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...

Drilling and Completion Anomaly Detection in Daily Reports by Deep Learning and Natural Language Processing Techniques

Hongbao Zhang, Yijin Zeng, Hongzhi Bao, Lulu Liao, Jian Song, Zaifu Huang, Xinjin Chen, Zhifa Wang, Yang Xu, Xin Jin

Unconventional Resources Technology Conference (URTEC)

...”, “grapple” and “bumper”, which are all fishing related tools, that means the model has learned the semantics of words. Convolutional neural network (CNN...

Machine-Learning-Assisted Segmentation of FIB-SEM Images with Artifacts for Improved of Pore Space Characterization of Tight Reservoir Rocks

Andrey Kazak, Kirill Simonov, Victor Kulikov

Unconventional Resources Technology Conference (URTEC)

... on a convolutional neural network (CNN) in the DeepUnet configuration. The implementation utilized the Pytorch framework in a Linux environment...

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...

Physics-based preconditioned multidimensional deconvolution in the time domain

David Vargas, Ivan Vasconcelos, Matteo Ravasi, Nick Luiken

International Meeting for Applied Geoscience and Energy (IMAGE)

... the convolutional kernel in (4) cannot be decoupled on a frequency-by-frequency basis. In the time-domain, the operator P+ is too large to be explicitly...

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...

Optimized transparent boundary conditions for wave propagation

G. Roncoroni, B. Arntsen, E. Forte, M. Pipan

International Meeting for Applied Geoscience and Energy (IMAGE)

... within a 1D velocity model, we ensure the absence of boundary reflections within the region of interest of the extended domain (see Figure 2), thereby...

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...

Fluid distribution modeling impact on estimating CO2 saturation in Cranfield: A capillary pressure equilibrium approach with invertible neural networks

Sohini Dasgupta, Arnab Dhara, Mrinal K. Sen

International Meeting for Applied Geoscience and Energy (IMAGE)

... inversion strategy which uses a capillary pressure based rock physics model with invertible neural networks (INNs) to estimate CO2 saturation...

Joint inversion of magnetotelluric and seismic travel time data with intelligent interpretation of geophysical models

Hongyu Zhou, Rui Guo, Maokun Li, Fan Yang, Shengheng Xu, Zuzhi Hu, Deqiang Tao

International Meeting for Applied Geoscience and Energy (IMAGE)

... equations ∂ 2 Ex ∂ 2 Ex + + (ω 2 µε + iω µσ )Ex = 0 ∂ y2 ∂ z2 (1) ∂ Ex = iω µHy (2) ∂z from the Maxwell’s equation in the frequency domain with time...

A self-attention enhanced encoder-decoder network for seismic data denoising

Stefan Knispel, Jan Walda, Ruediger Zehn, Alexander Bauer, Dirk Gajewski

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutions (Bello et al., 2019), where attentional feature maps are generated and concatenated to the convolutional feature maps. This does not replace...

Understanding the Seismic Wavelet; Steven G. Henry; Search and Discovery Article #40028 (2001)

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Abstract: Deterministic Marine Deghosting: Tutorial and Recent Advances; #90224 (2015)

Mike J. Perz and Hassan Masoomzadeh

Search and Discovery.com

... is minimum phase. We can easily express this operator in the frequency domain as (2) 𝐺(𝑤) = 1 − 𝑟𝑒 𝑖𝑤𝑡 𝑑 , and its minimum phase inverse as: 1...

Tops fingerprinting: Revolutionizing well log analysis with music recognition technology

Alan Lindsey, Morgan Cox, Aaron Hugen

International Meeting for Applied Geoscience and Energy (IMAGE)

... matching techniques, like those employed by apps such as Shazam, begin by converting audio into a spectrogram, which shows the frequency of sound over time...

Multiple, Diffractions and Diffracted Multiples in the South China Sea: How Dense Does Our Acquisition Geometry Need to be? (Geophysics Paper 16)

Rosemary K Quinn, Lynn B Comeaux

Geological Society of Malaysia (GSM)

... be horizontal over the scale of the SRME aperture in order for the model to be predicted accurately. Clearly, this is rarely the case, but as long...

First arrival enhancement by statics preserving filtering using surface-consistent constraints

Alejandro Quiaro, Mauricio D. Sacchi

International Meeting for Applied Geoscience and Energy (IMAGE)

... model, we are considering the effect of T, as applying a time shift 't"sr = -r(s)+-r(r) +noise on the otherwise predictable signal. In the frequency...

Seismic Facies Segmentation Using Deep Learning

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Application of Machine Learning and Deep Learning for Complex Fault Network Characterizationon the North Slope, Alaska

Search and Discovery.com

Time-lapse attenuation variations during CO2 injection using DAS VSP data from the CaMI Field Research Station, Alberta, Canada

Yichuan Wang, Donald C. Lawton

International Meeting for Applied Geoscience and Energy (IMAGE)

... measurement from the DAS VSP data can help to detect injected CO2 at the FRS. Attenuation model and measurement In the frequency domain, the non-stationary...

Towards Universal Production Forecasting via Adversarial Transfer Learning and Transformer with Application in the Shengli Oilfield, China

Ji Chang, Jin Meng, Dongwei Zhang, Tianrui Ye, Han Wang, Yitian Xiao

Unconventional Resources Technology Conference (URTEC)

... compare it with three nontransfer training strategies: source only, which refers to testing the target domain ( ) with the model trained only...

The Role of Forward Seismic Modeling: Outcrop Analogs of Deep-Water Architectures; #51679 (2020)

Jamie K. Pringle, David A. Stanbrook

Search and Discovery.com

... is then imported into specialist software that convolves the model, using user-specified seismic acoustic impedance contrasts and central frequency...

Interpretation of deep neural networks for carbonate thin section classification

Lukas Mosser, George Ghon, Gregor Baechle

International Meeting for Applied Geoscience and Energy (IMAGE)

... SUMMARY This study uses ImageNet pretrained convolutional neural networks (CNNs), VGG11 and ResNet18 models to predict carbonate rock and pore types...

Convolutional Neural Networks Forecasting for Unconventional Drilling Units in US Land

Francisco J. Parga Garcia, Jie Fang, Niven Shumaker

Unconventional Resources Technology Conference (URTEC)

...Convolutional Neural Networks Forecasting for Unconventional Drilling Units in US Land Francisco J. Parga Garcia, Jie Fang, Niven Shumaker URTeC...

An Overview of Reservoir Seismic Stratigraphy, Frontmatter

Tom Wittick

North Texas Geological Society

... Acoustic impedance Reflection coefficients Wavelets The convolutional model III. Preparation of seismic data for stratigraphic work Data...

Abstract: New Approach to Finite-Difference Memory Variables by Using Lagrangian Mechanics; #90187 (2014)

Wubing Deng and Igor Morozov

Search and Discovery.com

...  J (Pa.s) k Figure 1. Dissipation factor as a function of frequency for a GSLS model with five Maxwell bodies. k J (MPa) 1000 15 15 15 15 15...