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The AAPG/Datapages Combined Publications Database
Showing 57,483 Results. Searched 200,357 documents.
2023 Middle East Oil, Gas and Geosciences Show ; - Abstracts, #91204 (2023).
Search and Discovery.com
2023
Vision-based Sedimentary Structure Identification of Core Images using Transfer Learning and Convolutional Neural Network Approach
Baosen Zhang, Shiwang Chen, Yitian Xiao, Laiming Zhang, Chengshan Wang
Unconventional Resources Technology Conference (URTEC)
... Academy of Sciences, v. 115, p. E5716. Odi, U., and T. Nguyen, 2018, Geological Facies Prediction Using Computed Tomography in a Machine Learning...
2021
Microsoft Word - image2023_final (10).docx
J0381057
International Meeting for Applied Geoscience and Energy (IMAGE)
.... AlRegib, 2019, A machine learning benchmark for facies classification: Interpretation, 7, no. 3, 1A– T725, doi: https://doi.org/10.1190/INT-2018-0249.1...
Unknown
Solving seismic inverse problems by an unsupervised hybrid machine-learning approach
Mrinal K. Sen, Arnab Dhara
International Meeting for Applied Geoscience and Energy (IMAGE)
...Solving seismic inverse problems by an unsupervised hybrid machine-learning approach Mrinal K. Sen, Arnab Dhara Solving Seismic Inverse Problems...
2022
Advanced Joint Interpretation of Image Logs and Conventional Well Logs in Organic-Rich Mudrock Formations for Improved Formation Evaluation: Application to the Permian Basin
Andres Gonzalez, Sabyasachi Dash, Zoya Heidari
Unconventional Resources Technology Conference (URTEC)
... of image analysis techniques and detection of facies/rock classes using supervised and unsupervised machine learning techniques. The process...
2020
A Comparison of Popular Neural Network Facies Classification Schemes
Search and Discovery.com
N/A
Physics-Assisted Transfer Learning for Production Prediction in Unconventional Reservoirs
J. Cornelio, S. Mohd Razak, A. Jahandideh, Y. Cho, H-H. Liu, R. Vaidya, B. Jafarpour
Unconventional Resources Technology Conference (URTEC)
... using a machine learning-based proxy model. Journal of Petroleum Science and Engineering...
2021
This thin section doesnt exist: On the generation of synthetic petrographic datasets
Ivan Ferreira, Luis Ochoa, Ardiansyah Koeshidayatullah
International Meeting for Applied Geoscience and Energy (IMAGE)
... and Ardiansyah Koeshidayatullah*, King Fahd University of Petroleum and Minerals Summary With the increasing implementation of machine learning algorithms, data...
2022
Abstract: Source to Sink In Reservoir Prediction: Integrating Provenance Data and Machine Learning to Predict Reservoir Distribution; #91202 (2022)
James Lovell Kennedy, Jonathan Redfern, Stefan Schröder, and John Argent
Search and Discovery.com
...Abstract: Source to Sink In Reservoir Prediction: Integrating Provenance Data and Machine Learning to Predict Reservoir Distribution; #91202 (2022...
2022
Machine-Learning Assisted Production Allocation Using a 3-D Full Field Geochemical Model of Produced Oils in the Eagle Ford and Austin Chalk of South Texas
Jason Jweda, Hui Long, Eric Michael
Unconventional Resources Technology Conference (URTEC)
...Machine-Learning Assisted Production Allocation Using a 3-D Full Field Geochemical Model of Produced Oils in the Eagle Ford and Austin Chalk of South...
2021
Outcrop to Subsurface Reservoir Characterization of the Mississippian Sycamore/Meramec Play in the SCOOP Area, Arbuckle Mountains, Oklahoma, USA
Benmadi Milad, Roger Slatt
Unconventional Resources Technology Conference (URTEC)
... lithofacies and their classification using various machine learning methods. Outcrop lithofacies were first classified based on the field work rock...
2019
Accelerate Well Correlation with Deep Learning; #42429 (2019)
Bo Zhang, Yuming Liu, Xinmao Zhou, Zhaohui Xu
Search and Discovery.com
... units by their well log patterns. A machine learning algorithm like CNN is trained by a human interpreter to recognize these patterns and subsequently...
