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The AAPG/Datapages Combined Publications Database
Showing 55,899 Results. Searched 195,372 documents.
A deep learning workflow for petro-mechanical facies predictions in unconventionals
Noah R. Vento, Enru Liu, Mary Johns
International Meeting for Applied Geoscience and Energy (IMAGE)
... is a subset of machine learning using neural networks, to characterize facies using an unconventional dataset. A 1D U-Net is trained to predict PMFs...
2023
Application of Unsupervised Machine Learning Techniques in Detailed Recognition of Gas Producing Subtle Submarine Channel System; #42588 (2023)
Mariusz Łukaszewski
Search and Discovery.com
...Application of Unsupervised Machine Learning Techniques in Detailed Recognition of Gas Producing Subtle Submarine Channel System; #42588 (2023...
2023
Machine Learning Based Stereoscopic Triple Sweet Spot Evaluation Method for Shale Reservoirs
Yuxuan Deng, Wendong Wang, Xianfei Du, Yuliang Su, Shibo Sun, Yan Zhang, Jiancheng Teng
Unconventional Resources Technology Conference (URTEC)
... using lithologic and geomechanical facies classification. (Alshakhs and Rezaee, 2019) estimated key shale play properties including total organic carbon...
2023
Abstract: A Convolutional Neural Network for Vuggy Facies Classification from Borehole Images;
Jiajun Jiang, Dawn McAlpin, Chicheng Xu, Rui Xu, Scott James, Weichang Li
Search and Discovery.com
... the robustness of using microresistivity image logs in a deep-learning method to classify facies as either vuggy or non-vuggy. AAPG Datapages/Search...
Unknown
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, and Alexandre Bertrand
AAPG Bulletin
... to evaluate machine learning models on a limited data sample using a resampling procedure (Stone, 1974). One of the most common cross-validation methods...
2022
Stratigraphic Control on Oil Field Performance in Clastic Reservoirs of the Norwegian Continental Shelf: An Insight from Machine-learning Techniques, #30612 (2019).
Kachalla Aliyuda, John A. Howell, Adrian Hartley
Search and Discovery.com
...Stratigraphic Control on Oil Field Performance in Clastic Reservoirs of the Norwegian Continental Shelf: An Insight from Machine-learning Techniques...
2019
Technology Explained: Artificial Intelligence Its Use in Exploration and Production Part 2
Barrie Wells
GEO ExPro Magazine
... INTELLIGENCE – ITS USE IN EXPLORATION AND PRODUCTION PART 2 Machine Learning: SHUT TERSTOCK Magic or Mathematical Statistics? Dr Barrie Wells Conwy...
2022
Counterfactual uncertainty for high dimensional tabular dataset
Prithwijit Chowdhury, Ahmad Mustafa, Mohit Prabhushankar, Ghassan AlRegib
International Meeting for Applied Geoscience and Energy (IMAGE)
... of Electrical and Computer Engineering, Georgia Institute of Technology. SUMMARY With the advent of machine learning (ML) and deep learning in geophysics...
2023
Rapid History Matching of Petroleum Production from Well Logs and 4D Seismic via Machine Learning Techniques in the Norne Field, Offshore Norway
Jones Ebinesan, Greg Smith, Ritu Gupta
Australian Petroleum Production & Exploration Association (APPEA) Journal
...Rapid History Matching of Petroleum Production from Well Logs and 4D Seismic via Machine Learning Techniques in the Norne Field, Offshore Norway...
2023
Chancing Methods to Predict Porosity in a Middle Eastern Carbonate Reservoir from Full-Function Machine-Learning Neural Networks, Seismic Attributes and Inversions
Search and Discovery.com
N/A
Integrated Facies Modeling for Unconventional and Tight Reservoirs
Matt Campbell, Rachel Aisner-Williams, Taskin Akpulat, Jorge Estrada, Mark Kittridge, Mitch Pavlovic, Maksym Pryporov
Unconventional Resources Technology Conference (URTEC)
... through a machine-learning classification process to determine log-scale Petrophysical Rock Types (PRT) (Akpulat, 2019). Log-based PRTs provide high...
2022
An Integrated Deep Learning Solution for Petrophysics, Pore Pressure, and Geomechanics Property Prediction
Ehsan Zabihi Naeini, Sam Green, Marianne Rauch-Davies
Unconventional Resources Technology Conference (URTEC)
... such as porosity or URTeC 111 3 pore pressure. Machine learning improves on this by incorporating more information than using only the elastic properties...
