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Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling

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Cover of 'Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Probabilistic Analytics for Geoscience Data
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    Chapter 3 Statistical Analysis of Geoscience Data
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    Chapter 4 Correlation Analysis
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    Chapter 5 Principal Component Analysis
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    Chapter 6 Regression-Based Predictive Analytics
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    Chapter 7 Introduction to Geoscience Data Analytics Using Machine Learning
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    Chapter 8 Multiscale Heterogeneities in Reservoir Geology and Petrophysical Properties
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    Chapter 9 Petrophysical Data Analytics for Reservoir Characterization
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    Chapter 10 Facies and Lithofacies Classifications from Well Logs
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    Chapter 11 Generating Facies Probabilities by Integrating Spatial and Frequency Analyses
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    Chapter 12 Seismic Data Analytics for Reservoir Characterization
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    Chapter 13 Geostatistical Variography for Geospatial Variables
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    Chapter 14 Introduction to Geological and Reservoir Modeling
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    Chapter 15 Constructing 3D Model Framework and Change of Support in Data Mapping
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    Chapter 16 Geostatistical Estimation Methods: Kriging
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    Chapter 17 Stochastic Modeling of Continuous Geospatial or Temporal Properties
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    Chapter 18 Geostatistical Modeling of Facies
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    Chapter 19 Porosity Modeling
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    Chapter 20 Permeability Modeling
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    Chapter 21 Water Saturation Modeling and Rock Typing
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    Chapter 22 Hydrocarbon Volumetrics Estimation
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    Chapter 23 Introduction to Model Upscaling, Validation and History Match
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    Chapter 24 Uncertainty Analysis
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Title
Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling
Published by
Springer International Publishing, June 2019
DOI 10.1007/978-3-030-17860-4
ISBNs
978-3-03-017859-8, 978-3-03-017860-4
Authors

Ma, Y. Z.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 21%
Researcher 5 15%
Student > Master 5 15%
Student > Ph. D. Student 4 12%
Lecturer > Senior Lecturer 3 9%
Other 1 3%
Unknown 8 24%
Readers by discipline Count As %
Earth and Planetary Sciences 10 30%
Engineering 8 24%
Agricultural and Biological Sciences 1 3%
Energy 1 3%
Chemistry 1 3%
Other 1 3%
Unknown 11 33%