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

<p><b>KEITH R. HOLDAWAY</b> is Principal Industry Consultant and Principal Solutions Architect at SAS, where he helps drive implementation of innovative oil and gas solutions and products. He also develops business opportunities for the SAS global oil and gas business unit that align SAS advanced analytics from Exploratory Data Analysis and predictive models to subsurface reservoir characterization and drilling/production optimization in conventional and unconventional fields. Prior to joining SAS, Holdaway was a senior geophysicist with Shell Oil, where he conducted seismic processing and interpretation and determined seismic attributes in 3D cubes for soft computing statistical data mining.

<p>Uncertainty is the enemy of oil and gas exploration and production—and while it will never be eliminated entirely from these processes, big data analytics can go a long way to mitigating risks and revealing efficiencies that can make energy production more reliable and profitable. With <i>Harness Oil and Gas Big Data with Analytics</i>, expert author Keith Holdaway offers the most comprehensive compendium of advanced analytics methodologies that oil and gas engineers can apply to their data. <p>This complete resource 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. The book delves into the three major issues that face the oil and gas industry during the exploration and production stages: <ul> <li>Data management, including a discussion of the challenge of storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data</li> <li>Quantification of uncertainty, featuring an in-depth review of the statistical and data analytics methods for making predictions and determining the relative certainty of those predictions</li> <li>Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly assess and prepare for unknown risks</li> </ul> <p>Intended as a hands-on guide, this resource provides a complete overview of upstream data analysis and data management and then dives into details specific to the industry, addressing such topics as seismic attribute analysis, drilling and completion optimization, reservoir management, production optimization, and exploratory and predictive data analysis. Covering the major issues facing the oil and gas industry in the exploration and production stages, this groundbreaking work reveals how to model big data to realize efficiencies and business benefits in the oil and gas exploration and production phases like never before.

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