A novel deep learning-assisted reservoir fracture delineation with conditional generative adversarial networks

Ardiansyah Koeshidayatullah, Ivan Ferreira

International Meeting for Applied Geoscience and Energy (IMAGE)

...) model being chosen to test the abilities of image domain-translation in geoscientific tasks. This architecture uses a U-Net Ronneberger et al. (2015...

Abstract: Color Correction for Gabor Deconvolution and Nonstationary Phase Rotation; #90171 (2013)

Peng Cheng and Gary F. Margrave

Search and Discovery.com

... deconvolution is based on a nonstationary convolution model of the seismic trace. Margrave (1998) presented a nonstationary convolutional model, which...

Abstract: Color Correction for Gabor Deconvolution and Nonstationary Phase Rotation; #90171 (2013)

Peng Cheng and Gary F. Margrave

Search and Discovery.com

... deconvolution is based on a nonstationary convolution model of the seismic trace. Margrave (1998) presented a nonstationary convolutional model, which...

An immersed absorbing boundary condition for scalar wavefield modeling under topography

KeJi Chen, Hanming Chen, Hui Zhou

International Meeting for Applied Geoscience and Energy (IMAGE)

... interface. The Ricker wavelet with the main frequency of 20 Hz is used as the source to be excited under the surface. The sinusoidal surface model is used...

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...

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...

Deep carbonate reservoir characterization with unsupervised machine-learning approaches

Xuanying Zhu, Luanxiao Zhao, Xiangyuan Zhao, Yuchun You, Minghui Xu, Tengfei Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... (PCA), T-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Convolutional Autoencoder (CAE...

Integration of deep neural networks into seismic workflows for low-carbon energy

Biondo Biondi, Joseph Jennings, Min Jun Park, Stuart Farris, Bob Clapp

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the frequency domain: Geophysics, 68, 634–640, doi: https://doi.org/10.1190/1.1567233. Second International Meeting for Applied Geoscience & Energy...

Time-lapse full-waveform inversion by model order reduction using radial basis function

Haipeng Li, Robert G. Clapp

International Meeting for Applied Geoscience and Energy (IMAGE)

...Time-lapse full-waveform inversion by model order reduction using radial basis function Haipeng Li, Robert G. Clapp Time-lapse full-waveform...

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...

3D GPR data mel-frequency cepstral coefficients features for effective CNN classification of urban utilities

Jide Nosakare Ogunbo, Sang Hun Baek, Sang-Wook Kim

International Meeting for Applied Geoscience and Energy (IMAGE)

...3D GPR data mel-frequency cepstral coefficients features for effective CNN classification of urban utilities Jide Nosakare Ogunbo, Sang Hun Baek...

Demultiple of High Resolution P-Cable Data in the Norwegian Barents Sea „ An Iterative Approach

A.J. Hardwick, S. Jansen, B. Kjolhamar

Petroleum Exploration Society of Australia (PESA)

... from the latest The final adapted model is then subtracted in the curvelet n. A data driven, iterative approach is domain. For the first time, through...

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...

ABSTRACT: Maximum Likelihood Deconvolution: a New Perspective, by Jerry M. Mendel; #91035 (2010)

Search and Discovery.com

Abstract: Fast and Accurate Impedance Inversion by Well-Log Calibration; #90171 (2013)

Igor B. Morozov and Jinfeng Ma

Search and Discovery.com

...) the convolutional equation; 2) time-depth constraints from the seismic data, 3) background low-frequency model from the logs or seismic/geological interpretation...

A prior regularized 3D full-waveform inversion using 2D generative diffusion models

Fu Wang, Tariq Alkhalifah, Xinquan Huang

International Meeting for Applied Geoscience and Energy (IMAGE)

... of low-frequency data allows for coarse grid simulation, which is much cheaper. Therefore, we resize the model size from 201[Inline] ◊ 201[Crossline...

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...

The Hybrid Theory-Guided Data Science-Based Method: Unlocking the Full Potential of Seismic Reservoirs Characterization

Rino Saputra, Akash Mathur, Awal Mandong

Indonesian Petroleum Association

... Base) that are later needed for the low-frequency model generation. The well WTR-4A has more complete data and was used as reference well...

A simultaneous denoising and event picking approach using supervised machine learning

Salman Abbasi, Motaz Alfarraj, Dmitry Borisov, Vikram Jayaram, Iftekhar Alam, Bakhtawer Sarosh

International Meeting for Applied Geoscience and Energy (IMAGE)

... problems (i.e., denoising and event detection) using a single network. A convolutional neural network is used to capture the high frequency times series...

Dual constrained reservoir modeling with geological factors and seismic attributes for exploration stage

Hongmei Luo, Yiran Xing, Changjiang Wang, Zhijing Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... model as the covariate to realize the geostatistical modeling in the exploration stage. Consequently, the reliability and effectiveness of the modeling...

Deep nonlinear seismic prior for seismic interpolation

Yuhan Sui, Xiaojing Wang, Jianwei Ma

International Meeting for Applied Geoscience and Energy (IMAGE)

... to be a local linear event in the frequency domain. In the transform-based methods (Sacchi et al., 1998; Yu et al., 2015), seismic data is represented...

Estimating soil strength using ultra high-resolution seismic and geological unit

Donglin Zhu, Ge Jin, Yi Shen, Xuefeng Shang, Shuang Hu, Jinbo Chen, Vanessa Goh

International Meeting for Applied Geoscience and Energy (IMAGE)

... soil assessment for foundation design, due to their high foundation costs and complex integration into marine environments. We propose a convolutional...

Efficient and accurate velocity building from Gramian-constrained multiphysics reflection and transmission data

Jide Nosakare Ogunbo

International Meeting for Applied Geoscience and Energy (IMAGE)

..., the use of the convolutional model (Buland and Omre, 2003), by the z-transform, is readily more practical than the seismic operator for either...

Seismic image-to-image translation using a conditional GAN with Bayesian inference

Xiaolei Song, Muhong Zhou, Petr Jilek, Rodney Johnston, Sean Cardinez, Kareem Vincent

International Meeting for Applied Geoscience and Energy (IMAGE)

... uncertainties. We take a similar approach by adopting two convolutional Bayesian layers as the network output layers to capture the model...

Micro transient EM for seismic sand corrections through physics-coupled deep learning

Daniele Colombo, Ersan Turkoglu, Ernesto Sandoval-Curiel, Javier Giraldo-Buitrago

International Meeting for Applied Geoscience and Energy (IMAGE)

.... data-driven approaches (e.g., tomography), at exploration seismic acquisition specifications, are inadequate to reliably model the extremely low sand...