Comparison of Seismic Reconvolution and Gabor Deconvolution in Improving Seismic Images to Detect Local Fluid Trapping

Madaniya Oktariena, Wahyu Triyoso

Indonesian Petroleum Association

....   The convolutional model is constructed using the Gabor Transform of a non-stationary seismic to estimate Gabor Transform of the reflectivity. While...

The use of FWI in coal exploration

Mehdi Asgharzadeh, Maryam Bahri, Milovan Urosevic

Petroleum Exploration Society of Australia (PESA)

... wave with dominant frequency of 80 Hz and propagation velocity of 3000 m/s near the boundaries of the model. To simulate shot records, we selected FD...

Orogenic gold prospectivity mapping using machine learning

Mike McMillan, Jen Fohring, Eldad Haber, Justin Granek

Petroleum Exploration Society of Australia (PESA)

... developed a new algorithm for mineral prospectivity mapping using a VNet deep convolutional neural network and applied it to finding gold at the Committee...

Integrated well data and 3D seismic inversion study for reservoir delineation and description

Qazi Sohail Imran, Numair Ahmad Siddiqui, Abdul Halim Abdul Latif, Yasir Bashir, Almasgari Abdalsalam, Abduh Saeed Ali, Muhammad Jamil

Geological Society of Malaysia (GSM)

... because of its bandlimited nature. A plausible broader band frequency is difficult to build when as the model (known as an a priori model) building...

Abstract: Cost Efficient Acquisition to Reduce Coarse Land 3D Line Spacings Through Beyond Nyquist Interpolation and Wavefield Reconstruction for Signal and Noise; #90187 (2014)

Bill Goodway

Search and Discovery.com

... not exceed Nyquist. Both authors concluded that the assumption of a smoothly varying linear model for the wavefield (or a plane wave decomposition...

Insights using machine learning in predicting faults and horizons: A case study onshore Texas

Dan Ferdinand Fernandez, Mustafa Karer, Richard Hearn, Ryan King, Sunil Manikani, Gavin Menzel-Jones

International Meeting for Applied Geoscience and Energy (IMAGE)

... Texas dataset. By employing ML technology through convolutional neural networks (CNNs) trained on real data we predict multiple layers of faults from...

Simultaneous imaging of basement relief and varying susceptibility in deep-learning approach

Zhuo Liu, Yaoguo Li

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the basement rock assuming a 2D model. Particularly, the U-net architecture followed by a fully connected (FC) layer is adopted to map the information...

HIGH-PRECISION ALGORITHM FOR GRAIN SEGMENTATION OF THIN SECTIONS BY MULTI-ANGLE OPTICAL-MICROSCOPIC IMAGES

Timur Murtazin, Zufar Kayumov, Vladimir Morozov, Radik Akhmetov, Anton Kolchugin, Dmitrii Tumakov, Danis Nurgaliev, Vladislav Sudakov

Journal of Sedimentary Research (SEPM)

.... (2020) for semantic segmentation of the porosity of petrographic thin sections. The U-Net model is a fully connected convolutional neural network...

Enhancement of the reliability of the ant-tracking algorithm via U-net and dual-threshold iteration

Seunghun Choi, Yongchae Cho

International Meeting for Applied Geoscience and Energy (IMAGE)

... squared error, root mean squared error) to determine the most effective for model training, and the Mean Squared Error function excelled in five...

A Deep Learning-Based Surrogate Model for Rapid Assessment of Geomechanical Risks in Geologic CO2 Storage

Fangning Zheng, Birendra Jha, Behnam Jafarpour

Carbon Capture, Utilization and Storage (CCUS)

... storage. Using simulated data, we train a U-Net convolutional neural network to learn a mapping between well locations s and spatially distributed model...

Generalization Capability of Data-driven Deep Learning Models for Seismic Full-waveform Inversion: An Example Using the OpenFWI Dataset

Ayrat Abdullin, Umair Bin Waheed

International Meeting for Applied Geoscience and Energy (IMAGE)

... model, and ill-posedness of the inverse problem. There is a lack of Data-driven approaches have witnessed development for FWI, including multilayer...

Robust Event Recognition in Real-Time Hydraulic Fracturing Data for Live Reporting and Analysis

Samid Hoda, Jessica Iriarte

Unconventional Resources Technology Conference (URTEC)

... by the model and the frequency of the analysis can be modified to accommodate a variety of internet and streaming conditions. This approach URTeC 2782...

Chapter Nine: Inversion and Interpretation of Impedance Data

Rebecca B. Latimer

AAPG Special Volumes

... of inversion and forward modeling. Figure 9-3. Graphic representation of trace inversion from the reflection series to the low-frequency earth model...

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...

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...

An Introduction to Deep Learning: Part III

Lasse Amundsen, Hongbo Zhou, Martin Landrø

GEO ExPro Magazine

... computer model that learns to perform classification tasks directly from images. The one that started it all was the 2012 publication ‘ImageNet...

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...

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...