Harness Oil and Gas Big Data with Analytics E-Book

Keith R. Holdaway

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: SAS Institute Inc
• Sprache: Englisch

Seitenzahl: 520

Veröffentlichungsjahr: 2014

Beschreibung

Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: * Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data * Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions * Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.

Leseprobe

Contents

Preface

Chapter 1: Fundamentals of Soft Computing

Current Landscape in Upstream Data Analysis

Evolution from Plato to Aristotle

Descriptive and Predictive Models

The SEMMA Process

High-Performance Analytics

Three Tenets of Upstream Data

Exploration and Production Value Propositions

Oilfield Analytics

I am a . . .

Notes

Chapter 2: Data Management

Exploration and Production Value Proposition

Data Management Platform

Array of Data Repositories

Structured Data and Unstructured Data

Extraction, Transformation, and Loading Processes

Big Data Big Analytics

Standard Data Sources

Case Study: Production Data Quality Control Framework

Best Practices

Notes

Chapter 3: Seismic Attribute Analysis

Exploration and Production Value Propositions

Time-Lapse Seismic Exploration

Seismic Attributes

Reservoir Characterization

Reservoir Management

Seismic Trace Analysis

Case Study: Reservoir Properties Defined by Seismic Attributes

Notes

Chapter 4: Reservoir Characterization and Simulation

Exploration and Production Value Propositions

Exploratory Data Analysis

Reservoir Characterization Cycle

Traditional Data Analysis

Reservoir Simulation Models

Case Studies

Notes

Chapter 5: Drilling and Completion Optimization

Exploration and Production Value Propositions

Workflow One: Mitigation of Nonproductive Time

Workflow Two: Drilling Parameter Optimization

Case Studies

Notes

Chapter 6: Reservoir Management

Exploration and Production Value Propositions

Digital Oilfield of the Future

Analytical Center of Excellence

Analytical Workflows: Best Practices

Case Studies

Notes

Chapter 7: Production Forecasting

Exploration and Production Value Propositions

Web-Based Decline Curve Analysis Solution

Unconventional Reserves Estimation

Case Study: Oil Production Prediction for Infill Well

Notes

Chapter 8: Production Optimization

Exploration and Production Value Propositions

Case Studies

Notes

Chapter 9: Exploratory and Predictive Data Analysis

Exploration and Production Value Propositions

EDA Components

EDA Statistical Graphs and Plots

Ensemble Segmentations

Data Visualization

Case Studies

Notes

Chapter 10: Big Data: Structured and Unstructured

Exploration and Production Value Propositions

Hybrid Expert and Data-Driven System

Case Studies

Multivariate Geostatistics

Big Data Workflows

Integration of Soft Computing Techniques

Notes

Glossary

About the Author

Index

End User License Agreement

List of Illustrations

Figure 1.1 Artificial Neural Network

Figure 1.2 Analytics Lifecycle Turning Raw Data into Knowledge

Figure 1.3 SEMMA Process for Data-Mining Workflows

Figure 1.4 Heat Map Highlighting Gas–Oil Separation Plants (GOSPs) and Associated Water Cut

