{"product_id":"enhance-oil-and-gas-exploration-with-data-driven-geophysical-and-petrophysical-models-isbn-9781119215103","title":"Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models","description":"\u003cb\u003eLeverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data\u003c\/b\u003e \u003cp\u003e\u003ci\u003eEnhance Oil \u0026amp; Gas Exploration with Data-Driven Geophysical and Petrophysical Models\u003c\/i\u003e demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. \u003c\/p\u003e\u003cp\u003eRecent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eApply data-driven modeling concepts in a geophysical and petrophysical context\u003c\/li\u003e \u003cli\u003eLearn how to get more information out of models and simulations\u003c\/li\u003e \u003cli\u003eAdd value to everyday tasks with the appropriate Big Data application\u003c\/li\u003e \u003cli\u003eAdjust methodology to suit diverse geophysical and petrophysical contexts\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eData-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. \u003ci\u003eEnhance Oil \u0026amp; Gas Exploration with Data-Driven Geophysical and Petrophysical Models\u003c\/i\u003e takes you beyond traditional deterministic interpretation to the future of exploration data analysis. \u003c\/p\u003e\u003cp\u003eForeword xv\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eAcknowledgments xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction to Data-Driven Concepts 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 2\u003c\/p\u003e \u003cp\u003eCurrent Approaches 2\u003c\/p\u003e \u003cp\u003eIs There a Crisis in Geophysical and Petrophysical Analysis? 3\u003c\/p\u003e \u003cp\u003eApplying an Analytical Approach 4\u003c\/p\u003e \u003cp\u003eWhat Are Analytics and Data Science? 5\u003c\/p\u003e \u003cp\u003eMeanwhile, Back in the Oil Industry 8\u003c\/p\u003e \u003cp\u003eHow Do I Do Analytics and Data Science? 10\u003c\/p\u003e \u003cp\u003eWhat Are the Constituent Parts of an Upstream Data Science Team? 13\u003c\/p\u003e \u003cp\u003eA Data-Driven Study Timeline 15\u003c\/p\u003e \u003cp\u003eWhat Is Data Engineering? 18\u003c\/p\u003e \u003cp\u003eA Workflow for Getting Started 19\u003c\/p\u003e \u003cp\u003eIs It Induction or Deduction? 30\u003c\/p\u003e \u003cp\u003eReferences 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Data-Driven Analytical Methods Used in E\u0026amp;P 34\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 35\u003c\/p\u003e \u003cp\u003eSpatial Datasets 36\u003c\/p\u003e \u003cp\u003eTemporal Datasets 37\u003c\/p\u003e \u003cp\u003eSoft Computing Techniques 39\u003c\/p\u003e \u003cp\u003eData Mining Nomenclature 40\u003c\/p\u003e \u003cp\u003eDecision Trees 43\u003c\/p\u003e \u003cp\u003eRules-Based Methods 44\u003c\/p\u003e \u003cp\u003eRegression 45\u003c\/p\u003e \u003cp\u003eClassification Tasks 45\u003c\/p\u003e \u003cp\u003eEnsemble Methodology 48\u003c\/p\u003e \u003cp\u003ePartial Least Squares 50\u003c\/p\u003e \u003cp\u003eTraditional Neural Networks: The Details 51\u003c\/p\u003e \u003cp\u003eSimple Neural Networks 54\u003c\/p\u003e \u003cp\u003eRandom Forests 59\u003c\/p\u003e \u003cp\u003eGradient Boosting 60\u003c\/p\u003e \u003cp\u003eGradient Descent 60\u003c\/p\u003e \u003cp\u003eFactorized Machine Learning 62\u003c\/p\u003e \u003cp\u003eEvolutionary Computing and Genetic Algorithms 62\u003c\/p\u003e \u003cp\u003eArtificial Intelligence: Machine and Deep Learning 64\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Advanced Geophysical and Petrophysical Methodologies 68\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 69\u003c\/p\u003e \u003cp\u003eAdvanced Geophysical Methodologies 69\u003c\/p\u003e \u003cp\u003eHow Many Clusters? 