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...

Application of Both Physics-Based and Data-Driven Techniques for Real-Time Screen-Out Prediction with High Frequency Data

Jianlei John Sun, Arvind Battula, Brandon Hruby, Paymon Hossaini

Unconventional Resources Technology Conference (URTEC)

... (i.e., screen-out) using domain expert knowledge and three-segment curve fitting. Modified Inverse Slope Model In order to address the issues mentioned...

Abstract: Utilizing Seismic Attributes for Machine Assisted Fault Detection and Extraction; #91204 (2023)

Muhammad Khan, Yasir Bashir, Saleh Dossary, Syed Ali

Search and Discovery.com

... labelled data as transfer learning to update the foundation Convolutional Neural Network (CNN) model that was initially trained on synthetic data...

Deep learning Laplace-Fourier full-waveform inversion with virtual supershot gathers

Lei Fu, Daniele Colombo, Weichang Li, Ernesto Sandoval-Curiel, Ersan Turkoglu

International Meeting for Applied Geoscience and Energy (IMAGE)

... the velocity model from seismic data organized in the virtual super gathers (VSG) in the Laplace-Fourier domain by deep learning network (DNN). The proposed new...

Technical Article: Finding Subtle Traps with Seismic: Interpretative Criteria Clarified

A. Easton Wren

Petroleum Exploration Society of Australia (PESA)

... to what the section should look like. Progressive understanding of the seismic method introduced the concept of the convolutional model: this found...

Methods of estimating wavelet stationarity, stabilizing non-stationarity, and evaluating its impact on inversion: A synthetic example using SEAM II Barrett unconventional model

Jesse Buckner, Michael Fry, Joe Zuech, Peter Harris, Bill Shea

International Meeting for Applied Geoscience and Energy (IMAGE)

... is simulated across a continuous 3D convolutional synthetic seismic volume, derived from the earth model of the SEAM II Barrett dataset. Multiple...

Using Machine Learning to Automate FDI Analysis

Reid Thompson, Lance Legel, Thomas Hanlon

Unconventional Resources Technology Conference (URTEC)

... is an automated stage detection model. The core of the stage detection model is a onedimensional deep convolutional U-net neural network with residual layers...

Abstracts: Revisiting Homomorphic Wavelet Estimation and Phase Unwrapping; #90173 (2015)

Roberto H. Herrera and Mirko van der Baan

Search and Discovery.com

.... The convolutional operator is denoted by  and η (t ) is the additive noise. In the frequency domain equation (1) can be expressed as: = W ( f ) R( f ) + Ν...

APPLICATION OF MACHINE LEARNING IN COORDINATION NUMBER ESTIMATION FOR RESERVOIR ROCK EXTRACTION

Dinanti Syafirani Zahra, Maharani Arisandy, Shafa Maura Fidela, Aldenia Alexandra, and Irwan Ary Dharmawan

Indonesian Petroleum Association

..., relying on experimental data or manual image analysis. This study explores a machine learning approach using a custom- developed Convolutional Neural...

ABSTRACT: Seismic Heterogeneity Cubes and Corresponding Equiprobable Simulations; #90013 (2003)

Matthias Imhof, William Kempner

Search and Discovery.com

... attributes. Instead, model statistics with only six parameters are fitted to the raw statistics. These six parameters include three orthogonal...

Abstract: A Transfer Learning Approach to Rock Property Estimation Workflows;

Ahmad Mustafa, Motaz Alfarraj, Ghassan Alregib

Search and Discovery.com

.... This results in vertical discontinuities in the computed property volumes using such a model, since it becomes sensitive to lateral changes...

Research on fault-karst reservoir identification method based on deep convolutional network

Zhipeng Gui, Junhua Zhang, Hong Zhang, Dong Chen, Pengbo Yin

International Meeting for Applied Geoscience and Energy (IMAGE)

... model adopted in this paper is shown in Figure 1a. Following the model input, it is connected to an MFEB module, followed by two convolutional layers...