Figure 1.5 E&P Value Propositions

Figure 1.6 Potential Oilfield Analytics Framework

Figure 2.1 Big Data Combined with Big Analytics

Figure 2.2 Oil and Gas Four-Tiered DM Architecture

Figure 2.3 Multivariant Perspective

Figure 2.4 Multidimensional Perspective

Figure 2.5 Multivariate Perspective

Figure 2.6 Stochastic Perspective

Figure 2.7 Information and Operational Technology Comparison

Figure 2.8 Data Integration Management Lifecycle

Figure 2.9 Pareto’s Principle: The 80/20 Rule

Figure 2.10 Data Converted from Their Raw State to Garner Knowledge

Figure 3.1 Key Workflow Steps in a Comprehensive Seismic Data Analysis

Figure 3.2 3D Trace Plots

Figure 3.3 Trace Attribute Relationships

Figure 3.4 Trace Comparison Illustrating Mean Amplitude

Figure 3.5 Scatter Plot of Amplitudes Comparing Trace 101 and 224

Figure 3.6 Trace Cluster Analysis

Figure 3.7 Wavelet Decomposition Detailing Coefficient Plot

Figure 3.8 Wavelet Decomposition

Figure 3.9 Single-Sided Spectrum Analysis

Figure 3.10 Cleansing the Spectrum Illustrates 10 Dominant Frequencies

Figure 3.11 Scree and Variance Plots

Figure 3.12 Component Pattern Profiles

Figure 3.13 Score Plot for the 39 Traces Under Study

Figure 3.14 Hotteling T2 Chart

Figure 3.15 Distance-to-Model Valuation

Figure 3.16 Trace with Its Hilbert Transform

Figure 3.17 Instantaneous Phase Attribute Plot

Figure 3.18 Instantaneous Frequency Attribute Plot

Figure 3.19 Signal Envelope Deduced via a Hilbert Transform

Figure 3.20 Seismic Attribute PCA/SOM/ANN Pattern Recognition Workflow

Figure 3.21 Dimension Reduction Using Line Segments with Mean

Figure 3.22 Dendrogram of Wells Classified by Cumulative Liquids

Figure 3.23 Hierarchical and K-Means Clustering to Evaluate EOR

Figure 3.24 Principal Components Analysis of Singular Seismic Attribute

Figure 3.25 PCA Workflow

Figure 3.26 Distribution of Fluids and Formation Solids

Figure 3.27 Sequential Gaussian Simulation Workflow

Figure 3.28 Single Value and Cumulative Percentage Eigenspectrum

Figure 3.29 Singular Spectrum Analysis Results

Figure 3.30 First and Second Spectral Group of the Amplitude Anomaly

Figure 3.31 Periodogram and Spectral Density of the Seismic Traces

Figure 3.32 Slope Component of the Amplitude Anomaly

Figure 3.33 Slope Component Distribution

Figure 3.34 Seismic Facies Workflow

Figure 3.35 Geologic Velocity Model and Seismic Response

Figure 3.36 Analysis Results Implementing the Proposed Methodology

Figure 4.1 Accelerated Uptake of Soft Computing Technical Papers

Figure 4.2 Exploration and Production Value Chains

Figure 4.3 Reservoir Characterization Cycle

Figure 4.4 Filtering the Decision Variable

Figure 4.5 Gamma Ray Variable Displayed in a Q-Q Plot

Figure 4.6 Q-Q Plots with Histograms and CDFs for Three Gamma Ray Logs

Figure 4.7 Artificial Neural Network

Figure 4.8 3D Scatterplot Surfacing Relationship among Porosity, Permeability, and the Objective Function Recovery Factor

Figure 4.9 Scatterplot Matrix Illustrating Degrees of Correlation with the Recovery Factor

Figure 4.10 Multivariate Correlations of Influencing Reservoir Parameters

Figure 4.11 Color Map Depicting Correlations and the Analysis from a PCA

Figure 4.12 Pairwise Correlations Report with a 3D Scatterplot

Figure 4.13 Outlier Analysis with Upper Control Limit (UCL) Defined

Figure 4.14 Distributions of Thickness and Porosity that Exhibit Predictive Powers for the Recovery Factor

Figure 4.15 Continuous Parameters with Fitted Estimates

Figure 4.16 Partition Tree Classification

Figure 5.1 Real-Time Drilling Engineering Methodology

Figure 5.2 Multivariant, Multidimensional, Multivariate, and Stochastic Drilling

Figure 5.3 Real-Time Drilling Methodology

Figure 5.4 Solution Architecture to Reduce NPT

Figure 5.5 NPT Catalog

Figure 5.6 Cluster Analysis to Identify Similar Classes by Tactics/Strategies and NPT

Figure 5.7 NPT Methodology

Figure 5.8 Differential Sticking: Ph (Hydrostatic Pressure) Exhibiting a Higher Value than Pf (Pore Pressure of a Permeable Formation)

