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The AAPG/Datapages Combined Publications Database
Showing 2,442 Results. Searched 200,685 documents.
Feature-based Probabilistic Interpretation of Geobodies from Seismic Amplitudes
J. Caers, B. G. Arpat, C. A. Garcia
AAPG Special Volumes
..., Appleton and Lange, 673 p.Haykin, S., 1999, Neural networks: A comprehensive foundation: Upper Saddle River, New Jersey, Prentice Hall, 842 p.Mallet, J.-L...
2006
ABSTRACT: Inferring Lithofacies from Well Logs by Applying Hybrid Neural Network-Hidden Markov Model Classifiers; #90017 (2003)
Piotr Mirowski, David McCormick
Search and Discovery.com
... Artificial Neural Networks (ANNs) and Hidden Markov Models (HMMs) schemes have proven to be a viable alternative to human interpretation when applied...
2003
Abstract: Evaluation of Hybrid Prediction Models for Accurate Rate of Penetration (ROP) Prediction in Drilling Operations; #91206 (2023)
Abdelhakim Khouissat, Youcefi Mohamed Riad, Ghoulem Ifrene
Search and Discovery.com
... methods, such as artificial neural networks and genetic algorithms, to improve prediction accuracy...
2023
Neural network assisted responses simulation and data correction of array laterolog in invaded formations
Yueyang Han, Lei Wang, Donghan Hao, Xiyong Yuan, Nan Wang, Zhen Yang, Jianwen Zhou, Liwei Li
International Meeting for Applied Geoscience and Energy (IMAGE)
.... After comparing multiple groups of neural networks, it was found that a network with 5 hidden layers, each containing 20 neurons, achieves better...
2024
Abstract: Contribution of Artificial Neural Networks to the Characterization of Low Resistivity Hydrocarbon Reservoirs Using Well Log Data. An Example from the Algerian Sahara; #91210 (2025)
Leila Aliouane, Sid-Ali Ouadfeul
Search and Discovery.com
...Abstract: Contribution of Artificial Neural Networks to the Characterization of Low Resistivity Hydrocarbon Reservoirs Using Well Log Data...
2025
Artificial Intelligence Application on Seismic Data for Automatic First-Break Arrival Picking
Search and Discovery.com
N/A
Abstract: Recognizing facies in the Red River Formation of North Dakota using a Convolutional Neural Network; #90301 (2017)
Stephan H. Nordeng, Ian E. Nordeng, Jeremiah Neubert, Emily G. Sundell
Search and Discovery.com
... and texture. The reason for this new approach is the development of massively intricate algorithms known as Convolutional Neural Networks (CNN) or Deep...
2017
Geophysics and neural networks: learning from computer vision
Mark Grujic, Liam Webb, Tom Carmichael
Petroleum Exploration Society of Australia (PESA)
...Geophysics and neural networks: learning from computer vision Mark Grujic, Liam Webb, Tom Carmichael Geophysics and neural networks: learning from...
2019
Abstract: A First Attempt to Predict Delta System Dynamic with Artificial Neural Networks, by E. Puhl, O. C. Pedrollo, A. L. O. Borges, and R. D. Maestri; #90090 (2009).
Search and Discovery.com
2009
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
Abstract: Grain Segmentation and Region Mask Generation in Digital Rock Images Using Convolutional Neural Networks;
Rengarajan Pelapur, Arash Aghaei, Connor Burt, Bidur Bohara
Search and Discovery.com
...Abstract: Grain Segmentation and Region Mask Generation in Digital Rock Images Using Convolutional Neural Networks; Rengarajan Pelapur, Arash Aghaei...
Unknown
-- no title --
user1
Search and Discovery.com
... Lithotypes Classification with Convolutional Neural Networks Evgeny E. Baraboshkin1, Evdokiya A. Panchenko2, Andrey E. Demidov1, Ardiansyah...
Unknown
A novel technique for modeling fracture intensity: A case study from the Pinedale anticline in Wyoming
Patrick M. Wong
AAPG Bulletin
...., 1995, Neural networks for pattern recognition: New York, Oxford University Press, 504 p.Boerner, S., D. Gray, A. Zellou, D. Todorovic-Marinic, and G...
