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The AAPG/Datapages Combined Publications Database
Showing 622 Results. Searched 200,357 documents.
A Physics-Guided Deep Learning Predictive Model for Robust Production Forecasting and Diagnostics in Unconventional Wells
Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Unconventional Resources Technology Conference (URTEC)
... data set and do not offer the opportunity to incorporate domain constraints. In data-driven modeling, a trained model extracts salient features from...
2021
Boosting self-supervised blind-spot networks via transfer learning
Claire Birnie, Tariq Alkhalifah
International Meeting for Applied Geoscience and Energy (IMAGE)
... networks that learn a pixel’s value based on neighbouring pixels, we propose to train a supervised model in a blind-spot manner such that the model learns...
2022
Machine-learning Facilitates Prediction of Geomechanical Properties Directly From SEM Images in Unconventional Plays
Heehwan Yang, Deepak Devegowda, Mark Curtis, Chandra Rai
Unconventional Resources Technology Conference (URTEC)
... of the multi-mineral domain for mechanical properties. While this has shown promising results in the past, there is a high degree of subjectivity...
2023
Abstract: Deep Learning Inversion on Seismic Cubes; #91204 (2023)
Aleksandr Koriagin, Alexey Kozhevin, Stepan Goriachev, Roman Khudorozhkov
Search and Discovery.com
... show how one can perform inference on full seismic cubes using convolutional neural networks and specific prediction aggregation techniques...
2023
Detect Oil Spill in Offshore Facility Using Convolutional Neural Network and Transfer Learning
Dharmawan Raharjo, Muhamad Solehudin
Indonesian Petroleum Association
...Detect Oil Spill in Offshore Facility Using Convolutional Neural Network and Transfer Learning Dharmawan Raharjo, Muhamad Solehudin This paper has...
2021
Uncertainty quantification of single and multi-parameter full-waveform inversion through a variational autoencoder
Abdelrahman Elmeliegy, Mrinal Sen, Jennifer Harding, Hongkyu Yoon
International Meeting for Applied Geoscience and Energy (IMAGE)
.... The input to the network is seismic shot gathers and the output are samples (distribution) of model parameters. We then use these samples to estimate...
2024
CO2 Plume Imaging with Accelerated Deep Learning-based Data Assimilation Using Distributed Pressure and Temperature Measurements at the Illinois Basin-Decatur Carbon Sequestration Project
Takuto Sakai, Masahiro Nagao, Chin Hsiang Chan, Akhil Datta-Gupta
Carbon Capture, Utilization and Storage (CCUS)
... by proposing an accelerated deep learning-based workflow for model calibration and prediction of CO2 plume evolution in the reservoir. In the proposed...
2024
Estimating CO2 saturation maps from seismic data using deep convolutional neural networks
Zi Xian Leong, Tieyuan Zhu, Alexander Y. Sun
International Meeting for Applied Geoscience and Energy (IMAGE)
... deep convolutional neural networks interpolated velocity and density conform with the seismic structure. We select a 2D slice (Fig. 1) from the 3D model...
2022
Comparison of Machine Learning and Statistical Predictive Models for Production Time Series Forecasting in Tight Oil Reservoirs
Hamid Rahmanifard, Ian Gates, Abdolmohsen Shabib-Asl
Unconventional Resources Technology Conference (URTEC)
... by a pooling layer and a fully connected layer. A 1D CNN with a convolutional layer to extract features from the input sequences, followed by an LSTM model...
2022
Precursory Detection of Casing Deformation and Induced Seismicity in Unconventional Reservoirs, via Real-Time Surface Pressure Data Analytics
Thomas de Boer, Matthew Adams, Andrew McMurray, Giovanni Grasselli
Unconventional Resources Technology Conference (URTEC)
... frequency-domain techniques. This paper introduces a newly developed signal decomposition and machine learning framework capable of transforming raw...
2025
The use of FWI in coal exploration
Mehdi Asgharzadeh, Maryam Bahri, Milovan Urosevic
Petroleum Exploration Society of Australia (PESA)
... wave with dominant frequency of 80 Hz and propagation velocity of 3000 m/s near the boundaries of the model. To simulate shot records, we selected FD...