Figure 5.9 Wellbore Instability Is Prone to a Stuck-Pipe Event, Leading to NPT

Figure 5.10 Study Results for Well Pair 1

Figure 5.11 Neural Network Models for Water and Oil for Well Pair 3

Figure 5.12 Association Rules Implemented on SAGD Dataset for Well Pair 1

Figure 5.13 Association Rules Implemented on SAGD Dataset for Well Pair 3

Figure 5.14 Optimal Control Variable Values for Well Pair 1

Figure 5.15 Optimal Control Variable Values for Well Pair 3

Figure 5.16 Box Plot for mdbit, mdhole, and hkldav

Figure 5.17 Complexity of a Hydraulic Fracture Completion Strategy

Figure 5.18 Examples of the Datasets Studied

Figure 5.19 Correlation Matrix

Figure 5.20 Oblique Clustering Results on the Indicators

Figure 6.1 Reservoir Management Cogs

Figure 6.2 Well and Reservoir Management Lifecycle

Figure 6.3 DOFFs Integrated Actions Threaded by Analytical Workflows

Figure 6.4 Analytical Centers of Excellence Suite of Workflows

Figure 6.5 Implementation Architecture

Figure 6.6 Correlation Injected Water Rate, Produced Oil Rate with Ostensible Lag

Figure 6.7 Decision Tree Model

Figure 6.8 Water Cut and Fracture Distribution

Figure 6.9 Extracting Hidden Knowledge

Figure 6.10 Data Sources and EDA for Study

Figure 6.11 Typical Water Cut Behaviors

Figure 6.12 Water Cut Normalization Step

Figure 6.13 Distance to Free Water Level Influences the Water Cut

Figure 6.14 How Similar Are These Two Scatterplots?

Figure 6.15 Illustrates the Data Transformation Step

Figure 6.16 Dissimilarity Matrix Computed Using Jaccard Metric

Figure 6.17 Dendrogram of Wells in Time Window 1

Figure 6.18 Average Water Cut with Color Intensity Yielding Observation Frequency

Figure 6.19 Well Configuration Defined as Horizontal or Vertical

Figure 6.20 Annual Average SWP for the Two Clusters in Time Window 1

Figure 6.21 Annual Average SWP for the Four Clusters in Time Window 2

Figure 6.22 Annual Average SWP for the Five Clusters in Time Window 3

Figure 6.23 Distribution of Known Fractures across the Carbonate Reservoir

Figure 6.24 Distribution of Formation Permeability (

k

) and Thickness (

h

) Product

Figure 6.25 Distribution of Known Fractures across the Clusters

Figure 6.26 Tabulated Results by Cluster and Temporal Windows

Figure 6.27 Tabulated Data by Time Window

Figure 6.28 Rock–Fluid Characterization for Reservoir Management

Figure 7.1 Process Diagram Implementing Key Components

Figure 7.2 A Modified Bootstrap Methodology and Exploratory Data Analysis

Figure 7.3 Bootstrapping Underpins Automated Time Series Selection

Figure 7.4 Type Curves Fit the Temporal Data

Figure 7.5 Monte Carlo Simulation

Figure 7.6 Cluster Analysis Results on a Scatterplot

Figure 7.7 Decision Tree Optimizes Cluster Analysis Workflow

Figure 7.8 Cluster Analysis Visualization

Figure 7.9 SEMMA Process That Underpins a Data Mining Workflow

Figure 7.10 Data Mining Methodology with DCA Output to Enhance Models

Figure 7.11 Well Optimization Workflows

Figure 8.1 Top-Down Intelligent Reservoir Modeling Workflow

Figure 8.2 Continuous Gas Lift (CGL)

Figure 8.3 Gas Lift Optimization Curve

Figure 8.4 Sample Gas Lift Curves Data

Figure 8.5 Sample Gas Lift Curves

Figure 8.6 Optimization Results and Optimal Gas Usage

Figure 8.7 Optimization Results for Gas Injection Rates from 0 to 100 MMCF/D

Figure 8.8 Correlations and Regressions in Bakken Proppant Parameters

Figure 8.9 Heat Map Detailing Cumulative Production by County

Figure 8.10 Bubble Plot Detailing Frac Fluid Volume and Proppant Quantity

Figure 8.11 Categories Output by the Neural Network

Figure 8.12 Relative Impact of Most Significant Parameters on Stage Performance

Figure 8.13 Study of the Petrophysical Parameters by Stage

Figure 8.14 Study of the Petrophysical Parameters by Category

Figure 8.15 Study of the Geological Parameters across the Anticline

Figure 8.16 Tree Map for the Stage Performance and Proppant Usage by Wellbore

Figure 8.17 Tree Map for Individual Stage Performance and Proppant in Wellbore 10