70\u003c\/p\u003e \u003cp\u003eCase Study: North Sea Mature Reservoir Synopsis 72\u003c\/p\u003e \u003cp\u003eCase Study: Working with Passive Seismic Data 74\u003c\/p\u003e \u003cp\u003eAdvanced Petrophysical Methodologies 78\u003c\/p\u003e \u003cp\u003eWell Logging and Petrophysical Data Types 78\u003c\/p\u003e \u003cp\u003eData Collection and Data Quality 82\u003c\/p\u003e \u003cp\u003eWhat Does Well Logging Data Tell Us? 84\u003c\/p\u003e \u003cp\u003eStratigraphic Information 86\u003c\/p\u003e \u003cp\u003eIntegration with Stratigraphic Data 87\u003c\/p\u003e \u003cp\u003eExtracting Useful Information from Well Reports 89\u003c\/p\u003e \u003cp\u003eIntegration with Other Well Information 90\u003c\/p\u003e \u003cp\u003eIntegration with Other Technical Domains at the Well Level 90\u003c\/p\u003e \u003cp\u003eFundamental Insights 92\u003c\/p\u003e \u003cp\u003eFeature Engineering in Well Logs 95\u003c\/p\u003e \u003cp\u003eToward Machine Learning 98\u003c\/p\u003e \u003cp\u003eUse Cases 98\u003c\/p\u003e \u003cp\u003eConcluding Remarks 99\u003c\/p\u003e \u003cp\u003eReferences 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Continuous Monitoring 102\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 103\u003c\/p\u003e \u003cp\u003eContinuous Monitoring in the Reservoir 104\u003c\/p\u003e \u003cp\u003eMachine Learning Techniques for Temporal Data 105\u003c\/p\u003e \u003cp\u003eSpatiotemporal Perspectives 106\u003c\/p\u003e \u003cp\u003eTime Series Analysis 107\u003c\/p\u003e \u003cp\u003eAdvanced Time Series Prediction 108\u003c\/p\u003e \u003cp\u003eProduction Gap Analysis 112\u003c\/p\u003e \u003cp\u003eDigital Signal Processing Theory 117\u003c\/p\u003e \u003cp\u003eHydraulic Fracture Monitoring and Mapping 117\u003c\/p\u003e \u003cp\u003eCompletions Evaluation 118\u003c\/p\u003e \u003cp\u003eReservoir Monitoring: Real-Time Data Quality 119\u003c\/p\u003e \u003cp\u003eDistributed Acoustic Sensing 122\u003c\/p\u003e \u003cp\u003eDistributed Temperature Sensing 123\u003c\/p\u003e \u003cp\u003eCase Study: Time Series to Optimize Hydraulic Fracture Strategy 129\u003c\/p\u003e \u003cp\u003eReservoir Characterization and Tukey Diagrams 131\u003c\/p\u003e \u003cp\u003eReferences 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Seismic Reservoir Characterization 140\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 141\u003c\/p\u003e \u003cp\u003eSeismic Reservoir Characterization: Key Parameters 141\u003c\/p\u003e \u003cp\u003ePrincipal Component Analysis 146\u003c\/p\u003e \u003cp\u003eSelf-Organizing Maps 146\u003c\/p\u003e \u003cp\u003eModular Artificial Neural Networks 147\u003c\/p\u003e \u003cp\u003eWavelet Analysis 148\u003c\/p\u003e \u003cp\u003eWavelet Scalograms 157\u003c\/p\u003e \u003cp\u003eSpectral Decomposition 159\u003c\/p\u003e \u003cp\u003eFirst Arrivals 160\u003c\/p\u003e \u003cp\u003eNoise Suppression 161\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Seismic Attribute Analysis 174\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 175\u003c\/p\u003e \u003cp\u003eTypes of Seismic Attributes 176\u003c\/p\u003e \u003cp\u003eSeismic Attribute Workflows 180\u003c\/p\u003e \u003cp\u003eSEMMA Process 181\u003c\/p\u003e \u003cp\u003eSeismic Facies Classification 183\u003c\/p\u003e \u003cp\u003eSeismic Facies Dataset 188\u003c\/p\u003e \u003cp\u003eSeismic Facies Study: Preprocessing 189\u003c\/p\u003e \u003cp\u003eHierarchical Clustering 190\u003c\/p\u003e \u003cp\u003ek-means Clustering 193\u003c\/p\u003e \u003cp\u003eSelf-Organizing Maps (SOMs) 194\u003c\/p\u003e \u003cp\u003eNormal Mixtures 195\u003c\/p\u003e \u003cp\u003eLatent Class Analysis 196\u003c\/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA) 198\u003c\/p\u003e \u003cp\u003eStatistical Assessment 200\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Geostatistics: Integrating Seismic and Petrophysical Data 206\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 207\u003c\/p\u003e \u003cp\u003eData Description 208\u003c\/p\u003e \u003cp\u003eInterpretation 210\u003c\/p\u003e \u003cp\u003eEstimation 210\u003c\/p\u003e \u003cp\u003eThe Covariance and the Variogram 211\u003c\/p\u003e \u003cp\u003eCase Study: Spatially Predicted Model of Anisotropic Permeability 214\u003c\/p\u003e \u003cp\u003eWhat Is Anisotropy? 