2019
Seismic data augmentation for automatic faults picking using deep learning
Nam Pham, Sergey Fomel
International Meeting for Applied Geoscience and Energy (IMAGE)
..., S., 1959, Information theory and statistics: Wiley. Liu, M., M. Jervis, W. Li, and P. Nivlet, 2020, Seismic facies classification using supervised...
2022
Earth Model Building in Real-Time with an Automated Machine Learning Framework - A Midland Basin Example
Altay Sansal, Muhlis Unaldi, Edward Tian, Gareth Taylor
Unconventional Resources Technology Conference (URTEC)
...Earth Model Building in Real-Time with an Automated Machine Learning Framework - A Midland Basin Example Altay Sansal, Muhlis Unaldi, Edward Tian...
2021
Use of Machine Learning Production Driver Cross-Sections for Regional Geologic Insights in the Bakken-Three Forks Play
T. Cross, K. Sathaye, J. Chaplin
Unconventional Resources Technology Conference (URTEC)
... dataset for the predictions, using Shapley values, a method becoming increasingly widespread within the machine learning community. Finally, we...
2021
Uncertainty Reduction in Modelling Deltaic Reservoirs Using Intelligent Sedimentological Prior Information
Search and Discovery.com
N/A
Generative modeling for inverse problems
Rami Nammour
International Meeting for Applied Geoscience and Energy (IMAGE)
... inversion using generative adversarial networks as a geological prior: First EAGE/PESGB Workshop Machine Learning, 1–3, doi: https://doi.org...
2022
Evaluation and Optimization of Completion Design using Machine Learning in an Unconventional Light Oil Play
Luisa Porras, Christopher Hawkes, Arshad Islam
Unconventional Resources Technology Conference (URTEC)
...Evaluation and Optimization of Completion Design using Machine Learning in an Unconventional Light Oil Play Luisa Porras, Christopher Hawkes, Arshad...
2020
Integrating deep directional resistivity with machine learning for improved well placement in the Nikaitchuq Field, North Slope Alaska
Christopher McCullagh, Joshua Zuber
International Meeting for Applied Geoscience and Energy (IMAGE)
...Integrating deep directional resistivity with machine learning for improved well placement in the Nikaitchuq Field, North Slope Alaska Christopher...
2023
Quantitative evaluation of deepwater fan hierarchy: Insights from full physics-based forward stratigraphic models
Ali Downard, Fabien Laugier, Sarah Wright, Tao Sun
International Meeting for Applied Geoscience and Energy (IMAGE)
.... In this work, we apply an unsupervised machine learning workflow to elements interpreted within a deepwater fan to test the scale-dependent consistency...
2022
Abstract: Modeling of Channel Stacking Patterns Controlled by NearWellbore Modeling; #91201 (2022)
Luis C. Escobar Arenas, Patrick Ronnau, Lisa Stright, Steve Hubbard, and Brian Romans
Search and Discovery.com
... learning. These results anchor points to correlate deep-water channels between wellbores using surface-based modeling. Machine learning workflows...
Unknown
Geological Realism of Deep Water Channel Reservoir Models with Intelligent Priors, #41011 (2012)
Temistocles Rojas, Vasily Demyanov, Mike Christie, Dan Arnold
Search and Discovery.com
... Machine Learning Techniques Environment Medicine Classification/Regression Capture non-linear multivariate relations Outline • Introduction...
2012
Abstract: Multiscale Reservoir Characterization in a Marine Transitional Setting: Leveraging Automated Core Data Integration and Digital 3D Outcrop Models; #91214 (2025)
Claudia Ruiz-Graham, Ailen Borya, Kevin Henao
Search and Discovery.com
... to automate lithological descriptions. Then, these descriptions are refined using thinsection petrography for detailed facies analysis. Facies...
2025
Implementation of Seismic Data Quality Characterisation Using Supervised Deep Learning
Joshua Thorp, Krista Davies, Julien Bluteau, Peter Hoiles
Australian Petroleum Production & Exploration Association (APPEA) Journal
...., and Day, A. (2019). Automatic QC of denoise processing using a machine learning classification. First Break 37, 51–58. Kleiss, E., and Wall, J...
2020
Applying deep learning for identifying bioturbation from core photographs
Eric Timmer, Calla Knudson, and Murray Gingras
AAPG Bulletin
... and availability of deep learning (DL) software have recently allowed the development, testing, and deployment of automated image classification...
2021