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The AAPG/Datapages Combined Publications Database
Showing 2,441 Results. Searched 200,599 documents.
Volumetric supervised contrastive learning for seismic semantic segmentation
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib
International Meeting for Applied Geoscience and Energy (IMAGE)
..., 2021, Explainable seismic neural networks using learning statistics: Presented at the First International Meeting for Applied Geoscience & Energy...
2022
The Application of Semi-supervised Geobody Detection Technique using Multiple Seismic Attributes in Petroleum Exploration; #41165 (2013)
Lei Li, Xianhua Ran, Chunfeng Tao, Zhonghong Wan, and Shifan Zhan
Search and Discovery.com
... Quantization: Neural Networks 3, p. 277-291. Davis, J., 1986, Statistics and data analysis in geology, 2nd ed.,Wiley, 646 p. Dumay, J., and F. Fournier...
2013
Microseismic Calibration of Surface Seismic Brittleness Estimates: Application to a Barnett Shale Survey; #80623 (2017)
Roderick Perez-Altamar, John Henry Alzate, Amanda Trumbo, Kurt Marfurt
Search and Discovery.com
... ray volume obtained using artificial neural networks. In the lower Barnett high gamma ray values are possible zones of high TOC, indicated by orange...
2017
Merging Physics and Data-Driven Methods for Field-Wide Bottomhole Pressure Estimation in Unconventional Wells
Diego Molinari, Sathish Sankaran
Unconventional Resources Technology Conference (URTEC)
... for multiphase fluid flow modeling have also been developed based on pure data-driven models such as artificial neural networks (Osman, 2004), support vector...
2021
A Deep Learning-Based Surrogate Model for Rapid Assessment of Geomechanical Risks in Geologic CO2 Storage
Fangning Zheng, Birendra Jha, Behnam Jafarpour
Carbon Capture, Utilization and Storage (CCUS)
..., 2022). CCUS 4003166 3 State-of-the-art neural network architectures, including convolutional neural networks, U-Net, recurrent neural networks, LSTM...
2024
Use of Volume-Based 3-D Seismic Attribute Analysis to Characterize Physical-Property Distribution: A Case Study to Delineate Sedimentologic Heterogeneity at the Appleton Field, Southwestern Alabama, U.S.A.
Juliana M. Tebo, Bruce S. Hart
Journal of Sedimentary Research (SEPM)
...., Schultz, P. S., Hattori, M., and Corbett, C., 1994, Seismic-guided estimation of log properties: Part 2: Using artificial neural networks...
2005
Porosity prediction from seismic attributes of the Ordovician Trenton-Black River groups, Rochester field, southern Ontario
O. C. Ogiesoba
AAPG Bulletin
... and structure of the Rochester fault-related dolomite reservoir using 3-D seismic data and neural networks to predict porosity. By predicting porosity...
2010
Deep learning velocity model building using an ensemble regression approach
Stuart Farris, Guillaume Barnier, Robert Clapp
International Meeting for Applied Geoscience and Energy (IMAGE)
...). Various learning-based velocity model building methods are emerging which harness the ability of neural networks to approximate highly nonlinear...
2022
Enhancing Coal Quality Estimation Through Multiple Geophysical Log Analysis
Binzhong Zhou, Graham O'Brien
Petroleum Exploration Society of Australia (PESA)
...-dimensional space. There are many ways to tackle this problem, such as model regression and artificial neural networks. In this study, we will use a multi...
2016
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)
... variety of techniques such as artificial neural networks, high performance random forest, linear logistic regression, support vector regression...
2019
Prediction of Porosity and Permeability Using a Data Mining Approach: Appleton Field, Alabama
Wen-Tai Yang , Hui-Chuan Chen and Ernest A. Mancini
GCAGS Transactions
... classification of electrofacies: Mathematical Geology, v. 27, p. 3-22. Pao, Y.H., 1986, Adaptive Pattern Recognition and Neural Networks, Addison...
1999
Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells
Carolan Laudon, Jie Qi, Yin-Kai Wang
Unconventional Resources Technology Conference (URTEC)
... Deep Learning Convolution Neural Networks (CNN) with a conventional data pre-processing step and an image processing-based post processing approach...
2022
Rock Thin-section Analysis and Mineral Detection Utilizing Deep Learning Approach
Fatick Nath, Sarker Asish, Shaon Sutradhar, Zhiyang Li, Nazmul Shahadat, Happy R. Debi, S M Shamsul Hoque
Unconventional Resources Technology Conference (URTEC)
... classical artificial neural networks and random forest classifiers-based model for detecting mineralogy and porosity in petrographic thin slices...
