Unlocking hidden potential in shallow water Gulf of Mexico legacy data for carbon capture and storage exploration

Rachel Collings, Igor Marino, Adriana Arroyo Acosta, Jack Kinkead, Hugo Medel, Trong Tang, Gabriela Suarez, Brett Sellers

International Meeting for Applied Geoscience and Energy (IMAGE)

... deployed a comprehensive wavelet processing workflow. To obtain a high-resolution velocity model, a seismic inversion workflow was implemented...

Facies-induced bias in machine learning-enhanced seismic lithology (inversion)

Hongliu Zeng, Bo Zhang, Mariana Olariu

International Meeting for Applied Geoscience and Energy (IMAGE)

... tests on the subject. A geologically realistic model is used to quantitively demonstrate the methods to reduce the bias. A field-data test...

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...

Abstract: Innovative QCs for More Effective 4D Processing; #90187 (2014)

Cyril Saint Andre, Benoit Blanco, Christian Hubans, and Benoit Paternoster

Search and Discovery.com

... problem in the framework of the 1D convolutional model. This attribute and the following developments were initially detailed by Cantillo (2012) [3...

Intelligent Prediction of Shale Oil Fracturing Curves Based on A Sequence-to-Sequence Model

Leyi Zheng, Tianbo Liang, Yunjin Wang, Fujian Zhou, Junlin Wu, Bin Wang, Jiaming Zhang, Maoqin Yang, Gong Chen, Xingyuan Liang

Unconventional Resources Technology Conference (URTEC)

... of critical events during fracturing. A novel sequence-tosequence prediction model (TCN-LSTM) is proposed that integrates a temporal convolutional...

Rapid Play Evaluation through AI Interpretation

Jacob Smith, Peter Szafian

Australian Petroleum Production & Exploration Association (APPEA) Journal

... identifying faults, we must focus on how to transfer this complexity into a useful interpretation and then into our static model. In these examples we can...

InvMixer An efficient deep neural network for seismic inversion

Tianyi Zhang, Mauricio Araya-Polo, Anshumali Shrivastava

International Meeting for Applied Geoscience and Energy (IMAGE)

... layers in UNet use learnable kernels with of size 3×3 or 5×5 to model the relationships between traces. Since a single convolutional layer has limited...

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...

An automatic velocity picking method based on object detection

Ce Bian, Weifeng Geng, Ping Yang, Pengyuan Sun, Guiren Xue, Haikun Lin

International Meeting for Applied Geoscience and Energy (IMAGE)

... an automatic velocity spectrum picking method based on object detection, and applies neural network model named FCOS (Fully Convolutional OneStage Object...

Finding the reservoir in an intensely scattered wavefield: A case study from the Central Sichuan Basin, China

John Coffin, Mengmeng Zhang, Panos Doulgeris, Peter Haffinger, Angie Kelsay, Jinsong Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... an elastic modelling scheme including interbed multiples and multiple mode conversions for the well-tie. A linear convolutional model results in perfectly...

Abstract: Hydrocarbon Generation and Expulsion Model for Upper Paleozoic Source Rocks in the Hangjinqi Area, Northern Ordos Basin, China;

Huijun Wang, Shuangfang Lu, Guiping Zhao

Search and Discovery.com

...Abstract: Hydrocarbon Generation and Expulsion Model for Upper Paleozoic Source Rocks in the Hangjinqi Area, Northern Ordos Basin, China; Huijun Wang...

Abstract: Missing Well-Log Data Prediction Using a Hybrid U-Net and LSTM Network Model; #91212 (2025)

Benard Sasu Oppong, Po Chen, En-Jui Lee, Wu-yu Liao

Search and Discovery.com

...Abstract: Missing Well-Log Data Prediction Using a Hybrid U-Net and LSTM Network Model; #91212 (2025) Benard Sasu Oppong, Po Chen, En-Jui Lee, Wu-yu...

Abstract: PetrografIA: Artificial Intelligence in Identifying Lithologies of the Barra Velha Formation - Pre-Salt; #91215 (2026)

T. F. Matos, B. D. Coutinho, E. R. Ramos, J. C. Guerrero, E. L. Faria, R. T. Barbosa

Search and Discovery.com

..., carried out by various petrographers. The developed model uses a convolutional neural network to extract relevant features from complex data, where...

GAN-based priors for Bayesian inference of subsurface geology at large scale

Kevin B. Daly, Tuan A. Tran, Brent D. Wheelock, Grant J. Seastream

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the process stratigraphy simulations. One solution is to select a generative ML model with a fully convolutional architecture, where the latent coordinate...

Seismic reservoir characterization of the Strawn Group, northern part of the Eastern Shelf, King County, North-Central Texas: Case study

Osareni C. Ogiesoba

International Meeting for Applied Geoscience and Energy (IMAGE)

...-frequency model. These reflectivities are then convolved with the wavelet to get the synthetic seismogram which is compared with the actual seismic...

Improving pre-stack inter-trace variation extraction with a self-supervised learning approach

Yifeng Fei, Xin He, Bangli Zou, Jiandong Liang, Dajun Li

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Notable examples include utilizing a geological and geophysical model-driven convolutional neural network (CNN) for the extraction of elastic parameter...

Deep learning in salt interpretation from R&D to deployment: Challenges and lessons learned

Pandu Devarakota, Apurva Gala, Zhenggang Li, Engin Alkan, Yihua Cai, John Kimbro, Dean Knott, Jeff Moore, Gislain Madiba

International Meeting for Applied Geoscience and Energy (IMAGE)

... a critical role in velocity model building in both exploration and development fields. It is a time-consuming effort that requires key domain expertise...

Understanding Seismic Amplitudes, by Steve Henry, #40135 (2004).

Search and Discovery.com

Chapter 7: Advanced Reservoir Characterization Using 3D Seismic Data in Badger Basin, Bighorn Basin, Wyoming

John E. Buggenhagen

Montana Geological Society

... by This method was selected due to its ability to output a smoothly-varying velocity function. This low-frequency velocity model...

Seismic image resolution enhancement with limited-well datasets using deep learning

Son Phan, Haibin Di, Wenyi Hu, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Lee, M. W., 1986, Spectral whitening in the frequency domain: USGS, 86–108. Li, J., X. Wu, and Z. Hu, 2021, Deep learning for simultaneous seismic...

An unsupervised intelligent stacking velocity analysis based on clustering

Lide Wang, Xingrong Xu, Jie Wu, Huahui Zeng, Yundong Yong, Yanxiang Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... efficiency and different standards. With the increasing application of wide-frequency, wide-azimuth and highdensity seismic exploration technology...

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...

Fast self-supervised learning for reconstruction of 3D seismic data

Yinshuo Li, Wenkai Lu, We Cao

International Meeting for Applied Geoscience and Energy (IMAGE)

...) Complex convolution in the frequency domain Y f =CConv(X f ), X f =Xreal + iXimag , Yreal =Wreal Xreal − Wimag Ximag , (3) Yimag =Wreal Ximag + Wimag...

Convolutional Neural Networks for Semantic Segmentation of Micro-Pores in SEM Based Images of Shales

Search and Discovery.com

Data selection for velocity model estimation using a circular shot OBN survey

Felipe T. Costa, Sergio L. E. F. da Silva, Ammir A. Karsou, Felipe Capuzzo, Roger M. Moreira, Jorge Lopez, Marco Cetale

International Meeting for Applied Geoscience and Energy (IMAGE)

..., Seismic waveform inversion in the frequency domain, Part 1: Theory and verification in a physical scale model: Geophysics, 64, 888–901, doi: https...