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The AAPG/Datapages Combined Publications Database
Showing 624 Results. Searched 200,685 documents.
Implementation of Seismic Data Quality Characterisation Using Supervised Deep Learning
Joshua Thorp, Krista Davies, Julien Bluteau, Peter Hoiles
Australian Petroleum Production & Exploration Association (APPEA) Journal
... convolutional autoencoders. Geophysics 83, A39–A43. doi:10.1190/geo2017-0524.1 Tishchenko, I. (2016). Different methods of QC the low frequency content...
2020
Geostatistical Integration of Crosswell Data for Carbonate Reservoir Modeling, Mcelroy Field, Texas
William M. Bashore, Robert T. Langan, Karla E. Tucker, Paul J. Griffith
Special Publications of SEPM
... structures in order to be useful for the inversion process. The inversion is performed in the frequency domain, which requires the low-frequency model...
1995
Velocity continuation with Fourier neural operators for accelerated uncertainty quantification
Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann
International Meeting for Applied Geoscience and Energy (IMAGE)
... in the background squared-slowness model. Uncertainty quantification is essential for determining how variability in the background models affects seismic...
2022
Fault MLReal: A fault delineation study for the Decatur CO2 field data using neural network predicted passive seismic locations
Hanchen Wang, Yinpeng Chen, Tariq Alkhalifah, Youzuo Lin
International Meeting for Applied Geoscience and Energy (IMAGE)
... and performance of Convolutional Neural Networks (CNN). Considering we have labeled training data, referred to in domain adaptation circles...
2023
A Quantitative Application of Seismic Inversion and Multi-Attribute Analysis based on Rock Physics Linear Relationships to identify High Total Organic Carbon Shale - A Case Study from the Perth Basin, Western Australia
Y. Altowairqi, R. Rezaee, B. Evans, M. Urosevic
Unconventional Resources Technology Conference (URTEC)
...-attribute analysis is applied to predict TOC from a model-based inversion and used the AI as external attribute. A total of eight seismic attributes were...
2017
Research on first break picking based on deep learning for DAS-VSP data
Naijian Wang, Yinpo Xu, Yuxin Hou, Yingjie Pan, Mingxing Wang, Chun Zhang, Tianfu Yang
International Meeting for Applied Geoscience and Energy (IMAGE)
... of whole zone are covered; thirdly, the U-Net network is improved by adjusting the model hierarchy, optimizing the incentive function and adding batch...
2024
Internal multiple elimination with an inverse-scattering theory guided deep neural network
Zhiwei Gu, Liurong Tao, Haoran Ren, Ru-Shan Wu, Jianhua Geng
International Meeting for Applied Geoscience and Energy (IMAGE)
... with the convolutional operation. Combining the CNN with the autoencoder can improve the feature extraction ability of the network model and have higher computational...
2022
Auto-identification and Real-time Warning Method of Multiple Type Events During Multistage Horizontal Well Fracturing
Mingze Zhao, Yue Li, Yuyang Liu, Bin Yuan, Siwei Meng, Wei Zhang, He Liu
Unconventional Resources Technology Conference (URTEC)
... identification and real-time warning method of multiple types of events during multi-stage fracturing. A new intelligent identification model is developed...
2023
Deep learning software accelerators for full-waveform inversion
Sergio Botelho, Souvik Mukherjee, Vinay Rao, Santi Adavani
International Meeting for Applied Geoscience and Energy (IMAGE)
...-difference time domain (FDTD) method (Louboutin et al., 2019; Luporini et al., 2020). For preliminary experiments, we will use a velocity model...
2022
High-fidelity GPR image super-resolution via deep-supervised machine learning
Kai Gao, Carly M. Donahue, Bradley G. Henderson, Ryan T. Modrak
International Meeting for Applied Geoscience and Energy (IMAGE)
... migration images. To achieve this task, we adopt an attention-based residual convolutional neural network as the backbone (Bi et al., 2021), which uses...
2022
Pyseis: A high-performance, user-friendly Python package for GPU-accelerated seismic modeling and subsurface imaging
Stuart Farris, Guillaume Barnier, Ettore Biondi, Robert Clapp
International Meeting for Applied Geoscience and Energy (IMAGE)
... NVIDIA V100 GPU. In these tests, we kept the number of time steps constant while varying the size of the model domain, a realistic scenario where high...
2023
Training data versus deep learning architectures in the seismic fault attribute computation
Bo Zhang, Yitao Pu, Zhaohui Xu, Naihao Liu, Shizhen Li, Fangyu Li
International Meeting for Applied Geoscience and Energy (IMAGE)
...” and predicted result of side outputs (o1-o4). The model generates a “fuse” set to combine the side outputs at different scales. It is common that one...
