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The AAPG/Datapages Combined Publications Database
Showing 55,891 Results. Searched 195,354 documents.
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
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
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
Stratigraphic constraint for deep learning image segmentation of geological units
Boillot Lionel, Thouvenot Alexandre, Kuhn de Chizelle Julien, Guillon Sébastien
International Meeting for Applied Geoscience and Energy (IMAGE)
... Alaudah, Y., P. Michalowicz, M. Alfarraj, G. AlRegib, 2019, A machine learning benchmark for facies classification: Interpretation, 7, no. 3, 1A– T725, doi...
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
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
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
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
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
Investigating Shelf Margin Delta Exploration Plays Using Successful Machine Learning Techniques: Columbus Basin, Trinidad and Tobago
Search and Discovery.com
N/A
A Machine-learning-assisted 3D Geologic Model for the Late Devonian Duvernay Formation, East Shale Basin, Western Canada
Search and Discovery.com
N/A
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
Abstract: Can Machine Learning Help Predict Channel Stacking Patterns in Deep-Water Systems?;
Noah Francis Vento, Lisa Stright, Stephen Hubbard, Brian Romans
Search and Discovery.com
... overall more successful for classifying channel body position than the unsupervised learning algorithms for both facies models. The classification...
Unknown
What samples must seismic interpreters label for efficient machine learning?
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
International Meeting for Applied Geoscience and Energy (IMAGE)
..., 2019a, A machine-learning benchmark for facies classification: Interpretation, 7, no. 3, SE175–SE187, doi: https://doi.org/10.1190/INT-2018-0249.1...
2023
The Power of Predictive Analytics in Oil Field Development: Integrating Machine Learning with Reservoir Hydrocarbon Data to Enable Enhanced Oil Recovery of Hugin Formation within the Theta Vest Structure
Lilik T. Hardanto, Fenny Chrisman
Indonesian Petroleum Association
...-lapse/4D seismic data to perform facies classification instead of conventional approaches. Machine learning can enhance the efficiency of Enhanced Oil...
2021
Abstract: Deep Learning Seismic Inversion for Reservoir Facies- Data Driven Workflow; #91204 (2023)
Bilal Saeed Syed, Samiran Roy
Search and Discovery.com
...Abstract: Deep Learning Seismic Inversion for Reservoir Facies- Data Driven Workflow; #91204 (2023) Bilal Saeed Syed, Samiran Roy Deep Learning...
2023
Breakthrough Business Opportunities in the Oil Industry for YPs and Small Team Geoscientists; #70325 (2018)
Susan Nash
Search and Discovery.com
... to performance history / history matching Probabilistic Rock Type Classification (Paradigm) • Generate probabilistic facies volumes calibrated...
2018
Introduction to Special Issue: Geoscience Data Analytics and Machine Learning
Michael J. Pyrcz
AAPG Bulletin
...Introduction to Special Issue: Geoscience Data Analytics and Machine Learning Michael J. Pyrcz 2022 2145 2148 106 11 MOTIVATION FOR THIS SPECIAL...
2022
Abstract: Integrating Petrophysics, Seismic Attributes, and Machine Learning for Shale Facies Identification and Prediction;
Justin Palmer, Lisa Goggin, Adam Halpert, Christopher Skelt, Laura Bandura, Huafeng Liu
Search and Discovery.com
...Abstract: Integrating Petrophysics, Seismic Attributes, and Machine Learning for Shale Facies Identification and Prediction; Justin Palmer, Lisa...
Unknown
Inversion Case Studies From the SCOOP and STACK Areas in the Anadarko Basin
Search and Discovery.com
N/A
Leveraging Unsupervised Machine Learning on Seismic Images of Miocene Deposits in the Carpathian Foredeep Basin for Facies Classification
Reva Wiratama, Fikrah Elhifzi Harahap, Imoleayo Fashagba, Kamil Cichostępski
Indonesian Petroleum Association
...Leveraging Unsupervised Machine Learning on Seismic Images of Miocene Deposits in the Carpathian Foredeep Basin for Facies Classification Reva...
2022
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
Sweet Infill Well Locations Prediction Using Multiple Supervised and Unsupervised Machine Learning Models
Mochammad Naufal Septifiandi, Ferdiansyah Rahman, Muhammad Husni Mubarak Lubis
Indonesian Petroleum Association
...Sweet Infill Well Locations Prediction Using Multiple Supervised and Unsupervised Machine Learning Models Mochammad Naufal Septifiandi, Ferdiansyah...
2023
Unsupervised Machine Learning Applications for Seismic Facies Classification
Satinder Chopra, Kurt J. Marfurt
Unconventional Resources Technology Conference (URTEC)
...Unsupervised Machine Learning Applications for Seismic Facies Classification Satinder Chopra, Kurt J. Marfurt URTeC: 557 Unsupervised Machine...
2019
An Innovative and Simple Approach to Spatially Evaluate a Proxy for Stress Shadow Effect of Unconventional Reservoir Completion Activity
Shane J. Prochnow, Alena Grechishnikova, Paymon Hossaini, Mohamed Ibrahim, Sandra Saldana
Unconventional Resources Technology Conference (URTEC)
... investigated the impact of completion parameters on the production performance of the wells in the Eagle Ford Shale by using machine learning models. Kong...
2022