Research and application of Intelligent high resolution processing method based on ISTA-Net

Huahui Zeng, Qin Su, Sanyi Yuan, Lide Wang, Yanwu Xu, Huijie Meng, Deying Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... of these methods in mathematical form and numerical test. Yuan et al. (2021) established a connection between traditional model-driven high-resolution processing...

Residual Saturation During Multiphase Displacement in Heterogeneous Fractures with Novel Deep Learning Prediction

Eric Guiltinan, Javier E. Santos, Qinjun Kang

Unconventional Resources Technology Conference (URTEC)

... until a steady state is reached. To create a predictive model that is able to learn from the LBM simulation outputs, we used a convolutional neural...

Abstract: Azimuthal Simultaneous Elastic Inversion; #90172 (2014)

Jon Downton, Benjamin Roure

Search and Discovery.com

... would like to generalize the model to the case of a stack of anisotropic layers. Secondly, as Goodway et al. (2006) argue, the near offset...

Seismic Data Preconditioning for Improved Reservoir Characterization (Inversion and Fracture Analysis); #41347 (2014)

Darren Schmidt, Alicia Veronesi, Franck Delbecq, and Jeff Durand

Search and Discovery.com

...) or convolutional Zoeppritz-type synthetics (in which only primaries are modeled). Comparing the real seismic to the well synthetic response is the best...

Remote Well Site Biostratigraphy and Advances in Automated Fossil Analysis; #41930 (2016)

Gunilla Gard, Iain Prince, Jason A. Crux, J. M. Shin, Bernard Lee

Search and Discovery.com

... Convolutional Neural Network (CNN) is applied to training fossil recognition. Accuracy 4. In each iteration, the training software checks the training quality...

Using Second-Order Adjoint State Methods in GPUS to Quantify Resolution on Full Waveform Inversions, #42034 (2017).

Sergio Abreo, Ana Ramirez, Oscar Mauricio Reyes Torres

Search and Discovery.com

... Inversion (FWI) allows quantifying resolution of the velocity model obtained. Although there are different ways to compute approximations of the Hessian...

A flexible and versatile joint inversion framework using deep learning

Yanyan Hu, Jiefu Chen, Xuqing Wu, Yueqin Huang

International Meeting for Applied Geoscience and Energy (IMAGE)

... and prospects. Unlike conventional end-to-end networks that map directly from the data domain to the model domain, this DLE framework is designed...

Self-supervised learning for seismic swell noise removal

Yuan Zi, Shirui Wang, Pengyu Yuan, Xuqing Wu, Jiefu Chen, Zhu Han

International Meeting for Applied Geoscience and Energy (IMAGE)

... and achieved impressive results. Farmani and Pedersen (2020) utilized the convolutional neural network for swelling noise attenuation. Zhang et al...

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...

Tops fingerprinting: Revolutionizing well log analysis with music recognition technology

Alan Lindsey, Morgan Cox, Aaron Hugen

International Meeting for Applied Geoscience and Energy (IMAGE)

..., false positives are a problem. Convolutional Neural Networks are quite powerful, but require substantial training, and they tend to be a black-box...

Advanced digital tools for passive seismic monitoring of a heavy oil field at Cold Lake, Alberta, Canada

Adarsh Kumar Gupta, Stefano Scaini, Ravi Singh, Simona Costin, Colin Brisco, Raj Janakkumar Bhutwala, Shelly Dutta, Monomita Chattopadhyay, Divyanshu Yadav, Taylor Fink

International Meeting for Applied Geoscience and Energy (IMAGE)

... Analysis using Deep learning (SA-DEEP) is a convolutional neural network (CNN) classifier that uses Fourth International Meeting for Applied Geoscience...

Oriented ellipsoidal DBSCAN for clustering faults from deep learning attributes

Samuel Chambers, Jesse Lomask

International Meeting for Applied Geoscience and Energy (IMAGE)

..., and A. Z. Yusifov, 2019, FaultNet3D: Predicting fault probabilities, strikes, and dips with a single convolutional neural network: IEEE Transactions...

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)

... non-parametric regression resulting in a unified, easily generalizable model that performs robustly when tested against previously unseen images. Our...

Integrating Deep Learning and Seismic Data for Mudstone Characterization and SAGD Development in Heterogeneous Reservoirs

Huiwen Pang, Hanqing Wang, Chuan Qin, Jingwei Tian

Unconventional Resources Technology Conference (URTEC)

... calibration to establish spatiotemporally aligned training data; (2) Deep Learning Model Development, employing convolutional neural networks...

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...

Improved Resolution of Thin Turbiditic Sands in Offshore Sabah with Bandwidth Extension … A Pilot Study (Paper C11)

G. Yu, N. Shah, M. Robinson, N. H. Nghi, A. A. Nurhono, G. S. Thu

Geological Society of Malaysia (GSM)

.... Methodology Bandwidth Extension algorithm utilizes the Continuous Wavelet Transform (CWT) to model harmonics and subharmonics to recover both high and low...

Abstract: Can Q Explain Observations Made from a VSP?, by Hamish Wilson, Scott W. Peters, and Robert W. Wiley; #90105 (2010)

Search and Discovery.com

Filter-Bank Strategies for Efficient Computation of Radon Transforms for SNR Enhancement

Mauricio D. Sacchi

Search and Discovery.com

...: by improving focusing with multi-path Radon operators (Trad et al., 2001) or via local Radon operators that attempt to model the waveforms in a small...

Filter-Bank Strategies for Efficient Computation of Radon Transforms for SNR Enhancement

Mauricio D. Sacchi

Search and Discovery.com

...: by improving focusing with multi-path Radon operators (Trad et al., 2001) or via local Radon operators that attempt to model the waveforms in a small...

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

... (t )  ...   n * n (t )  ... n 1 (1) where all  n are small time-independent convolutional (*) filters. Equation (1) provides a base for four unique methods...

Abstract: Integrating Geologic and Geophysical Data in Geostatistical Inversion; #90187 (2014)

John V. Pendrel

Search and Discovery.com

... constraints are applied simultaneously The seismic and reservoir properties are related through a predictive rock physics model The facies definitions...

Abstracts: Application of Neural Network Analysis and Post-Stack Inversion - Case Studies in Alberta; #90173 (2015)

Somanath Misra and Satinder Chopra

Search and Discovery.com

... the P-impedance from the post-stack data by way of model based inversion as well as neural network analysis. We are showing comparisons of the results...

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...

Use of Machine Learning to Estimate Sonic Data for Seismic Well Ties; #42471 (2019)

Thanapong Ketmalee

Search and Discovery.com

... Computed Convolutional Model Filter DT Casing Bad hole condition Spike RC * Wavelet Synthetic Seismogram AI Comparison Scenarios Actual DT ML...

Abstract: Machine Learning and Deep Learning in Oil and Gas Industry: A Review Ofapplications, Opportunities and Challenges; #91204 (2023)

Tejas Balasaheb Sabale, Syed Aaquib Hussain, Mohd Zuhair, Mohammad Saud Afzal, Arnab Ghosh

Search and Discovery.com

... algorithms to predict the outcome correctly [17]. The model is initialized with control parameters and then the input data is fed into the model...