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The AAPG/Datapages Combined Publications Database
Showing 324 Results. Searched 195,452 documents.
Prestack Seismic Data Inversion for Shale Gas Reservoir Characterization in China
Gang Yu, Yusheng Zhang, Ximing Wang, Xing Liang, Uwe Strecker, Maggie Smith
Unconventional Resources Technology Conference (URTEC)
... model along with interpreted horizons for structural control. The seismic velocity field provides low frequency impedance trends away from the well...
2016
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
Efficient subsurface carbon storage modeling with Fourier neural operator
Suraj Pawar, Pandu Devarakota, Faruk O. Alpak, Jeroen Snippe, Detlef Hohl
International Meeting for Applied Geoscience and Energy (IMAGE)
... accurately model the complex interplay of buoyancy, viscous, and capillary forces for large subsurface CO2 containers over long forecast periods. However...
2023
Digital Innovation in Subsea Integrity Management
Ricky Thethi, Dharmik Vadel, Mark Haning, Elizabeth Tellier
Australian Petroleum Production & Exploration Association (APPEA) Journal
... (RNN) and convolutional neural network (CNN) based algorithms have been found (Sundararaman et al. 2018) to work well in developing time domain stress...
2020
Abstract: Utilizing Seismic Attributes for Machine Assisted Fault Detection and Extraction; #91204 (2023)
Muhammad Khan, Yasir Bashir, Saleh Dossary, Syed Ali
Search and Discovery.com
... labelled data as transfer learning to update the foundation Convolutional Neural Network (CNN) model that was initially trained on synthetic data...
2023
ABSTRACT: Seismic Heterogeneity Cubes and Corresponding Equiprobable Simulations; #90013 (2003)
Matthias Imhof, William Kempner
Search and Discovery.com
... attributes. Instead, model statistics with only six parameters are fitted to the raw statistics. These six parameters include three orthogonal...
2003
Abstract: A Transfer Learning Approach to Rock Property Estimation Workflows;
Ahmad Mustafa, Motaz Alfarraj, Ghassan Alregib
Search and Discovery.com
.... This results in vertical discontinuities in the computed property volumes using such a model, since it becomes sensitive to lateral changes...
Unknown
Technical Article: Finding Subtle Traps with Seismic: Interpretative Criteria Clarified
A. Easton Wren
Petroleum Exploration Society of Australia (PESA)
... to what the section should look like. Progressive understanding of the seismic method introduced the concept of the convolutional model: this found...
1986
Methods of estimating wavelet stationarity, stabilizing non-stationarity, and evaluating its impact on inversion: A synthetic example using SEAM II Barrett unconventional model
Jesse Buckner, Michael Fry, Joe Zuech, Peter Harris, Bill Shea
International Meeting for Applied Geoscience and Energy (IMAGE)
... is simulated across a continuous 3D convolutional synthetic seismic volume, derived from the earth model of the SEAM II Barrett dataset. Multiple...
2023
Novel application of machine learning assisted fault interpretation to delineate earthquake risk from saltwater disposal in the Midland Basin
Niven Shumaker, Mohammed Afia
International Meeting for Applied Geoscience and Energy (IMAGE)
... seismic survey using a 3D convolutional neural for edge pixels in a 2D data array. Lines that pass through network (Abubakar et al. 2022). Fault points...
2023
Introduction to Deep Learning: Part I
Hongbo Zhou, Lasse Amundsen, Martin Landrø
GEO ExPro Magazine
... that showed that computers could perform tasks once thought to be solely the domain of human capability. However, lack of computer power soon stopped...
2017
Earthquake Detection and Focal Mechanism Calculation Using Artificial Intelligence
Shane Quimby, Yanwei Zhao, Jie Zhang, GeoTomo
Unconventional Resources Technology Conference (URTEC)
... network (FCN). FCNs are supervised deep learning networks based on convolutional layers, without being fully connected. This necessitates fewer model...