2018
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
Automated metallic pipeline detection using magnetic data and convolutional neural networks
Brett Bernstein, Yaoguo Li, Richard Hammack
International Meeting for Applied Geoscience and Energy (IMAGE)
...Automated metallic pipeline detection using magnetic data and convolutional neural networks Brett Bernstein, Yaoguo Li, Richard Hammack Automated...
2022
Improving Depth Prediction Accuracy of Quantified Drilling Hazards
W. Scott Leaney, William H. Borland
Geological Society of Malaysia (GSM)
... problem without a forward problem, and the forward problem underlying seismic trace inversion is the convolutional model. A processed seismic trace...
1996
An Introduction to Deep Learning: Part III
Lasse Amundsen, Hongbo Zhou, Martin Landrø
GEO ExPro Magazine
... computer model that learns to perform classification tasks directly from images. The one that started it all was the 2012 publication ‘ImageNet...
2018
Estimating subsurface geostatistical parameters from surface-based GPR reflection data using a deep-learning approach
Yu Liu, James Irving, Klaus Holliger
International Meeting for Applied Geoscience and Energy (IMAGE)
... task in a highly efficient manner. The proposed approach uses a convolutional neural network (CNN), which is trained on a vast database...
2023
Embedding Physical Flow Functions into Deep Learning Predictive Models for Improved Production Forecasting
Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Unconventional Resources Technology Conference (URTEC)
...trained model is composed of several fully-connected regression layers and one- URTeC 3702606 6 dimensional (1D) convolutional layers. A fully-co...
2022
Sub-seafloor reflectivity estimation by upgoing wavefield deconvolution
Hassan Masoomzadeh, Tim Seher, M. A. H. Zuberi
International Meeting for Applied Geoscience and Energy (IMAGE)
... in shallow water settings. The first practical obstacle is that node data sampling in the space domain is usually quite sparse. The second concern...
2023
Impact of an Integrated Seismic Data Processing Approach: A Case Study in Central Sumatra
Mars E. Semaan, Eddy Murhantoro, Maryanto, Achmad Bermawi, Budi Subianto, Hari Santoso
Indonesian Petroleum Association
... correction DMO / Pre-stack migration in the common offset domain NMO / Stack / Post-stack enhancement Frequency filtering / Scaling / Display...
1992
Delineation of Gas Sands by Seismic Stratigraphy in the Pericocal Area, Orinoco Heavy Oil Belt, Venezuela: Section IV. Exploration Methods
J. Licheri, N. Parra
AAPG Special Volumes
... are interpreted in terms of geology and/or presence of fluids, according to the convolutional model. -- The parameters that allow changes in the seismic...
1987
Deep learning to predict subsurface properties from injected CO2 plume bodies using time-lapse seismic shot gathers
Son Phan, Wenyi Hu, Aria Abubakar
International Meeting for Applied Geoscience and Energy (IMAGE)
... between the depth domain property contrast and the time domain seismic response to CO2 injection and plume body migration using a fully processed baseline...
2022
3D Seismic Facies Classification on CPU and GPU HPC Clusters
Sergio Botelho, Vishal Das, Davide Vanzo, Pandu Devarakota, Vinay Rao, Santi Adavani
Unconventional Resources Technology Conference (URTEC)
...; second, neural network design becomes increasingly challenging due to the higher number of parameters in the model and its larger training time. We...
2021
CGG 3D Surface-Related Multiple Modelling: A Unique Approach, #41590 (2015).
David Le Meur, Antonio Pica, Terje Weisser
Search and Discovery.com
... and shot lines for the required convolutional process. Model-based modeling techniques may require interpolation between streamers, but not between...
2015
Seismic random noise attenuation via enhanced similarity self-supervised learning
Jiale Wang, Naihao Liu, Yihuai Lou, Jinghuai Gao
International Meeting for Applied Geoscience and Energy (IMAGE)
... in the frequency domain, which seriously affects the subsequent seismic data processing and geological interpretation (Dong et al., 2020). Therefore, many...
2022
Massive focal mechanism solutions from deep learning in west Texas
Yangkang Chen, Omar M. Saad, Alexandros Savvaidis, Fangxue Zhang, Yunfeng Chen, Dino Huang, Huijian Li, Farzaneh Aziz Zanjani
International Meeting for Applied Geoscience and Energy (IMAGE)
... and simulation of high-frequency waveforms. In this work, we focus on the first-motion-based methods. Picking the P-wave first-motion polarities can...
2024