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The AAPG/Datapages Combined Publications Database
Showing 2,442 Results. Searched 200,756 documents.
ABSTRACT: A Review of Applications of Artificial Intelligence for Predictive Analysis in Petrophysics - Practical Example Using Symbolic Regression; #90115 (2010)
Olivier Malinur
Search and Discovery.com
... are neural networks usually used for predicting missing values such as permeability. Self organizing maps, a class of unsupervised neural networks...
2010
Enhancing Limited Log Suites with Neural Networks
J.S. Arbogast, M.H. Franklin, M.L. Butler, K.A. Thompson
Rocky Mountain Association of Geologists
...Enhancing Limited Log Suites with Neural Networks J.S. Arbogast, M.H. Franklin, M.L. Butler, K.A. Thompson 155 Enhancing Limited Log Suites...
1997
Depositional Facies Identification in Wireline Log Patterns Using 1D Convolutional Neural Network (CNN) Deep Learning Algorithms
Galatio Giovani Prabowo, Muhammad Fahmi Ramdani, Abiyyu Daffa Revanzha, Brian Muara Sianturi, Natalia Angel Momongan
Indonesian Petroleum Association
... erroneous data. Therefore, non-linear activation functions are preferred in neural networks to address the complexities inherent in real-world data better...
2024
Attention-based self-calibrated convolution neural network for efficient facies classification
Motaz Alfarraj
International Meeting for Applied Geoscience and Energy (IMAGE)
... and production operations. Deep convolutional neural networks have been widely used for seismic interpretation tasks including detection, classification...
2024
Prediction of the Flowing Bottom-Hole Pressure Using Advanced Data Analytics
Mahshid Firouzi, Suren Rathnayake
Unconventional Resources Technology Conference (URTEC)
... network-based approaches. Overall, neural networks resulted in the best predictions with the root mean squared error (RMSE) within 198 - 450 kPa...
2019
Applications of Artificial Intelligence in Log Analysis: Chapter 7
John H. Doveton
AAPG Special Volumes
... gives the core concept of artificial neural networks. Obviously we cannot hope to create a model that is even a feeble approximation of the human...
1994
GeoMind: An intelligent earth model building tool
Saleh Al Saleh, Ewenet Gashawbeza, Mustafa Marzooq, Hussam Banaja, Husain Al Shakhs, Jianwu Jiao
International Meeting for Applied Geoscience and Energy (IMAGE)
..., etc. 2. Generating scenarios of each model from the previous step using unsupervised Generative Adversarial Neural Networks (GANNs). Each cell...
2022
Introduction to Deep Learning: Part I
Hongbo Zhou, Lasse Amundsen, Martin Landrø
GEO ExPro Magazine
... networks or ANNs (weighted decision paths), which are electronic networks of ‘neurons’ loosely analogous to the neural structure of the brain...
2017
Automated detection of ferromagnetic pipelines from magnetic total-field anomaly data using convolutional neural networks
Brett Bernstein, Yaoguo Li, Richard Hammack
International Meeting for Applied Geoscience and Energy (IMAGE)
...Automated detection of ferromagnetic pipelines from magnetic total-field anomaly data using convolutional neural networks Brett Bernstein, Yaoguo Li...
2023
Abstract: Object Detection in SEM Images Using Convolutional Neural Networks: Application on Pyrite Framboid Size-Distribution in Fine-Grained Sediments;
Artur Davletshin, Lucy Tingwei Ko, Kitty Milliken, Priyanka Periwal, Wen Song
Search and Discovery.com
...Abstract: Object Detection in SEM Images Using Convolutional Neural Networks: Application on Pyrite Framboid Size-Distribution in Fine-Grained...
Unknown
Abstract: Neural Networks Facilitate Precise at - Bit Formation Detection Suitable for Deployment in Automated Drilling Systems; #91204 (2023)
Lucas Katzmann, Stefan Wessling, Matthew Forshaw, Joern Koeneke
Search and Discovery.com
...Abstract: Neural Networks Facilitate Precise at - Bit Formation Detection Suitable for Deployment in Automated Drilling Systems; #91204 (2023) Lucas...
2023
Viability of long-short term memory neural networks for seismic refraction first break detection a preliminary study
Tasman Gillfeather-Clark, Eun-Jung Holden, Daniel Wedge, Tom Horrocks, Carlie Byrne, Matthew Lawrence
Petroleum Exploration Society of Australia (PESA)
...Viability of long-short term memory neural networks for seismic refraction first break detection a preliminary study Tasman Gillfeather-Clark, Eun...
