Geostatistical Reservoir Modeling 1st Edition by Michael J Pyrcz, Clayton V Deutsch ISBN 0199731446 9780199731442 by Clayton V. Deutsch 0195138066 instant download after payment.

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Product details:

ISBN 10: 0199731446 
ISBN 13: 9780199731442
Author:  Michael J Pyrcz, Clayton V Deutsch

In this revised second edition, Deutsch collaborates with co-author Michael Pyrcz to provide an expanded (in coverage and format), full color illustrated, more comprehensive treatment of the subject with a full update on the latest tools, methods, practice, and research in the field of petroleum Geostatistics.

Key geostatistical concepts such as integration of geologic data and concepts, scale considerations, and uncertainty models receive greater attention, and new comprehensive sections are provided on preliminary geological modeling concepts, data inventory, conceptual model, problem formulation, large scale modeling, multiple point-based simulation and event-based modeling. Geostatistical methods are extensively illustrated through enhanced schematics, work flows and examples with discussion on method capabilities and selection. For example, this expanded second edition includes extensive discussion on the process of moving from an inventory of data and concepts through conceptual model to problem formulation to solve practical reservoir problems.

A greater number of examples are included, with a set of practical geostatistical studies developed to illustrate the steps from data analysis and cleaning to post-processing, and ranking. New methods, which have developed in the field since the publication of the first edition, are discussed, such as models for integration of diverse data sources, multiple point-based simulation, event-based simulation, spatial bootstrap and methods to summarize geostatistical realizations.

Geostatistical Reservoir Modeling 1st Table of contents:

Part I: Fundamental Concepts

• Chapter 1: Introduction

  • Plan for the Book
  • Key Concepts in Geostatistical Reservoir Modeling
  • Motivation for Reservoir Models
  • Data for Reservoir Modeling
  • An Introductory Example
  • Work Flow (Overview of the Modeling Process)

• Chapter 2: Preliminary Statistical Concepts

  • Geological Populations and Stationarity
  • Classical Statistical Tools (Histograms, measures of central tendency and dispersion)
  • Data Transformations (e.g., normal score transformation)
  • Declustering and Correcting for Non-Representative Data
  • Handling Outliers and Extreme Values
  • Work Flow

• Chapter 3: Gridding Reservoir Layers

  • Gridding for Geologic Modeling
  • Stratigraphic Correlation and Coordinates
  • Handling Faults
  • Uncertainty in Reservoir Geometry
  • Work Flow

• Chapter 4: Quantifying Spatial Correlation

  • The Random Function Concept
  • Calculating Experimental Variograms (Variograms and Covariance)
  • Interpreting Experimental Variograms
  • Horizontal and Vertical Variograms
  • Variogram Modeling (fitting theoretical models)
  • Cross Variograms (for multiple variables)
  • Work Flow

• Chapter 5: Preliminary Mapping Concepts

  • Kriging (Simple, Ordinary, Universal)
  • Sequential Gaussian Simulation
  • Direct Sequential Simulation
  • Indicator Formalism
  • P-Field Methods
  • Accounting for Trends (drift modeling)
  • Work Flow

Part II: Advanced Modeling Methods

• Chapter 6: Cell-Based Facies Modeling

  • Choosing the Appropriate Method for Facies
  • Sequential Indicator Simulation (SIS)
  • Truncated Gaussian Simulation
  • Cleaning Cell-Based Facies Realizations
  • Work Flow

• Chapter 7: Object-Based Facies Modeling

  • Background and Concepts of Object-Based Models
  • Stochastic Shales (modeling discrete bodies)
  • Fluvial Modeling (e.g., channels, levee deposits)
  • Non-Fluvial Depositional Systems
  • Work Flow

• Chapter 8: Porosity and Permeability Modeling

  • Background (Petrophysical properties)
  • Gaussian Techniques for Porosity Modeling
  • Porosity/Permeability Transforms
  • Gaussian Techniques for Permeability Modeling
  • Indicator Technique for Permeability
  • Work Flow

• Chapter 9: Simulated Annealing for Geostatistics

  • Background and Principles of Simulated Annealing
  • Steps in Annealing for Geostatistical Problems
  • Common Problem Areas and Solutions
  • Place of Simulated Annealing in the Workflow

• Chapter 10: Uncertainty Management

  • Models of Uncertainty in Reservoir Characterization
  • Cross Validation and the Jackknife (model validation)
  • Checking Distributions of Uncertainty
  • How Many Realizations are Needed?
  • Ranking Realizations for Decision Making
  • Decision Making with Uncertainty (economic evaluation, risk assessment)

• Chapter 11: Special Topics

  • Multiple Point Statistics (MPS)
  • Model Scaling and Upscaling
  • Multivariate Mapping
  • Parameter Uncertainty
  • Process Mimicking Models
  • Model Post-Processing and Checking
  • Integration of Diverse Data Sources (e.g., seismic, well logs, production data)
  • Event-Based Simulation
  • Spatial Bootstrap
  • Summarizing Geostatistical Realizations

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Tags: Michael J Pyrcz, Clayton V Deutsch, Geostatistical