoirs
Search and Discovery.com
N/A
Deep carbonate reservoir characterization with unsupervised machine-learning approaches
Xuanying Zhu, Luanxiao Zhao, Xiangyuan Zhao, Yuchun You, Minghui Xu, Tengfei Wang
International Meeting for Applied Geoscience and Energy (IMAGE)
...Deep carbonate reservoir characterization with unsupervised machine-learning approaches Xuanying Zhu, Luanxiao Zhao, Xiangyuan Zhao, Yuchun You...
2023
Extended Abstract: Machine Learning Facies Classification as Applied to the Upper Devonian Duvernay Formation, Western Canadian Sedimentary Basin
Elisabeth G. Rau, Kathy Breen, Scott C. James, Stacy C. Atchley, Anna M. Thorson
GCAGS Transactions
..., Extremely randomized trees: Machine Learning, v. 63, p. 3–42,
2019
Integrating machine learning and artificial intelligence for reservoir characterization of Z gas field, Lower Indus Basin, Pakistan
Muhammad Bilal Malik, M. Armaghan, Faisal Miraj, Maha Ali Haider, Muhammad Tallal Malik
International Meeting for Applied Geoscience and Energy (IMAGE)
... cube using machine learning techniques. • To model facies distribution and validate predictions against lithofacies data. • To assess the efficiency...
2024
-- no title --
user1
Search and Discovery.com
... Image Analysis Using Machine Learning and Image Processing Technologies for the Oil and Gas Industry Raua Al Maskari1, Frederic Knap1, Farhad Khalilzadeh2...
Unknown
Abstract: Evaluation of a Machine Learning Approach to Detecting Channels in Seismic Volumes; #91204 (2023)
Marc Servais, Estanislao Kozlowski, Graham Baines, Andrew Davies
Search and Discovery.com
... and density. Using the synthetic seismic volume as input and channel masks derived from the facies grids as labels, we adopt a supervised learning approach...
2023
A Supervised Machine-Learning Approach to Stratigraphic Surface Picking in Well Logs from the Mannville Group of Alberta, Canada; #42403 (2019)
Justin Gosses, Licheng Zhang
Search and Discovery.com
... and Beyond: AAPG Studies in Geology 64, Chapter: 7. Hall, B., 2016, Facies classification using machine learning: The Leading Edge, v. 35/10, p. 906-909. Olea...
2019
Automated Lithology Prediction From Core Images and Well Log Data Using Machine Learning Models: A Case Study From the Greater Schiehallion Area, West of Shetland, United Kingdom
Search and Discovery.com
N/A
Abstract: Geological Control in Machine Learning based Seismic Facies Recognition; #91204 (2023)
Sihai Zhang
Search and Discovery.com
...Abstract: Geological Control in Machine Learning based Seismic Facies Recognition; #91204 (2023) Sihai Zhang Geological Control in Machine Learning...
2023
Enhancing Lithology Classification through a Deep Learning Framework
P. Zhang, T. Gao, R. Li
Unconventional Resources Technology Conference (URTEC)
... to deliver impactful analyses. This research enhances rock facies classification and highlights the potential of merging virtual microscopy with machine...
2025
-- no title --
user1
Search and Discovery.com
... log facies interpretation was revisited using a machine learning approach. A prototype model and algorithm based on decision trees was applied...
Unknown
Improving 3D seismic facies interpretation with an advanced deep learning method utilizing both spatial and temporal dependencies
Miao Tian, Sumit Verma, Yining Gao
International Meeting for Applied Geoscience and Energy (IMAGE)
... Geologists REFERENCES Alaudah, Y., P. Michałowicz, M. Alfarraj, and G. AlRegib, 2019, A machine-learning benchmark for facies classification...
2024
Application of SVM machine learning high-resolution fusion inversion in stratigraphic correlation
Lyu Huaxing, Zhang Weiwei, Chen Zhaoming, Zhang Zhenbo, Liu Junyi, Xu Hao
International Meeting for Applied Geoscience and Energy (IMAGE)
...Application of SVM machine learning high-resolution fusion inversion in stratigraphic correlation Lyu Huaxing, Zhang Weiwei, Chen Zhaoming, Zhang...
2024
Abstract: Interactive Deep Learning Assisted Seismic Interpretation Technology Applied to Reservoir Characterization: A Case Study From Offshore Santos Basin in Brazil;
Ana Krueger, Bode Omoboya, Paul Endresen, Benjamin Lartigue
Search and Discovery.com
... propose a method to accelerate seismic interpretation using an interactive approach to deep learning that allows geoscientists to be in complete...
Unknown
Unsupervised machine learning for seismic facies classification using a 3D grid approach
David Manzano, Edgar Galvan, Dan Ferdinand Fernandez
International Meeting for Applied Geoscience and Energy (IMAGE)
...Unsupervised machine learning for seismic facies classification using a 3D grid approach David Manzano, Edgar Galvan, Dan Ferdinand Fernandez...
2024
Machine Learning Prediction for Fluid Identification in Kujung Formation East Java
Freddy Calvin Bryan, Violita Indrayani Putri, Ardian Nengkoda, Andy Noorsaman Sommeng, Sutrasno Kartohardjono
Indonesian Petroleum Association
... formation, therefore formation pressure was still influenced by hydrostatic pressure from drilling mud. After machine learning modelling using algorithms...
2024