Abstract: Color Correction for Gabor Deconvolution and Nonstationary Phase Rotation; #90171 (2013)

Peng Cheng and Gary F. Margrave

Search and Discovery.com

... deconvolution is based on a nonstationary convolution model of the seismic trace. Margrave (1998) presented a nonstationary convolutional model, which...

Abstract: Color Correction for Gabor Deconvolution and Nonstationary Phase Rotation; #90171 (2013)

Peng Cheng and Gary F. Margrave

Search and Discovery.com

... deconvolution is based on a nonstationary convolution model of the seismic trace. Margrave (1998) presented a nonstationary convolutional model, which...

Geobody-oriented interpretable velocity fusion modeling in depth domain with seismic facies informed segmentation method

Meng Li, Qingcai Zeng, Hao Shou, Nan Qin, Chunming Wang, Tongsheng Zeng

International Meeting for Applied Geoscience and Energy (IMAGE)

... modeling and combines it with seismic inversion to improve the resolution of velocity model in depth domain. This work is based on the assumption...

NLP applications in the oil and natural gas industry

Prashanth Pillai, Srikanth Ryali, Hiren Maniar, Purnaprajna Mangsuli, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... of previous architectures such as long-shortterm-memory (LSTM) (Hochreiter et al., 1997) and convolutional neural network (CNN) (Krizhevsky et al...

Efficient and accurate velocity building from Gramian-constrained multiphysics reflection and transmission data

Jide Nosakare Ogunbo

International Meeting for Applied Geoscience and Energy (IMAGE)

..., the use of the convolutional model (Buland and Omre, 2003), by the z-transform, is readily more practical than the seismic operator for either...

Revolutionizing seismic data compression: Unlocking the power of stable diffusion neural networks

Ayrat Abdullin, Umair Bin Waheed, Naveed Iqbal

International Meeting for Applied Geoscience and Energy (IMAGE)

... principal component analysis (DPCA). By leveraging a mixture model to represent the statistics of seismic traces and computing global principal components...

Unlocking Lithium Potential from Oilfield Brines: A Deep Learning-Driven Resource Assessment

Rajkanwar Singh, Saaksshi Jilhewar, Audrey Der, Ryan Mercer, Vikram Jayaram

Unconventional Resources Technology Conference (URTEC)

... structured chemical data. • CNN-Bidirectional GRU (BiGRU) Model: Combining convolutional layers with bidirectional Gated Recurrent Units (GRUs) to enhance...

Transfer Learning Applied to Seismic Images Classification

Search and Discovery.com

Abstract: CO2 Distribution Prediction Using Machine Learning Based Proxy Model in Geological Carbon Sequestration;

Zhi Zhong, Alexander Sun

Search and Discovery.com

... knowledge and assumptions on input data distributions. In particular, our cDC-GAN model is designed to learn cross-domain mappings between high...

Seismic reflectivity inversion via a regularized deep image prior

Hongling Chen, Mauricio D. Sacchi, Jinghuai Gao

International Meeting for Applied Geoscience and Energy (IMAGE)

... deconvolution with frequency-domain constraints: Geophysics, 59, 938– 945, doi: https://doi.org/10.1190/1.1443653. Shi, Y., X. Wu, and S. Fomel, 2020...

Deep Earth: Leveraging neural networks for seismic exploration objectives

Tariq Alkhalifah, Claire Birnie, Randy Harsuko, Hanchen Wang, Oleg Ovcharenko

International Meeting for Applied Geoscience and Energy (IMAGE)

... cost of training, availability of training samples, the applicability of the trained model to real data (generalization), and more importantly...

Assessing properties of internal multiples for different geologies

Paul Ras, Mikhail Davydenko, Eric Verschuur

International Meeting for Applied Geoscience and Energy (IMAGE)

... analysis: multiple reflectivity power spectrum (left) and frequency-time (FT) plot (right), model 2. The VSP display with FWMod modeling in Figure 10...

Earthquake Detection and Focal Mechanism Calculation Using Artificial Intelligence

Shane Quimby, Yanwei Zhao, Jie Zhang, GeoTomo

Unconventional Resources Technology Conference (URTEC)

... network (FCN). FCNs are supervised deep learning networks based on convolutional layers, without being fully connected. This necessitates fewer model...