2023
Mitigating elastic effects of acoustic full-waveform inversion with deep learning and application to field data
Dimitri Voytan, Adriano Gomes, Debanjan Datta, Ren-douard Plessix, Anu Chandran, Ken Matson
International Meeting for Applied Geoscience and Energy (IMAGE)
... in geophysics using deep neural networks: 82nd Annual International Conference and Exhibition, EAGE, Extended Abstracts, 1–5. Zhu, J.-Y., T. Park, P...
2022
Mapping the Natural Fracture Network in Utica Shale Using Artificial Intelligence (AI)
Shahab D. Mohaghegh
Unconventional Resources Technology Conference (URTEC)
... networks that together can correlate the seismic attributes to the NFN Density (one of such networks is shown in Figure 31), 5. Deploy the trained neural...
2017
Reducing Uncertainties In Shear Wave Petrophysical Log Prediction By Using Deep Neural Network and Machine Learning Methods
Muhammad Faris Abdurrachman, Muhammad Dhafit Muhsin
Indonesian Petroleum Association
...Reducing Uncertainties In Shear Wave Petrophysical Log Prediction By Using Deep Neural Network and Machine Learning Methods Muhammad Faris...
2021
Production Forecasting in Shale Reservoirs through Conventional DCA and Machine/Deep Learning Methods
Cenk Temizel, Celal Hakan Canbaz, Onder Saracoglu, Dike Putra, Ali Baser, Tomi Erfando, Shanker Krishna, Luigi Saputelli
Unconventional Resources Technology Conference (URTEC)
... limitations.. Available unconventional tight-shale reservoir data is analyzed by an artificial recurrent neural network (RNN) architecture such as LSTM...
2020
Enhancing reservoir simulation workflows by coupling a GPU-accelerated full-physics simulator with a Fourier neural operator-based surrogate model
Karthik Mukundakrishnan, Klaus Wiegand, Vidyasagar Ananthan, Daniel Kahn, Dmitriy Tischekin, Marcos Kajita, Clement Etienam
International Meeting for Applied Geoscience and Energy (IMAGE)
...Enhancing reservoir simulation workflows by coupling a GPU-accelerated full-physics simulator with a Fourier neural operator-based surrogate model...
2024
Fast-Track and Robust Reservoir Modeling Using Probabilistic Neural Network; #42205 (2018)
Islam A. Mohamed, Basem K. Abd El-Fattah
Search and Discovery.com
...Fast-Track and Robust Reservoir Modeling Using Probabilistic Neural Network; #42205 (2018) Islam A. Mohamed, Basem K. Abd El-Fattah Fast-Track...
2018
An application of FWI with progressive transfer learning
Shirui Wang, Jinjun Liu, Yuchen Jin, Xuqing Wu, Jiefu Chen
International Meeting for Applied Geoscience and Energy (IMAGE)
... and Convolutional Neural Networks (CNNs) have been used in very broad areas. Encouraged by the promising potential, scientists have been using deep learning...
2022
Explainable machine learning for hydrocarbon prospect risking
Ahmad Mustafa, Ghassan AlRegib
International Meeting for Applied Geoscience and Energy (IMAGE)
..., and G. E. Hinton, 2017, ImageNet classification with deep convolutional neural networks: Communications of the ACM, 60, 84–90, doi: https://doi.org...
2022
Seismic Data Compression by Variational Autoencoder With Hyperprior
Shirui Wang, Wenyi Hu, Aria Abubakar, Xuqing Wu, Jiefu Chen
International Meeting for Applied Geoscience and Energy (IMAGE)
...l transformations depicted in Fig. 1 with fully parameterized deep neural networks, custom compressors for specific datasets can be constructe...
2023
Unsupervised clustering of frequently repeated 4D seismic data for delineation of CO2 plume development
Boshara M. Arshin Sukar, Colin MacBeth
International Meeting for Applied Geoscience and Energy (IMAGE)
...: Sschematic cartoon shows the proposed well2seisML workflow. SOM uses neural networks nodes that learn the topology of the training data. The method starts...
2023
Facies classification with different machine learning algorithm An efficient artificial intelligence technique for improved classification
Partha Pratim Mandal, Reza Rezaee
Petroleum Exploration Society of Australia (PESA)
.... Mandal and Rezaee Bhatt, A. (2002). Reservoir properties from well logs using neural networks. PhD. Dissertation, Department of Petroleum Engineering...
2019
Supervised vs unsupervised deep learning for time-lapse seismic repeatability enforcement
Son Phan, Wenyi Hu, Aria Abubakar
International Meeting for Applied Geoscience and Energy (IMAGE)
... datasets. In both cases, the deep learning algorithms are designed with layers of convolutional neural networks, aiming to retain only the time-lapse...
2024