A rock physics inversion method based on physics-guided autoencoder network

Zhuofan Liu, Umair bin Waheed, Ammar El-Husseini, Jiajia Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

...- guided convolutional neural network: Interpretation, 7, no. 3, SE161–SE174, doi: https://doi.org/10.1190/INT-2018-0236.1. Bosch, M., T. Mukerji, and E. F...

Abstract: Interactive Deep Learning Assisted Seismic Interpretation Technology Applied to Reservoir Characterization: A Case Study From Offshore Santos Basin in Brazil;

Ana Krueger, Bode Omoboya, Paul Endresen, Benjamin Lartigue

Search and Discovery.com

... Convolutional Neural Networks (CNN), the deep neural network acts as an extension of the interpreter to assist in mapping sub-surface geological...

Abstract: Neural Networks Facilitate Precise at - Bit Formation Detection Suitable for Deployment in Automated Drilling Systems; #91204 (2023)

Lucas Katzmann, Stefan Wessling, Matthew Forshaw, Joern Koeneke

Search and Discovery.com

... an alternative, data-driven solution using a multi-layer supervised machine learning model to identify such formation changes. Methods Analysis...

Chapter 2: Basics of Reflection Seismology that Relate to Seismic Stratigraphy

Tom Wittick

North Texas Geological Society

... for those prospective signatures. The Convolutional Model Figure 2-4 is a cartoon showing the relationship between a lithologic column...

Basics of Reflection Seismic Technology

Abilene Geological Society

... for those prospective signatures. The Convolutional Model Figure 2-4 is a cartoon showing the relationship between a lithologic column...

Shaking up the Earth: The AI revolution in seismic interpretation

Ryan Williams

GEO ExPro Magazine

... for seismic interpretation is much the same despite the complex challenges. Geoteric AI seismic interpretation powered by multiple 3D convolutional neural...

Vision-based Sedimentary Structure Identification of Core Images using Transfer Learning and Convolutional Neural Network Approach

Baosen Zhang, Shiwang Chen, Yitian Xiao, Laiming Zhang, Chengshan Wang

Unconventional Resources Technology Conference (URTEC)

...Vision-based Sedimentary Structure Identification of Core Images using Transfer Learning and Convolutional Neural Network Approach Baosen Zhang...

Rock Thin-section Analysis and Mineral Detection Utilizing Deep Learning Approach

Fatick Nath, Sarker Asish, Shaon Sutradhar, Zhiyang Li, Nazmul Shahadat, Happy R. Debi, S M Shamsul Hoque

Unconventional Resources Technology Conference (URTEC)

... of rock thin sections. In a similar objective, Nanjo et al. (2019) implemented convolutional neural network-based model to classify four types of rock...

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...

Implementation of Seismic Data Quality Characterisation Using Supervised Deep Learning

Joshua Thorp, Krista Davies, Julien Bluteau, Peter Hoiles

Australian Petroleum Production & Exploration Association (APPEA) Journal

... convolutional autoencoders. Geophysics 83, A39–A43. doi:10.1190/geo2017-0524.1 Tishchenko, I. (2016). Different methods of QC the low frequency content...

Geostatistical Integration of Crosswell Data for Carbonate Reservoir Modeling, Mcelroy Field, Texas

William M. Bashore, Robert T. Langan, Karla E. Tucker, Paul J. Griffith

Special Publications of SEPM

... structures in order to be useful for the inversion process. The inversion is performed in the frequency domain, which requires the low-frequency model...

Velocity continuation with Fourier neural operators for accelerated uncertainty quantification

Ali Siahkoohi, Mathias Louboutin, Felix J. Herrmann

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the background squared-slowness model. Uncertainty quantification is essential for determining how variability in the background models affects seismic...

Abstract: A Real Swell Noise Benchmark Dataset for Seismic Data Denoising with Deep Learning; #91215 (2026)

R. D. Sardinha, G. A. Meneses Arboleda, L. Sadala Valente, I. Melo, A. Aveleda, S. Netto, A. Evsukoff, P. M. Barros, A. Bulcão

Search and Discovery.com

... that can capture small variations in model results. The results show that DL models are effective for noise reduction in seismic data, but removing noise...