2022
Abstract: Harmonic Decomposition of a Vibroseis Sweep Using Gabor Analysis; #90174 (2014)
Christopher B. Harrison, Gary Margrave, Michael Lamoureux, Art Siewert, and Andrew Barrett
Search and Discovery.com
... (left) and the frequency domain (right) individual results (magenta) of time-dependent Gabor decomposition with respects to the fundamental, H2, H3, H4...
2014
From Chaos to Caves An Evolution of Seismic Karst Interpretation at the Vorwata Field
Riangguna Eloni, M.R. Husni Sahidu, Ilham Panggeleng, Christopher S. Birt, Ted Manning
Indonesian Petroleum Association
...’ between layers. It is often preferable to transform the reflectivity data into the impedance domain because impedance can be used to approximate...
2016
Automated machine learning first-break picking in the Sichuan Basin A case study
Jianfa Wu, Xuewen Shi, Qiyong Gou, Ersi Xu, Dongjun Zhang, Dingxue Wang, Phil Bilsby, Qing Zhou, Rong Li
International Meeting for Applied Geoscience and Energy (IMAGE)
... to the various machine learning model architectures employed and demonstrate the uplift in both the resulting reservoir imaging and the reduction...
2024
Aiding self-supervised coherent noise suppression by the introduction of signal segmentation using blind-spot networks
Sixiu Liu, Claire Birnie, Tariq Alkhalifah, Andrey Bakulin
International Meeting for Applied Geoscience and Energy (IMAGE)
... al., 2019; Wang and Chen, 2019; Birnie et al., 2021a). A number of NN-based denoising procedures utilise Convolutional Neural Networks (CNNs) to learn...
2022
Joint 3D inversion of gravity and magnetic data using deep learning neural networks
Nanyu Wei, Dikun Yang, Zhigang Wang, Yao Lu
International Meeting for Applied Geoscience and Energy (IMAGE)
... uses a supervised deep neural network, developed based on fully convolutional networks and further combined with a U-Net architecture. Two multi-model...
2022
Lithofacies identification in cores using deep learning segmentation and the role of geoscientists: Turbidite deposits (Gulf of Mexico and North Sea)
Oriol Falivene, Neal C. Auchter, Rafael Pires de Lima, Luuk Kleipool, John G. Solum, Pedram Zarian, Rachel W. Clark, and Irene Espejo
AAPG Bulletin
... of convolutional neural networks (CNNs) using semantic segmentation architectures to automate the identification of common lithofacies from core images. Images...
2022
Improving Resolution and Clarity with Neural Networks; #41911 (2016)
Christopher P. Ross
Search and Discovery.com
... and anisotropic model parameters simultaneously with wave-equation modeling. Well logs may be used as part of the low-frequency initial model building...
2016
Deep learning-based Vz-noise attenuation for OBS data
Jing Sun, Arash Jafargandomi, Julian Holden
International Meeting for Applied Geoscience and Energy (IMAGE)
... component is its coherency in the common-receiver domain and incoherency in the commonshot domain. There have been a range of noise attenuation...
2023
Machine learning and seismic attributes for prospect identification and risking: An example from offshore Australia
Mohammed Farfour, Douglas Foster
International Meeting for Applied Geoscience and Energy (IMAGE)
... and convert them to Gas chimney probability cube, and to Gamma Ray cube. Next, pre-trained Convolutional Neural Network (CNN) is trained using...
2022
High-Resolution DFN Modeling via Seismic Attribute Integration in the Sichuan Basin for Completion Optimization
Xuefeng Yang, Shengxian Zhao, Dongchen Liu, Deliang Zhang, Lieyan Cao, Joseph Leines Artieda, Chuxi Liu, Wei Yu, Jijun Miao
Unconventional Resources Technology Conference (URTEC)
... discrete fracture network (DFN) model. The DFN captures both small- and large-scale geological discontinuities, providing critical insights for optimizing...
2025
Implementation of Denoising Diffusion Probability Model for Seismic Interpretation
Fan Jiang, Konstantin Osypov, Julianna Toms
International Meeting for Applied Geoscience and Energy (IMAGE)
...: Aleatoric uncertainty; 5: Epistemic uncertainty. As generative model, diffusion process gains popularity and recognition in the image generation domain...
2023
Assessment of Micro-Fracture Density using Combined Interpretation of NMR Relaxometry and Electromagnetic Logs
Lu Chi, Marcus, Elliot, Zoya Heidari, Mark Everett
Unconventional Resources Technology Conference (URTEC)
... NMR analytical model for fracture-pore coupling to account for micro-fractures in the rock. This model was verified through NMR numerical simulations...
2014
Deep Learning Models for Methane Emissions Identification and Quantification
Ismot Jahan, Mohamed Mehana, Bulbul Ahmmed, Javier E. Santos, Dan O’Malley, Hari Viswanathan
Unconventional Resources Technology Conference (URTEC)
... to prepare the data for the machine learning model. In this section, we will outline the preprocessing and Convolutional Neural Network (CNN) model...
2023