214\u003c\/p\u003e \u003cp\u003eAnalysis with Surface Trend Removal 215\u003c\/p\u003e \u003cp\u003eKriging and Co-kriging 224\u003c\/p\u003e \u003cp\u003eGeostatistical Inversion 229\u003c\/p\u003e \u003cp\u003eGeophysical Attribute: Acoustic Impedance 230\u003c\/p\u003e \u003cp\u003ePetrophysical Properties: Density and Lithology 230\u003c\/p\u003e \u003cp\u003eKnowledge Synthesis: Bayesian Maximum Entropy (BME) 231\u003c\/p\u003e \u003cp\u003eReferences 237\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Artificial Intelligence: Machine and Deep Learning 240\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 241\u003c\/p\u003e \u003cp\u003eData Management 243\u003c\/p\u003e \u003cp\u003eMachine Learning Methodologies 243\u003c\/p\u003e \u003cp\u003eSupervised Learning 244\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 245\u003c\/p\u003e \u003cp\u003eSemi-Supervised Learning 245\u003c\/p\u003e \u003cp\u003eDeep Learning Techniques 247\u003c\/p\u003e \u003cp\u003eSemi-Supervised Learning 249\u003c\/p\u003e \u003cp\u003eSupervised Learning 250\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 250\u003c\/p\u003e \u003cp\u003eDeep Neural Network Architectures 251\u003c\/p\u003e \u003cp\u003eDeep Forward Neural Network 251\u003c\/p\u003e \u003cp\u003eConvolutional Deep Neural Network 253\u003c\/p\u003e \u003cp\u003eRecurrent Deep Neural Network 260\u003c\/p\u003e \u003cp\u003eStacked Denoising Autoencoder 262\u003c\/p\u003e \u003cp\u003eSeismic Feature Identification Workflow 268\u003c\/p\u003e \u003cp\u003eEfficient Pattern Recognition Approach 268\u003c\/p\u003e \u003cp\u003eMethods and Technologies: Decomposing Images into Patches 270\u003c\/p\u003e \u003cp\u003eRepresenting Patches with a Dictionary 271\u003c\/p\u003e \u003cp\u003eStacked Autoencoder 272\u003c\/p\u003e \u003cp\u003eReferences 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Case Studies: Deep Learning in E\u0026amp;P 276\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 277\u003c\/p\u003e \u003cp\u003eReservoir Characterization 277\u003c\/p\u003e \u003cp\u003eCase Study: Seismic Profile Analysis 280\u003c\/p\u003e \u003cp\u003eSupervised and Unsupervised Experiments 280\u003c\/p\u003e \u003cp\u003eUnsupervised Results 282\u003c\/p\u003e \u003cp\u003eCase Study: Estimated Ultimate Recovery 288\u003c\/p\u003e \u003cp\u003eDeep Learning for Time Series Modeling 289\u003c\/p\u003e \u003cp\u003eScaling Issues with Large Datasets 292\u003c\/p\u003e \u003cp\u003eConclusions 292\u003c\/p\u003e \u003cp\u003eCase Study: Deep Learning Applied to Well Data 293\u003c\/p\u003e \u003cp\u003eIntroduction 293\u003c\/p\u003e \u003cp\u003eRestricted Boltzmann Machines 294\u003c\/p\u003e \u003cp\u003eMathematics 297\u003c\/p\u003e \u003cp\u003eCase Study: Geophysical Feature Extraction: Deep Neural Networks 298\u003c\/p\u003e \u003cp\u003eCDNN Layer Development 299\u003c\/p\u003e \u003cp\u003eCase Study: Well Log Data-Driven Evaluation for Petrophysical Insights 302\u003c\/p\u003e \u003cp\u003eCase Study: Functional Data Analysis in Reservoir Management 306\u003c\/p\u003e \u003cp\u003eReferences 312\u003c\/p\u003e \u003cp\u003eGlossary 314\u003c\/p\u003e \u003cp\u003eAbout the Authors 320\u003c\/p\u003e \u003cp\u003eIndex 323\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eKEITH R. HOLDAWAY\u003c\/b\u003e is advisory industry consultant and principal solutions architect at SAS. He holds seven patents and is the author of \u003ci\u003eHarness Oil and Gas Big Data with Analytics\u003c\/i\u003e.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDUNCAN H. B. IRVING\u003c\/b\u003e is a practice partner for oil and gas consulting at Teradata. He publishes regularly on big data analytics applied to the upstream domain\u003ci\u003e.\u003c\/i\u003e     \u003c\/p\u003e\u003cp\u003eThe established system of oil and natural gas exploration and production (E\u0026amp;P) is unfit to provide the quality answers today's big data can provide. The voluminous datasets across spatial and temporal variability can't possibly all be considered at once to see probabilistic patterns—without computational power. If you've ever felt limited by traditional seismic interpretation methods, \u003ci\u003eEnhance Oil \u0026amp; Gas Exploration with Data-Driven Geophysical and Petrophysical Models\u003c\/i\u003e is your authoritative solution to integrating big data into your E\u0026amp;P strategies in ways that generate real business impact.  \u003c\/p\u003e\u003cp\u003eBig data is a powerful tool for breaking down business silos in order to gain unprecedented insight into a variety of business functions, and petrophysics and geophysics are the two siloed engineering groups in the oil and gas industry with highly profitable potential when brought together in data-driven workflows. This groundbreaking guide lays out a clear road map to enabling seismic, subsurface, and reservoir datasets to speak for themselves to illuminate answers unattainable with conventional models.  \u003c\/p\u003e\u003cp\u003eThe highly qualified authors, both geophysicists, specifically wrote this comprehensive volume to shed light on the many profitable benefits of using a data-driven approach to E\u0026amp;P and give practitioners a dependable methodology for getting there. Whether you're reading it cover to cover or using it as a quick reference in the field, the convenient format logically takes you from acquiring insight, applying context, and adding value to everyday tasks using data-driven strategies. Along the way you'll gain:  \u003c\/p\u003e\u003cul\u003e \u003cli\u003eAn in-depth look at best practices for applying geostatistics to geophysics and petrophysics, complete with informative examples illustrating how solutions work in the real world\u003c\/li\u003e \u003cli\u003eA deep understanding of the specific workflows essential to real-time knowledge for informed business decisions\u003c\/li\u003e \u003cli\u003eLeading-edge knowledge on how artificial intelligence is solving critical E\u0026amp;P challenges with data analysis\u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003eBring your E\u0026amp;P up to the cutting-edge of science with the proven data-driven workflows in \u003ci\u003eEnhance Oil \u0026amp; Gas Exploration with Data-Driven Geophysical and Petrophysical Models.\u003c\/i\u003e     \u003c\/p\u003e\u003cp\u003e\u003cb\u003eUNEARTH A RICHER VALUE FROM GEOPHYSICAL AND PETROPHYSICAL EXPLORATION DATA\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003eThe disruptive effects of big data have reached the oil and gas industry and resulted in extraordinary advancements. \u003ci\u003eEnhance Oil \u0026amp; Gas Exploration with Data-Driven Geophysical and Petrophysical Models\u003c\/i\u003e is your practical guidebook to getting the most out of continually maturing computational intelligence in order to give you the highest quality, real-time information to make the right business decisions. \u003c\/p\u003e\u003cp\u003eWritten by two thought leaders in the field, this collection of easy-to-follow discussions offers a relevant depth of coverage and clarifying case studies to fully prepare you to use this dependable probabilistic model in the real world to:  \u003c\/p\u003e\u003cul\u003e \u003cli\u003eAdd greater value to your exploration data with new tools and methodologies for advanced geophysical and petrophysical data analysis\u003c\/li\u003e \u003cli\u003eBreak down business silos to streamline processes, produce more accurate determinations, and strip away the negative side effects of data     compartmentalization\u003c\/li\u003e \u003cli\u003eBlend traditional deterministic interpretation with data-driven deep learning and data mining to overcome a diverse variety of business challenges\u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003eLet your data tell you where profits lie with \u003ci\u003eEnhance Oil \u0026amp; Gas Exploration with Data-Driven Geophysical and Petrophysical Models.\u003c\/i\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989139865829,"sku":"NP9781119215103","price":95.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119215103.jpg?v=1761782959","url":"https:\/\/k12savings.com\/es\/products\/enhance-oil-and-gas-exploration-with-data-driven-geophysical-and-petrophysical-models-isbn-9781119215103","provider":"K12savings","version":"1.0","type":"link"}