Fault MLReal: A fault delineation study for the Decatur CO2 field data using neural network predicted passive seismic locations

Hanchen Wang, Yinpeng Chen, Tariq Alkhalifah, Youzuo Lin

International Meeting for Applied Geoscience and Energy (IMAGE)

... and performance of Convolutional Neural Networks (CNN). Considering we have labeled training data, referred to in domain adaptation circles...

A Quantitative Application of Seismic Inversion and Multi-Attribute Analysis based on Rock Physics Linear Relationships to identify High Total Organic Carbon Shale - A Case Study from the Perth Basin, Western Australia

Y. Altowairqi, R. Rezaee, B. Evans, M. Urosevic

Unconventional Resources Technology Conference (URTEC)

...-attribute analysis is applied to predict TOC from a model-based inversion and used the AI as external attribute. A total of eight seismic attributes were...

Research on first break picking based on deep learning for DAS-VSP data

Naijian Wang, Yinpo Xu, Yuxin Hou, Yingjie Pan, Mingxing Wang, Chun Zhang, Tianfu Yang

International Meeting for Applied Geoscience and Energy (IMAGE)

... of whole zone are covered; thirdly, the U-Net network is improved by adjusting the model hierarchy, optimizing the incentive function and adding batch...

Auto-identification and Real-time Warning Method of Multiple Type Events During Multistage Horizontal Well Fracturing

Mingze Zhao, Yue Li, Yuyang Liu, Bin Yuan, Siwei Meng, Wei Zhang, He Liu

Unconventional Resources Technology Conference (URTEC)

... identification and real-time warning method of multiple types of events during multi-stage fracturing. A new intelligent identification model is developed...

Internal multiple elimination with an inverse-scattering theory guided deep neural network

Zhiwei Gu, Liurong Tao, Haoran Ren, Ru-Shan Wu, Jianhua Geng

International Meeting for Applied Geoscience and Energy (IMAGE)

... with the convolutional operation. Combining the CNN with the autoencoder can improve the feature extraction ability of the network model and have higher computational...

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...

High-fidelity GPR image super-resolution via deep-supervised machine learning

Kai Gao, Carly M. Donahue, Bradley G. Henderson, Ryan T. Modrak

International Meeting for Applied Geoscience and Energy (IMAGE)

... migration images. To achieve this task, we adopt an attention-based residual convolutional neural network as the backbone (Bi et al., 2021), which uses...

Pyseis: A high-performance, user-friendly Python package for GPU-accelerated seismic modeling and subsurface imaging

Stuart Farris, Guillaume Barnier, Ettore Biondi, Robert Clapp

International Meeting for Applied Geoscience and Energy (IMAGE)

... NVIDIA V100 GPU. In these tests, we kept the number of time steps constant while varying the size of the model domain, a realistic scenario where high...

Training data versus deep learning architectures in the seismic fault attribute computation

Bo Zhang, Yitao Pu, Zhaohui Xu, Naihao Liu, Shizhen Li, Fangyu Li

International Meeting for Applied Geoscience and Energy (IMAGE)

...” and predicted result of side outputs (o1-o4). The model generates a “fuse” set to combine the side outputs at different scales. It is common that one...

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...

From Chaos to Caves … An Evolution of Seismic Karst Interpretation at the Vorwata Field

Riangguna Eloni, M.R. Husni Sahidu, Ilham Panggeleng, Christopher S. Birt, Ted Manning

Indonesian Petroleum Association

...’ between layers. It is often preferable to transform the reflectivity data into the impedance domain because impedance can be used to approximate...

Automated machine learning first-break picking in the Sichuan Basin „ A case study

Jianfa Wu, Xuewen Shi, Qiyong Gou, Ersi Xu, Dongjun Zhang, Dingxue Wang, Phil Bilsby, Qing Zhou, Rong Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... to the various machine learning model architectures employed and demonstrate the uplift in both the resulting reservoir imaging and the reduction...