2003
ABSTRACT: SEISMIC MULTI-ATTRIBUTE ANALYSIS FOR FLUID SATURATION AND LITHOLOGY DISCRIMINATION IN THE HIBISCUS FIELD, TRINIDAD & TOBAGO
Sierra, J., González, K., Machado, O. and Landa, A, Wong, C., Rojas, G. and Carnevali, B.,
Geological Society of Trinidad & Tobago
... of Trinidad. A post-stack method, supported on model based inversion and neural networks, was followed. The lithology and fluid classification was performed...
2007
THE APPLICATION OF ARTIFICIAL NE
Search and Discovery.com
N/A
Geophysical Corner Articles from September 1996 — present, Compiled by Randy Ray
Search and Discovery.com
N/A
Prediction of Fracture Porosity from Well Log Data by Artificial Neural Network, Case Study: Carbonate Reservoir, DEPOK Field
Okok Wijaya, Pebrian Tunggal P, Ahmat Dafit Hasim, Depta Mahardika, Sungkono, Bagus Jaya S
Indonesian Petroleum Association
... has another parameter called neuron. In the neural network, flow may contain more than one hidden layer (Figure 1). Artificial neural networks...
2015
ABSTRACT: Flow-Unit Modeling Using Neural Networks, Logs, and Core in a Vuggy Dolomite Reservoir, Dagger Draw Field, New Mexico; #90017 (2003)
Bob Wikan H. Adibrata, Neil F. Hurley
Search and Discovery.com
...ABSTRACT: Flow-Unit Modeling Using Neural Networks, Logs, and Core in a Vuggy Dolomite Reservoir, Dagger Draw Field, New Mexico; #90017 (2003) Bob...
2003
High Resolution Seismic Data Derived From Prestack Inversion and Machine Learning to Accurately Position Horizontal Wells in the Midland Basin, Texas
Robert Meek, Buzz Davis, Hector Bello
Unconventional Resources Technology Conference (URTEC)
... the data is high enough quality to invert. Stephens et al, 2011, showed how prestack inversion combined with neural networks was able to map out brittle...
2017
ABSTRACT: Hybridization of artificial neural network and grey relational analysis for the prediction of slope stability
Ashanira Mat Deris, Badariah Solemon
Geological Society of Malaysia (GSM)
... 0.999 ROC value and 99% accuracy, compared to 0.929 and 91% for a single ANN model. Keywords: Statistical machine learning, artificial neural networks...
2021
Abstract: Mining Big Data Using Principal Component Analysis and Using Results to Find Oil and Gas with Neural Analysis of Multiple Seismic Attributes Machine Learning!; #90304 (2017)
Deborah Sacrey
Search and Discovery.com
... recognition process using unsupervised neural networks, and can reveal the natural clustering and patters in the data which often are distinct...
2017
Estimating CO2 saturation and porosity using the double difference approach based invertible neural network
Arnab Dhara, Mrinal K. Sen, Sohini Dasgupta
International Meeting for Applied Geoscience and Energy (IMAGE)
... difference approach. The use of Invertible Neural Networks (INNs) over other network architectures is motivated by the fact that INNs can produce comparable...
2023
Innovative Deep Autoencoder and Machine Learning Algorithms Applied in Production Metering for Sucker-Rod Pumping Wells
Peng Yi, Xiong Chunming, Zhang Jianjun, Zhang Yashun, Gan Qinming, Xu Guojian, Zhang Xishun, Zhao Ruidong, Shi Junfeng, Liu Meng, Wang Cai, Chen Guanhong
Unconventional Resources Technology Conference (URTEC)
.... The machine-learning model contains two neural networks: first, a deep autoencoder to extract the feature representations from all the dynamometer...
2019
Guided strategies for improving machine learning models applied to geophysical problems
Satyan Singh, Konstantin Osypov, Graham Baines, Mark Willis
International Meeting for Applied Geoscience and Energy (IMAGE)
...://doi.org/10 .3389/feart.2022.997788. Roth, G., and A. Tarantola, 1994, Neural networks and inversion of seismic data: Journal of Geophysical...
2024