Figure 8.18 Hierarchical Clustering of Wells and Associated Attributes

Figure 9.1 Histograms Depicting Dynamic Relationships between Gas Production, Wellbore, Oil Production Rate, and Oil Production Volume

Figure 9.2 Outlier Detection Implementing a Box Plot

Figure 9.3 Box and Whiskers for Descriptive Statistics of Operational Parameters

Figure 9.4 3D Scatterplot Surfacing Relationship between Porosity, Permeability, and Water Saturation

Figure 9.5 Contour Profiler Observing the Recovery Factor against OOIP and Log of Water Saturation

Figure 9.6 Box and Whiskers

Figure 9.7 Histograms

Figure 9.8 3D Scatterplot

Figure 9.9 Heat Map

Figure 9.10 Bubble Plot

Figure 9.11 Tree Map Defining Cumulative Gas Production for Each Wellbore Stage

Figure 9.12 Tabular Format for Depicting Production Data

Figure 9.13 Tree Maps Explain Production of Hydrocarbons and Water by GOSP

Figure 9.14 Suite of Scatterplots with Correlation and Regression Insights

Figure 9.15 Heat Map Enables Identification of Sweet-Spot for Perforation

Figure 9.16 Box-Whiskers Chart Detailing Twenty-Fifth and Seventy-Fifth Percentiles

Figure 9.17 Animated Bubble Plot Offers Insight across a Temporal Slice of the Data

Figure 9.18 Bubble Plot Can Drill into a Hierarchy to Identify Well Performance

Figure 9.19 Animated Plot Investigating Proppant Quantities against Performance

Figure 9.20 Bubble Plot Mapped across the Well Locations

Figure 9.21 Crosstab Display Detailing Specific Measures

Figure 9.22 Data Stream Flows Generated by Sensors

Figure 9.23 Event Stream Processing Engine and Associated Data Flows

Figure 9.24 Workflows to Implement Predictive Models

Figure 10.1 Hybrid System Integrating a Data-Driven and Expert Workflow

Figure 10.2 Bayesian Belief Network

Figure 10.3 Self-Organizing Maps

Figure 10.4 Oil and Gas Production Flowpath

Figure 10.5 Actual and Theoretical Efficiency

Figure 10.6 Content Categorization Step

Figure 10.7 Stemming Process to Identify Roots of Multiple Spellings of a Word

Figure 10.8 Term Associations Implemented to Determine Root Causes for Issues

Figure 10.9 Drilling into the Failure Categories

Figure 10.10 Drilling into Subcategories for Mechanical Failures

Figure 10.11 Correlation Matrix Surfaces Relationships from a Bivariate Perspective

Figure 10.12 Forecasting Capability on Unstructured Data

Figure 10.13 Text Analytical Workflow

Figure 10.14 Unstructured Data Analytical Process Workflow

Figure 10.15 Big Data Workflows

Figure 10.16 Operationalizing a Predictive Model against Real-Time Data

Figure 10.17 Scoring ANN, FL, and GA

Figure 10.18 Neural Structure of Neuro-Fuzzy System

Figure 10.19 Log Data Projected onto the First Three Principal Components

Figure 10.20 3D Visualization of a Scatterplot Reflecting Different Planes

List of Tables

Table 3.1 Curvature Attributes

Table 3.2 Rock Solid Attributes

Table 5.1 Standard Data Input Parameters

Table 5.2 Feature-Ranking Algorithms

Table 6.1 Distribution of Wellbore Type Considered in the Study

Table 7.1 Well Analysis for Additive and Multiplicative Methods

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Guide

Cover

Table of Contents

Begin Reading

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