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The AAPG/Datapages Combined Publications Database
Showing 621 Results. Searched 200,293 documents.
Increasing signal-to-noise ratio of borehole image logs using convolutional neural networks
Mustafa A. Al Ibrahim, Mokhles M. Mezghani
International Meeting for Applied Geoscience and Energy (IMAGE)
... using a convolutional neural network. Results are evaluated quantitatively and qualitatively. Finally, the model is applied on the image log...
2022
Feasibility Study Methodology for Fracture Analysis Studies Using Seismic Azimuthal Amplitude Variation: Application in Southern Mexico
Alexis Ferrer Balas, Nahum Campos, Jesus Garcia Hernandez
GCAGS Transactions
... the well from depth to time domain. Horizons in the vicinity of the well are also required. Their extent depends on the size we want to model and may...
2011
Seismic simulations of experimental strata
Lincoln Pratson, Wences Gouveia
AAPG Bulletin
.... These reflection coefficients are then converted from the depth to the time domain using the model velocities. Note that the convolutional model is one...
2002
Research on fault-karst reservoir identification method based on deep convolutional network
Zhipeng Gui, Junhua Zhang, Hong Zhang, Dong Chen, Pengbo Yin
International Meeting for Applied Geoscience and Energy (IMAGE)
... model adopted in this paper is shown in Figure 1a. Following the model input, it is connected to an MFEB module, followed by two convolutional layers...
2024
Application of interactive convolutional neural network micro-fracture prediction technology based on prestack depth migration data in deep shale gas reservoirs
Xiaolan Wang, Furong Wu, Junfeng Liu, Dianguang Zang, Xiao Yang, Yangjing Li, Xiaoyan Cheng
International Meeting for Applied Geoscience and Energy (IMAGE)
... Neural Prediction Technology Network (CNN) Fracture Convolutional neural networks are a type of deep learning model specifically designed...
2024
Efficient Bayesian full-waveform inversion using a deep convolutional autoencoder prior
Shuhua Hu, Mrinal K Sen, Zeyu Zhao, Abdelrahman Elmeliegy, Shuo Zhang
International Meeting for Applied Geoscience and Energy (IMAGE)
... that model reparametrization using deep convolutional neural networks (CNN) naturally introduces regularization to FWI. To leverage the benefits of DNN...
2024
Unlocking Lithium Potential from Oilfield Brines: A Deep Learning-Driven Resource Assessment
Rajkanwar Singh, Saaksshi Jilhewar, Audrey Der, Ryan Mercer, Vikram Jayaram
Unconventional Resources Technology Conference (URTEC)
... structured chemical data. • CNN-Bidirectional GRU (BiGRU) Model: Combining convolutional layers with bidirectional Gated Recurrent Units (GRUs) to enhance...
2025
CMP domain near-surface velocity model building based on deep learning
Yihao Wang
International Meeting for Applied Geoscience and Energy (IMAGE)
...CMP domain near-surface velocity model building based on deep learning Yihao Wang CMP domain near-surface velocity model building based on deep...
2022
Retrieving high-resolution acoustic impedance using full-waveform inversion in presalt reservoir setting, offshore Brazil
Akshat Abhishek, Alok K. Soni, Rene-Edouard Plessix, Martijn Blaauw, Jaydip Guha, Gautam Kumar
International Meeting for Applied Geoscience and Energy (IMAGE)
... impedance inversion using our approach and perform a comparative study against the model parameters obtained from a convolutional model-based inversion work...
2022
High-efficient reflection retrieval from massive ambient noise using a deep-learning workflow
Yinghe Wu, Shulin Pan, Dawei Liu, Yaojie Chen, Qinghui Cui
International Meeting for Applied Geoscience and Energy (IMAGE)
... and frequencydomain data into the network. Figure 1: Convolutional autoencoder architecture used in training. The input is the time-domain seismic...
2024
Identification of vehicles from seismic signals using machine learning
Xiaoxuan Zhu, Ji Zhang, Jie Zhang
International Meeting for Applied Geoscience and Energy (IMAGE)
... to record seismic signals generated by passing vehicles. We then conduct analyses in the time domain to roughly categorize traffic vehicles into three...
2023
Automating the thresholding of multi-stage iterative source separation with priors using machine learning
Nam Pham, Rajiv Kumar, Sunil Manikani, Yousif Izzeldin Kamil Amin, Phillip Bilsby, Massimiliano Vassallo, Tao Zhao
International Meeting for Applied Geoscience and Energy (IMAGE)
... to the training data and transform them to the FK domain. By learning from different moveouts, the same trained neural network model can be applied...
