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Introduction to Special Issue: Geoscience Data Analytics and Machine Learning
Michael J. Pyrcz
Three common statistical missteps we make in reservoir characterization
Frank Male, Jerry L. Jensen
Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling
Wen Pan, Honggeun Jo, Javier E. Santos, Carlos Torres-Verdín, Michael J. Pyrcz
Machine learning applications to seismic structural interpretation: Philosophy, progress, pitfalls, and potential
Kellen L. Gunderson, Zhao Zhang, Barton Payne, Shuxing Cheng, Ziyu Jiang, Atlas Wang
Unconventional reservoir characterization by seismic inversion and machine learning of the Bakken Formation
Jackson R. Tomski, Mrinal K. Sen, Thomas E. Hess, Michael J. Pyrcz
A cross-shape deep Boltzmann machine for petrophysical seismic inversion
Son Dang Phan, Mrinal K. Sen2
Application of random forest algorithm to predict lithofacies from well and seismic data in Balder field, Norwegian North Sea
Hoang Nguyen, Bérengère Savary-Sismondini, Virginie Patacz, Arnt Jenssen, Robin Kifle, Alexandre Bertrand
Deep convolutional neural networks for generating grain-size logs from core photographs
Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott Cole, Ishtar Barranco
Shale brittleness prediction using machine learning—A Middle East basin case study
Ayyaz Mustafa, Zeeshan Tariq, Abdulazeez Abdulraheem, Mohamed Mahmoud, Shams Kalam, Rizwan Ahmed Khan
Improving total organic carbon estimation for unconventional shale reservoirs using Shapley value regression and deep machine learning methods
Jaewook Lee, David E. Lumley, Un Young Lim
A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data
Hamzeh Alimohammadi, Hamid Rahmanifard, Shengnan (Nancy) Chen
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