2022
Prestack Seismic Data Inversion for Shale Gas Reservoir Characterization in China; #41829 (2016)
Gang Yu, Yusheng Zhang, Uwe Strecker, Maggie Smith
Search and Discovery.com
... connected to well information through well-tie and wavelet extraction. Well data is also used in the low frequency model along with interpreted horizons...
2016
Transfer Learning Applied to Seismic Images Classification
Search and Discovery.com
N/A
Using machine learning to interpret 3D airborne electromagnetic inversions
Eldad Haber, Jen Fohring, Mike McMillan, Justin Granek
Petroleum Exploration Society of Australia (PESA)
... types of regularization and constraints to the model, but another approach is to learn what underlying structures or boundaries these smooth...
2019
Reservoir Modeling With Deep Learning
Search and Discovery.com
N/A
Abstract: FaciesNet: Machine Learning Applications for Facies Classification in Well Logs;
Chayawan Jaikla, Pandu Devarakota, Neal Auchter, Mohamed Sidahmed, Irene Espejo
Search and Discovery.com
... information, facies stacking pattern, and geologic correlations, FaciesNet. Our proposed model incorporates decoding and encoding deep convolutional...
Unknown
Post Migration Processing of Seismic Data
Dashuki Mohd.
Geological Society of Malaysia (GSM)
... or multiples. The basis for deconvolution is the convolutional model (Robinson, 1984). In the convolutional model, a seismic trace is viewed...
1994
3D seismic image-to-image translation
Xiaolei Song, Muhong Zhou, Lifeng Wang, Rodney Johnston
International Meeting for Applied Geoscience and Energy (IMAGE)
... by adopting two convolutional Bayesian layers as the network output layers to analyze the model uncertainties by calculating an uncertainty map from a local...
2023
Unconventional Reservoir Microstructural Analysis Using SEM and Machine Learning
Amanda S. Knaup, Jeremy D. Jernigen, Mark E. Curtis, John W. Sholeen, John J. Borer IV, Carl H. Sondergeld, Chandra S. Rai
Unconventional Resources Technology Conference (URTEC)
... specifically Convolutional Neural Networks (CNN), are being used for pixel labeling and feature identification using the CNN U-Net. This network...
2019
A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data
Hamzeh Alimohammadi, Hamid Rahmanifard, and Shengnan Nancy Chen
AAPG Bulletin
... at a batch size of 20. Figure 5. Optimum number of batch size (A) and dropout rate (B) for hybrid convolutional neural networks–long short-term memory model...
2022
Seismic Facies Segmentation Using Deep Learning; #42286 (2018)
Daniel Chevitarese, Daniela Szwarcman, Reinaldo Mozart D. Silva, Emilio Vital Brazil
Search and Discovery.com
... selected a trained convolutional neural network (CNN) with the highest accuracy on the classification task. Then, we modified the final part...
2018
Perceptual quality-based model training under annotator label uncertainty
Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib
International Meeting for Applied Geoscience and Energy (IMAGE)
...Perceptual quality-based model training under annotator label uncertainty Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib Perceptual quality-based...
2023
Convolution Neural Networks If They can Identify an Oncoming Car, can They Identify Lithofacies in Core?; #42312 (2018)
Rafael Pires de Lima, Fnu Suriamin, Kurt Marfurt, Matthew Pranter, Gerilyn Soreghan
Search and Discovery.com
... drive our cars but also taste our beer. Specifically, recent advances in the architecture of deep-learning convolutional neural networks (CNN) have...
2018
Abstract: Towards the Identification of Coal Macerals through Deep Learning
Na Xu, Qingfeng Wang, Pengfei Li, Mark A. Engle
The Society for Organic Petrology (TSOP)
... are compared with the other three existing image segmentation methods, including K-means [4], Gaussian mixture model (GMM), [5] and convolutional neural...
2023