Harness Big Data with Analytics

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Opis: Harness Big Data with Analytics - Keith Holdaway

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.Preface xi Chapter 1 Fundamentals of Soft Computing 1 Current Landscape in Upstream Data Analysis 2 Evolution from Plato to Aristotle 9 Descriptive and Predictive Models 10 The SEMMA Process 13 High-Performance Analytics 14 Three Tenets of Upstream Data 18 Exploration and Production Value Propositions 20 Oilfield Analytics 22 I am a... 27 Notes 31 Chapter 2 Data Management 33 Exploration and Production Value Proposition 34 Data Management Platform 36 Array of Data Repositories 45 Structured Data and Unstructured Data 49 Extraction, Transformation, and Loading Processes 50 Big Data Big Analytics 52 Standard Data Sources 54 Case Study: Production Data Quality Control Framework 55 Best Practices 57 Notes 62 Chapter 3 Seismic Attribute Analysis 63 Exploration and Production Value Propositions 63 Time-Lapse Seismic Exploration 64 Seismic Attributes 65 Reservoir Characterization 68 Reservoir Management 69 Seismic Trace Analysis 69 Case Study: Reservoir Properties Defined by Seismic Attributes 90 Notes 106 Chapter 4 Reservoir Characterization and Simulation 107 Exploration and Production Value Propositions 108 Exploratory Data Analysis 111 Reservoir Characterization Cycle 114 Traditional Data Analysis 114 Reservoir Simulation Models 116 Case Studies 122 Notes 138 Chapter 5 Drilling and Completion Optimization 139 Exploration and Production Value Propositions 140 Workflow One: Mitigation of Nonproductive Time 142 Workflow Two: Drilling Parameter Optimization 151 Case Studies 154 Notes 173 Chapter 6 Reservoir Management 175 Exploration and Production Value Propositions 177 Digital Oilfield of the Future 179 Analytical Center of Excellence 185 Analytical Workflows: Best Practices 188 Case Studies 192 Notes 212 Chapter 7 Production Forecasting 213 Exploration and Production Value Propositions 214 Web-Based Decline Curve Analysis Solution 216 Unconventional Reserves Estimation 235 Case Study: Oil Production Prediction for Infill Well 237 Notes 242 Chapter 8 Production Optimization 243 Exploration and Production Value Propositions 245 Case Studies 246 Notes 273 Chapter 9 Exploratory and Predictive Data Analysis 275 Exploration and Production Value Propositions 276 EDA Components 278 EDA Statistical Graphs and Plots 284 Ensemble Segmentations 290 Data Visualization 292 Case Studies 296 Notes 308 Chapter 10 Big Data: Structured and Unstructured 309 Exploration and Production Value Propositions 312 Hybrid Expert and Data-Driven System 315 Case Studies 321 Multivariate Geostatistics 330 Big Data Workflows 332 Integration of Soft Computing Techniques 336 Notes 341 Glossary 343 About the Author 349 Index 351


Szczegóły: Harness Big Data with Analytics - Keith Holdaway

Tytuł: Harness Big Data with Analytics
Autor: Keith Holdaway
Producent: John Wiley
ISBN: 9781118779316
Rok produkcji: 2014
Ilość stron: 384
Oprawa: Twarda
Waga: 0.87 kg


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Harness Big Data with Analytics

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.Preface xi Chapter 1 Fundamentals of Soft Computing 1 Current Landscape in Upstream Data Analysis 2 Evolution from Plato to Aristotle 9 Descriptive and Predictive Models 10 The SEMMA Process 13 High-Performance Analytics 14 Three Tenets of Upstream Data 18 Exploration and Production Value Propositions 20 Oilfield Analytics 22 I am a... 27 Notes 31 Chapter 2 Data Management 33 Exploration and Production Value Proposition 34 Data Management Platform 36 Array of Data Repositories 45 Structured Data and Unstructured Data 49 Extraction, Transformation, and Loading Processes 50 Big Data Big Analytics 52 Standard Data Sources 54 Case Study: Production Data Quality Control Framework 55 Best Practices 57 Notes 62 Chapter 3 Seismic Attribute Analysis 63 Exploration and Production Value Propositions 63 Time-Lapse Seismic Exploration 64 Seismic Attributes 65 Reservoir Characterization 68 Reservoir Management 69 Seismic Trace Analysis 69 Case Study: Reservoir Properties Defined by Seismic Attributes 90 Notes 106 Chapter 4 Reservoir Characterization and Simulation 107 Exploration and Production Value Propositions 108 Exploratory Data Analysis 111 Reservoir Characterization Cycle 114 Traditional Data Analysis 114 Reservoir Simulation Models 116 Case Studies 122 Notes 138 Chapter 5 Drilling and Completion Optimization 139 Exploration and Production Value Propositions 140 Workflow One: Mitigation of Nonproductive Time 142 Workflow Two: Drilling Parameter Optimization 151 Case Studies 154 Notes 173 Chapter 6 Reservoir Management 175 Exploration and Production Value Propositions 177 Digital Oilfield of the Future 179 Analytical Center of Excellence 185 Analytical Workflows: Best Practices 188 Case Studies 192 Notes 212 Chapter 7 Production Forecasting 213 Exploration and Production Value Propositions 214 Web-Based Decline Curve Analysis Solution 216 Unconventional Reserves Estimation 235 Case Study: Oil Production Prediction for Infill Well 237 Notes 242 Chapter 8 Production Optimization 243 Exploration and Production Value Propositions 245 Case Studies 246 Notes 273 Chapter 9 Exploratory and Predictive Data Analysis 275 Exploration and Production Value Propositions 276 EDA Components 278 EDA Statistical Graphs and Plots 284 Ensemble Segmentations 290 Data Visualization 292 Case Studies 296 Notes 308 Chapter 10 Big Data: Structured and Unstructured 309 Exploration and Production Value Propositions 312 Hybrid Expert and Data-Driven System 315 Case Studies 321 Multivariate Geostatistics 330 Big Data Workflows 332 Integration of Soft Computing Techniques 336 Notes 341 Glossary 343 About the Author 349 Index 351

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Cena 274,00 PLN
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