2019
New Heterolithic Opportunities - Getting the Most out of a Mature Heartland with Boomerang Methodology and Machine Learning application
Search and Discovery.com
N/A
Transfer Learning Applied to Seismic Images Classification; #42285 (2018)
Daniel Chevitarese, Daniela Szwarcman, Reinaldo Mozart D. Silva, Emilio Vital Brazil
Search and Discovery.com
... the transfer learning technique in the seismic facies classification context, using the dataset pre-processing presented in Chevitarese et al. (2018a...
2018
Preliminary Prioritization on Steam Flood Injection in "Kasai" Heavy Oil Field Using Random Forest Regression Method
Muhammad Rafi, Muhammad Faiz, Auranisa Destya
Indonesian Petroleum Association
... to gross). The advantage of using this machine learning method is that it can process large amount of data, get feature selection for variable...
2023
Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study From an Unconventional Shale Reservoir
Ted Holden, Ruth Kurian, Mohammed Ibrahim, Daniel Hampson, Jonathan Downton
Unconventional Resources Technology Conference (URTEC)
... on a more traditional machine learning workflow using multilinear regression and hand selected attributes. Introduction The objectives of this study...
2023
A Comparative Study of Machine Learning Model Results and Key Geologic Parameters for Unconventional Resource Plays
Jeff Bowman, Hamed Tabatabaie, Julie Anna Bowman
Unconventional Resources Technology Conference (URTEC)
... reservoir quality maps. The focus of this study was on validating the results obtained from machine learning of production variables by using geological...
2021
Geological risk evaluation using the Support Vector Machine with examples from the late Triassic–early Jurassic structural play in western Sverdrup Basin, Canadian Arctic Archipelago
Zhuoheng Chen, Yexin Liu, Kirk Osadetz
CSPG Bulletin
... the unclassified sample belongs to. In this study, we use Support Vector Machine (SVM), a machine-learning technique, to perform the classification...
2012
Mineralogical composition and total organic carbon quantification using x-ray fluorescence data from the Upper Cretaceous Eagle Ford Group in southern Texas
Ahmed Alnahwi, and Robert G. Loucks
AAPG Bulletin
... the models using machine learning algorithms. Geological applications of machine learning or artificial intelligence include classification...
2019
Abstract: Implementation of Machine Learning for Petrophysical Reservoir Characterization in a complex Carbonate Reservoir, a Case study on Miocene Carbonate, offshore Gulf of Suez, Egypt; #91204 (2023)
Mona Farouk, Wael Shehata, Eslam Atwa, Mostafa Hagag
Search and Discovery.com
... reservoir engineering data. Different machine learning techniques were used to predict the flow units in uncored wells using the open hole logs including...
2023
Abstract: Weakly Supervised Structural Interpretation Using Projection Matrices for Latent Space Factorization;
Oluwaseun Joseph Aribido, Ghassan AlRegib
Search and Discovery.com
... learning have shown great potential in the field of seismic interpretation. Virtually all facets of deep learning have been combed to advance facies...
Unknown
Integrating a Minerals Systems Approach with Machine Learning: A Case Study of Modern Minerals Exploration in the Mt Woods Inlier northern Gawler Craton, South Australia
Mark Rieuwers, Antoine Caté
Petroleum Exploration Society of Australia (PESA)
...Integrating a Minerals Systems Approach with Machine Learning: A Case Study of Modern Minerals Exploration in the Mt Woods Inlier northern Gawler...
2019
Transfer Learning with Multiple Aggregated Source Models in Unconventional Reservoirs
J. Cornelio, S. Mohd Razak, Y. Cho, H-H. Liu, R. Vaidya, B. Jafarpour
Unconventional Resources Technology Conference (URTEC)
... field production rates for waterflooding using a machine learning-based proxy model. Journal of Petroleum Science and Engineering. Zhuang, F., Qi, Z...
2022
Deep Convolutional Neural Networks for Seismic Salt-Body Delineation; #70360 (2018)
Haibin Di, Zhen Wang, Ghassan AlRegib
Search and Discovery.com
... of the existing machine learning-based classification focuses on facies analysis, and a comparison of several unsupervised clustering/classification...
2018
Ensemble Learning: A Robust Paradigm for Data-Driven Modeling in Unconventional Reservoirs
Jared Schuetter, Srikanta Mishra, Luan Lin, Divya Chandramohan
Unconventional Resources Technology Conference (URTEC)
.... Geological Facies Prediction Using Computed Tomography in a Machine Learning and Deep Learning Environment. Unconventional Resources Technology Con...
2019