Multiscenario-based deep learning workflow for high-resolution seismic inversion on Brazil presalt 4D

Yang Xue, Dan Clarke, Kanglin Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... model and 1D convolutional modeling. The training datasets are generated from scenario-based modeling with each group trained separately with a DL...

Augmented Data Management for Subsurface CCUS Data Sets

Rhys Blake, Jess B. Kozman, James Lamb, Lorena Pelegrin

Carbon Capture, Utilization and Storage (CCUS)

... workflows for using artificial and convolutional neural networks to find information in legacy documents that can predict physical properties...

Date-driven seismic velocity inversion via deep residual U-net

Yiran Huang, Chuang Pan, Qingzhen Wang, Jun Li, Jianhua Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

..., into convolutional neural network, which can propagate useful discriminative information from the low level to the high level, and thus improve...

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...

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...

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...

Artificial Intelligence (AI) Based Personnel Protective Equipment (PPE) Monitoring - Case Study in Rokan Drilling Operation

Freddy Frinly Rizki, Ade Anggi N S, Ari Sukma Negara

Indonesian Petroleum Association

... and in different light conditions. The next step was to use deep learning technology such as Yolov4 and train the model using the PPE datasets...

A Deep Learning Workflow for Integrated Geological, Petrophysical, and Geomechanical Interpretation

Vanessa Simoes, Atul Katole, Bhuvaneswari Sankaranarayanan, Tao Zhao, Aria Abubakar

Unconventional Resources Technology Conference (URTEC)

... wells, which serve as the basis for training an DL model for marker propagation. This model predicts major markers on the remaining wells, and a marker QC...

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...

Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling

Wen Pan, Honggeun Jo, Javier E. Santos, Carlos Torres-Verdín, and Michael J. Pyrcz

AAPG Bulletin

... a diverse ensemble of conditional, multiscale, architectural and petrophysical, property model realizations reproducing the patterns. We demonstrate...

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...

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...

Aiding self-supervised coherent noise suppression by the introduction of signal segmentation using blind-spot networks

Sixiu Liu, Claire Birnie, Tariq Alkhalifah, Andrey Bakulin

International Meeting for Applied Geoscience and Energy (IMAGE)

... al., 2019; Wang and Chen, 2019; Birnie et al., 2021a). A number of NN-based denoising procedures utilise Convolutional Neural Networks (CNNs) to learn...

Joint 3D inversion of gravity and magnetic data using deep learning neural networks

Nanyu Wei, Dikun Yang, Zhigang Wang, Yao Lu

International Meeting for Applied Geoscience and Energy (IMAGE)

... uses a supervised deep neural network, developed based on fully convolutional networks and further combined with a U-Net architecture. Two multi-model...

Harnessing AI and Computer Vision for Efficient Geothermal Field Exploration and Prospect Evaluation

Moamen Gasser, Danny Rehg, Marcus Oesterberg, Nathan Meehan

Unconventional Resources Technology Conference (URTEC)

.... The model showed a great alliance with the experts’ prediction of the geothermal potential presence. This AI tool is an immensely powerful tool especially...

AI: a Game Changer in Seismic Acquisition and Processing

Matt Deighton, Sverre Olsen

GEO ExPro Magazine

... the trained Convolutional Neural Network (CNN) to remove of fraud detection is that the coherent image domain noise. Noise indicated by yellow arrows...

Lithofacies identification in cores using deep learning segmentation and the role of geoscientists: Turbidite deposits (Gulf of Mexico and North Sea)

Oriol Falivene, Neal C. Auchter, Rafael Pires de Lima, Luuk Kleipool, John G. Solum, Pedram Zarian, Rachel W. Clark, and Irene Espejo

AAPG Bulletin

... of convolutional neural networks (CNNs) using semantic segmentation architectures to automate the identification of common lithofacies from core images. Images...

Improving Resolution and Clarity with Neural Networks; #41911 (2016)

Christopher P. Ross

Search and Discovery.com

... and anisotropic model parameters simultaneously with wave-equation modeling. Well logs may be used as part of the low-frequency initial model building...