PPT-Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case

Author : brianna | Published Date : 2023-10-29

Siddharth Garia 1 Arnab Kumar Pal 1 Karangat Ravi 2 Archana M Nair 2 1 Research Scholar Department of Civil Engineering Indian Institute of Technology Guwahati

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Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case: Transcript

Siddharth Garia 1 Arnab Kumar Pal 1 Karangat Ravi 2 Archana M Nair 2 1 Research Scholar Department of Civil Engineering Indian Institute of Technology Guwahati 781039 India. Seismic velocities – P & S. Relationship to elastic moduli. Seismic anisotropy. . -- directional variation in seismic velocity. Seismic Attenuation – 1/. Q. p. & 1/Q. s. . -- What is seismic attenuation?. . elastic. . waveform. . and. . gravity. . inversion. . for. . improved. . density. . model. . resolution. . applied. . to. . the. . Marmousi. -II . model. Daniel Wehner, Daniel Köhn, Denise De Nil, Sabine Schmidt, Said al . What are Artificial Neural Networks (ANN)?. ". Colored. neural network" by Glosser.ca - Own work, Derivative of File:Artificial neural . network.svg. . Licensed under CC BY-SA 3.0 via Commons - https://commons.wikimedia.org/wiki/File:Colored_neural_network.svg#/media/File:Colored_neural_network.svg. Table of Contents. Part 1: The Motivation and History of Neural Networks. Part 2: Components of Artificial Neural Networks. Part 3: Particular Types of Neural Network Architectures. Part 4: Fundamentals on Learning and Training Samples. Amit . Suman and Tapan Mukerji. SCRF Annual Meeting. 8 - 9. th. May 2013. Stanford University. 2. Joint Inversion Loop. Generate. multiple. models. Evaluate. misfit. .. Reservoir. Model. Observed flow and seismic response. PETROLEUM ENGINEERING. ÂNGELA PEREIRA (angela.pereira@tecnico.ulisboa.pt). Introduction. Frontier . basins and unexplored areas are challenging new venture projects due to the high risk and uncertainty related to scarcity of the data. Geological knowledge of the basin and seismic data are the main support in the first stages of an . Birhanu. . Abera. Managing . Data from . Seismic Networks. Pretoria. , . South Africa. 20 Aug . 2017. Outline. Introduction. M. ap of the current . ESSN . network. Data Acquisition and processing. D. Greg Lewis (MSR and NBER). Matt Taddy (MSR and Chicago). Goal. To work out how to use instrumental variables for counterfactual prediction using (arbitrary) machine learners. To explore the practicalities of implementing this approach using deep neural nets. Kurt J. Marfurt (The University of Oklahoma). Satinder Chopra (Arcis). Attributes for Resource Plays. 7-. 1. 7-. 2. Course Outline. . A short overview of spectral decomposition. A short overview of geometric attributes. Full . Waveform Inversion: Is . FWI . a . Bust. , a Boom, or . Becoming a . Commodity?. . Gerard . Schuster. KAUST. 0.0. 1.0. Dow Jones Index . Avg. /decade. 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s 2010-2016. WELCOME . TO THE PRESENTATION ON THE CAMEROON SEISMIC NETWORK. PRESENTATION OF THE CAMEROON SEISMIC NETWORK . . MOUZONG PEMI MARCELIN. Department. of . Physics. , . University. of . Yaounde. 1. Presentation Outlines. 1.0. . INTRODUCTION. 2.0. . LITERATURE REVIEW. 3.0 METHODOLODY. 1.1 Project Background. 1.2 Problem Statement. 1.3 Objective. s. 1.4 Scope of Study. 4.0 RESULTS & DISCUSSION. Usman Mohseni1, Sai Bargav Muskula2. 1,2Research Scholar, Department of Civil Engineering, IIT Roorkee, Roorkee, INDIA. INTRODUCTION. Rainfall-runoff modelling is one of the most prominent hydrological models used to examine the relation between rainfall and runoff . Mark Hasegawa-Johnson. April 6, 2020. License: CC-BY 4.0. You may remix or redistribute if you cite the source.. Outline. Why use more than one layer?. Biological inspiration. Representational power: the XOR function.

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