2024
Abstract: Short-time Wavelet Estimation in the Homomorphic Domain; #90174 (2014)
Roberto H. Herrera and Mirko van der Baan
Search and Discovery.com
... is also used in the log spectral averaging method. Seismic signals are nonstationary, i.e. they follow the time-invariant convolutional model only...
2014
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
... neural networks. This model is trained on a database of rock models generated using a 3D process-based modeling technique. Convolutional Neural Network...
Unknown
Abstract: AI- Assisted Palynological Analysis Using an Expert-Trained Convolutional Neural Network: A Case Study form the Jurassic in the North Sea; #91204 (2023)
Rader Abdul Fattah, Merijn de Bakker, Alexander Houben, Roel Verreussel, Robert Williams
Search and Discovery.com
...Abstract: AI- Assisted Palynological Analysis Using an Expert-Trained Convolutional Neural Network: A Case Study form the Jurassic in the North Sea...
2023
Elastic-AdjointNet: A physics-guided deep autoencoder to overcome crosstalk effects in multiparameter full-waveform inversion
Arnab Dhara, Mrinal Sen
International Meeting for Applied Geoscience and Energy (IMAGE)
... results in area of low data coverage. Method We use the time domain formulation of elastic wave equation to invert for P-wave velocity (Vp), S-wave...
2022
A recipe for practical iterative LSRTM with synthetic and real data examples from Brazil
Valeriy Brytik, Gopal Palacharla, Rishi Bansal, Diwi Snyder, Xu Li, Young Ho Cha, Partha Routh, Inma Dura-Gomez, Dmitriy Pavlov, Carey Marcinkovich
International Meeting for Applied Geoscience and Energy (IMAGE)
... into the model domain. The mapping between image and data space is usually achieved via computationally expensive wave-equation based methods...
2022
Extending Engine Change Out Respective to Running Hours Using Data Driven Using One Dimension (1-D) Convolutional Neural Network Algorithm
Subhan Malik, Harry Poetra Soedarsono
Indonesian Petroleum Association
...Extending Engine Change Out Respective to Running Hours Using Data Driven Using One Dimension (1-D) Convolutional Neural Network Algorithm Subhan...
2024
High-resolution angle gather tomography with Fourier neural operators
Sean Crawley, Guanghui Huang, Ramzi Djebbi, Jaime Ramos, Nizar Chemingui
International Meeting for Applied Geoscience and Energy (IMAGE)
... data and field data. Additionally, migrated data already occupies the same domain as the target velocity model (plus some kind of angle/extended image...
2023
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
Deep learning in salt interpretation from R&D to deployment: Challenges and lessons learned
Pandu Devarakota, Apurva Gala, Zhenggang Li, Engin Alkan, Yihua Cai, John Kimbro, Dean Knott, Jeff Moore, Gislain Madiba
International Meeting for Applied Geoscience and Energy (IMAGE)
... a critical role in velocity model building in both exploration and development fields. It is a time-consuming effort that requires key domain expertise...
2022
Abstract: Push the Limits of Seismic Resolution Using Surface Consistent Gabor Deconvolution; #90171 (2013)
Xinxiang Li and Darren P. Schmidt
Search and Discovery.com
... and the time-variant earth wavelet in a nonstationary convolutional trace model, which can be approximately factorized in the Gabor domain...
2013
Horizontal Stresses Prediction Using Sonic Transition Time Based on Convolutional Neural Network; #42587 (2023)
Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo
Search and Discovery.com
...Horizontal Stresses Prediction Using Sonic Transition Time Based on Convolutional Neural Network; #42587 (2023) Esmael Makarian, Ayub Elyasi, Fatemeh...
2023
Geobody-oriented interpretable velocity fusion modeling in depth domain with seismic facies informed segmentation method
Meng Li, Qingcai Zeng, Hao Shou, Nan Qin, Chunming Wang, Tongsheng Zeng
International Meeting for Applied Geoscience and Energy (IMAGE)
... modeling and combines it with seismic inversion to improve the resolution of velocity model in depth domain. This work is based on the assumption...
2023
Multi-realization seismic data processing with deep variational preconditioners
Matteo Ravasi
International Meeting for Applied Geoscience and Energy (IMAGE)
... is represented here by the latent space parameters of a fully-convolutional VAE, pre-trained directly on the available data sorted in a suitable domain...
2023