Abstract: Reflectivity Color Correction in Gabor Deconvolution; #90211 (2015)

Carlos Montana and Gary Margrave

Search and Discovery.com

... relies on the fulfillment of a set of assumptions on which the convolutional model is based: stationarity, minimum phase wavelet, white reflectivity...

Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study From an Unconventional Shale Reservoir

Ted Holden, Ruth Kurian, Mohammed Ibrahim, Daniel Hampson, Jonathan Downton

Unconventional Resources Technology Conference (URTEC)

...) Synthetic angle gathers are then generated for each pseudo-well using a convolutional model in which the P-wave reflection coefficients calculated using...

Do We Really Need Deep Learning? A Study on Play Identification using SEM Images

Hanyan Zhang, Max T. Kasumov, Deepak Devegowda, Mark E. Curtis

Unconventional Resources Technology Conference (URTEC)

... classification. For all image resolutions considered, surprisingly, the simplest and shallowest one-convolutional layer model performs remarkably well...

Seismic simulations of experimental strata

Lincoln Pratson, Wences Gouveia

AAPG Bulletin

... of the resulting seismic section. Convolutional Model The convolutional seismic model convolves a seismic wavelet with a time series of reflection...

ABSTRACT: Shaping the Wavelet, by Wang, Yuchun E.; Huo, Shoudong; #90141 (2012)

Search and Discovery.com

Seismic Facies Segmentation Using Deep Learning; #42286 (2018)

Daniel Chevitarese, Daniela Szwarcman, Reinaldo Mozart D. Silva, Emilio Vital Brazil

Search and Discovery.com

... selected a trained convolutional neural network (CNN) with the highest accuracy on the classification task. Then, we modified the final part...

A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data

Hamzeh Alimohammadi, Hamid Rahmanifard, and Shengnan Nancy Chen

AAPG Bulletin

... at a batch size of 20. Figure 5. Optimum number of batch size (A) and dropout rate (B) for hybrid convolutional neural networks–long short-term memory model...

Bayesian variational auto-encoder for seismic wavelet extraction

Ammar Ghanim, Ricard Durall, Norman Ettrich

International Meeting for Applied Geoscience and Energy (IMAGE)

... approaches is the convolutional model. In the well-tie process, the wavelet is convolved with the reflectivity series along the well path to produce...

Multi-realization seismic data processing with deep variational preconditioners

Matteo Ravasi

International Meeting for Applied Geoscience and Energy (IMAGE)

... problems of the form, d = Gx + ε, where the model x and the observed data d are connected via a linear operator G. Moreover, ε represents a combination...

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...

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...

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...

Abstract: Short-time Wavelet Estimation in the Homomorphic Domain; #90174 (2014)

Roberto H. Herrera and Mirko van der Baan

Search and Discovery.com

... phases in both the wavelet and the reflectivity. Theory The seismic signal is described by the convolutional model (Ulrych, 1971): s(t) = w(t) ⋆ r...

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...

An Introduction to Deep Learning: Part II

Lasse Amundsen, Hongbo Zhou, Martin Landrø

GEO ExPro Magazine

... often the model fails to predict the correct answer in their top five guesses (the top-5 error rate), in descending order of confidence. ILSVRC 2012...

Deep Learning Models for Methane Emissions Identification and Quantification

Ismot Jahan, Mohamed Mehana, Bulbul Ahmmed, Javier E. Santos, Dan O’Malley, Hari Viswanathan

Unconventional Resources Technology Conference (URTEC)

... to prepare the data for the machine learning model. In this section, we will outline the preprocessing and Convolutional Neural Network (CNN) model...

Seismic Forward Modeling of Semberah Fluvio-Deltaic Reservoir

Adi Widyantoro, Wahyu Dwijo Santoso

Indonesian Petroleum Association

... modeling at each UKM wells to understand lithology and fluid effects over amplitude variations, 3) conceptual 2D convolutional model to understand boundary...

Deterministic and Statistical Wavelet Processing

Lee Lu

Southeast Asia Petroleum Exploration Society (SEAPEX)

... on the convolutional model for a seismic trace: it is assumed that an observed trace, x, is the convolution of an “effective wavelet”, w, with an “effective reflectivity...

Machine learning applications to seismic structural interpretation: Philosophy, progress, pitfalls, and potential

Kellen L. Gunderson, Zhao Zhang, Barton Payne, Shuxing Cheng, Ziyu Jiang, and Atlas Wang

AAPG Bulletin

... amplitude (grayscale) and fault probability from convolutional neural network (CNN) (red-white scale). The CNN model accurately predicts the steeply dipping...

Abstract: Kirchhoff Imaging with Adaptive Greens Functions for Compensation for Dispersion, Attenuation, and Velocity Imprecision; #90187 (2014)

Andrew V. Barrett

Search and Discovery.com

... the imaging at higher frequencies. Here I present a method for deriving and applying adaptively a short, white operator to compensate...

4D Finite Difference Forward Modeling within a Redefined Closed-Loop Seismic Reservoir Monitoring Workflow, #41922 (2016).

David Hill, Dominic Lowden, Sonika, Chris Koeninger

Search and Discovery.com

...-field coupled dynamic integrated earth model to surface. From which 3D grids of petro-elastic parameters for a range of reservoir simulations...

Accurate seismic data interpolation based on multiband intelligent training

Xueyi Sun, Benfeng Wang, Tongtong Mo

International Meeting for Applied Geoscience and Energy (IMAGE)

... information about subsurface structures and geological features. During the optimization of convolutional neural network (CNN)-assisted seismic data...

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...

Deep convolutional neural networks for generating grain-size logs from core photographs

Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott Cole, and Ishtar Barranco

AAPG Bulletin

...Deep convolutional neural networks for generating grain-size logs from core photographs Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott...

VSP Guided Reprocessing and Inversion of Surface Seismic Data

R. Gir, Dominique Pajot, Serge Des Ligneris

Southeast Asia Petroleum Exploration Society (SEAPEX)

... seismic data is known as the “convolutional model of the seismogram”. This model states that after proper data processing, the final seismic data has...