2019
Development of a Data-Driven Operational Design Tool for CO2 Sequestration in Shale Gas Reservoirs
Search and Discovery.com
N/A
Realistic synthetic data generation using neural style transfer: Application to automatic fault interpretation
Min Jun Park, Joseph Jennings, Bob Clapp, Biondo Biondi
International Meeting for Applied Geoscience and Energy (IMAGE)
.../10.1190/geo2020-0945.1. Gatys, L., A. S. Ecker, and M. Bethge, 2015, Texture synthesis using convolutional neural networks: Advances in Neural...
2022
Abstract: Application of Seismic Stratigraphy, Multi-attribute Analysis and Neural Networks to Mitigate Risk in New Exploration Frontiers West Newfoundland Example; #90172 (2014)
Valentina Baranova, Azer Mustaqeem, Friso Brouwer
Search and Discovery.com
...Abstract: Application of Seismic Stratigraphy, Multi-attribute Analysis and Neural Networks to Mitigate Risk in New Exploration Frontiers West...
2014
ABSTRACT: Application of Chimney Cubes in the Design of Geochemical Surveys; #90013 (2003)
ROAR HEGGLAND
Search and Discovery.com
... of chimneys using neural networks (Meldahl et al., 1999 and Heggland et al., 1999). The method has been applied on post stack 3D seismic data to reveal...
2003
Semi-automated prestack seismic inversion workflow using temporal convolutional networks
Hussain Alfayez, Robert Smith, Ayman Suleiman, Nasher AlBinHasan
International Meeting for Applied Geoscience and Energy (IMAGE)
.... (2018) used Bayesianbased support vector machines (BSVM) to estimate velocity and density. Das et al. (2018) utilized convolutional neural networks (CNN...
2022
Generalization Capability of Data-driven Deep Learning Models for Seismic Full-waveform Inversion: An Example Using the OpenFWI Dataset
Ayrat Abdullin, Umair Bin Waheed
International Meeting for Applied Geoscience and Energy (IMAGE)
... perceptron (MLP) (Araya-Polo et al., 2018; Kim and Nakata, 2018), encoder-decoder based convolutional neural networks (CNNs) (Yang and Ma, 2019...
2023
RNN-based seismic velocity model building: Improving generalization using hybrid training data
Hani Alzahrani, Jeffrey Shragge
International Meeting for Applied Geoscience and Energy (IMAGE)
... (FWI). We present a multi-scale FWI-inspired approach that uses recurrent neural networks (RNNs) to invert frequency-domain seismic data using...
2022
Prediction and Analysis of Geomechanical Properties of the Upper Bakken Shale Using Artificial Intelligence and Data Mining
George K. Parapuram, Mehdi Mokhtari, Jalel Ben Hmida
Unconventional Resources Technology Conference (URTEC)
... velocity is first predicted by linear methods and neural networks. Shear wave velocity is crucial in making reliable calculations, especially...
2017
Sequence-to-Sequence (Seq2Seq) Long Short-Term Memory (LSTM) for Oil Production Forecast of Shale Reservoirs
Cristhian Aranguren, Alfonso Fragoso, Roberto Aguilera
Unconventional Resources Technology Conference (URTEC)
... is a methodology that integrates innovative and revolutionary machine learning techniques, which embed recurrent neural networks and Seq2Seq architectures commonly...
2022
Abstract: *Prediction of S-Wave Velocity using Machine Learning Algorithms Combined with Empirical Mode Decomposition-Based Approaches; #91202 (2022)
Said Gaci and Mohammed Farfour
Search and Discovery.com
..., multilayer perceptron neural networks (MLP ANN). Extended Abstract P- and S-wave velocity (Vp & Vs) bring manyuseful information to petroleum exploration...
2022
Mineralogical Estimation of Organic Rich Mudrocks from Well Logs Using Neural Networks: Overcoming Training Dataset Size Limitation by Integrating X-Ray Fluorescence Elemental Data; #42458 (2019)
Mustafa Al Ibrahim, Tapan Mukerji, Allegra Hosford Scheirer
Search and Discovery.com
...Mineralogical Estimation of Organic Rich Mudrocks from Well Logs Using Neural Networks: Overcoming Training Dataset Size Limitation by Integrating X...
2019
Predicting Permeability from Porosity Using Artificial Neural Networks
S. J. Rogers , H. C. Chen , D. C. Kopaska-Merkel , J. H. Fang
AAPG Bulletin
...Predicting Permeability from Porosity Using Artificial Neural Networks S. J. Rogers , H. C. Chen , D. C. Kopaska-Merkel , J. H. Fang 1995 1786 1796...
1995