Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models.

Cover -- Title Page -- Copyright -- Contents -- Foreword -- Preface -- Acknowledgments -- Chapter 1: Introduction to Data-Driven Concepts -- Introduction -- Current Approaches -- Is There a Crisis in Geophysical and Petrophysical Analysis? -- Applying an Analytical Approach -- What Are Analytics and Data Science? -- Meanwhile, Back in the Oil Industry -- How Do I Do Analytics and Data Science? -- What Are the Constituent Parts of an Upstream Data Science Team? -- A Data-Driven Study Timeline -- What Is Data Engineering? -- A Workflow for Getting Started -- Is It Induction or Deduction? -- References -- Chapter 2: Data-Driven Analytical Methods Used in E&amp -- P -- Introduction -- Spatial Datasets -- Temporal Datasets -- Soft Computing Techniques -- Data Mining Nomenclature -- Decision Trees -- Rules-Based Methods -- Regression -- Classification Tasks -- Ensemble Methodology -- Partial Least Squares -- Traditional Neural Networks: The Details -- Simple Neural Networks -- Random Forests -- Gradient Boosting -- Gradient Descent -- Factorized Machine Learning -- Evolutionary Computing and Genetic Algorithms -- Artificial Intelligence: Machine and Deep Learning -- References -- Chapter 3: Advanced Geophysical and Petrophysical Methodologies -- Introduction -- Advanced Geophysical Methodologies -- How Many Clusters? -- Case Study: North Sea Mature Reservoir Synopsis -- Case Study: Working with Passive Seismic Data -- Advanced Petrophysical Methodologies -- Well Logging and Petrophysical Data Types -- Data Collection and Data Quality -- What Does Well Logging Data Tell Us? -- Stratigraphic Information -- Integration with Stratigraphic Data -- Extracting Useful Information from Well Reports -- Integration with Other Well Information -- Integration with Other Technical Domains at the Well Level -- Fundamental Insights -- Feature Engineering in Well Logs.

Toward Machine Learning -- Use Cases -- Concluding Remarks -- References -- Chapter 4: Continuous Monitoring -- Introduction -- Continuous Monitoring in the Reservoir -- Machine Learning Techniques for Temporal Data -- Spatiotemporal Perspectives -- Time Series Analysis -- Advanced Time Series Prediction -- Production Gap Analysis -- Digital Signal Processing Theory -- Hydraulic Fracture Monitoring and Mapping -- Completions Evaluation -- Reservoir Monitoring: Real-Time Data Quality -- Distributed Acoustic Sensing -- Distributed Temperature Sensing -- Case Study: Time Series to Optimize Hydraulic Fracture Strategy -- Reservoir Characterization and Tukey Diagrams -- References -- Chapter 5: Seismic Reservoir Characterization -- Introduction -- Seismic Reservoir Characterization: Key Parameters -- Principal Component Analysis -- Self-Organizing Maps -- Modular Artificial Neural Networks -- Wavelet Analysis -- Wavelet Scalograms -- Spectral Decomposition -- First Arrivals -- Noise Suppression -- References -- Chapter 6: Seismic Attribute Analysis -- Introduction -- Types of Seismic Attributes -- Seismic Attribute Workflows -- SEMMA Process -- Seismic Facies Classification -- Seismic Facies Dataset -- Seismic Facies Study: Preprocessing -- Hierarchical Clustering -- k-means Clustering -- Self-Organizing Maps (SOMs) -- Normal Mixtures -- Latent Class Analysis -- Principal Component Analysis (PCA) -- Statistical Assessment -- References -- Chapter 7: Geostatistics: Integrating Seismic and Petrophysical Data -- Introduction -- Data Description -- Interpretation -- Estimation -- The Covariance and the Variogram -- Case Study: Spatially Predicted Model of Anisotropic Permeability -- What Is Anisotropy? -- Analysis with Surface Trend Removal -- Kriging and Co-kriging -- Geostatistical Inversion -- Geophysical Attribute: Acoustic Impedance.

Petrophysical Properties: Density and Lithology -- Knowledge Synthesis: Bayesian Maximum Entropy (BME) -- References -- Chapter 8: Artificial Intelligence: Machine and Deep Learning -- Introduction -- Data Management -- Machine Learning Methodologies -- Supervised Learning -- Unsupervised Learning -- Semi-Supervised Learning -- Deep Learning Techniques -- Semi-Supervised Learning -- Supervised Learning -- Unsupervised Learning -- Deep Neural Network Architectures -- Deep Forward Neural Network -- Convolutional Deep Neural Network -- Recurrent Deep Neural Network -- Stacked Denoising Autoencoder -- Seismic Feature Identification Workflow -- Efficient Pattern Recognition Approach -- Methods and Technologies: Decomposing Images into Patches -- Representing Patches with a Dictionary -- Stacked Autoencoder -- References -- Chapter 9: Case Studies: Deep Learning in E&amp -- P -- Introduction -- Reservoir Characterization -- Case Study: Seismic Profile Analysis -- Supervised and Unsupervised Experiments -- Unsupervised Results -- Case Study: Estimated Ultimate Recovery -- Deep Learning for Time Series Modeling -- Scaling Issues with Large Datasets -- Conclusions -- Case Study: Deep Learning Applied to Well Data -- Introduction -- Restricted Boltzmann Machines -- Mathematics -- Case Study: Geophysical Feature Extraction: Deep Neural Networks -- CDNN Layer Development -- Case Study: Well Log Data-Driven Evaluation for Petrophysical Insights -- Case Study: Functional Data Analysis in Reservoir Management -- References -- Glossary -- About the Authors -- Index -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.