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Natural gas and petroleum : production strategies, environmental implications, and future challenges /
Today's oil and gas are at record prices, whilst global energy demand is increasing from population and economic development pressures. New and highly improved methods to detect, explore, and exploit new resources of oil and gas are necessary to maintain the world's energy needs toward sus...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Nova Publishers,
[2013]
|
| Colección: | Energy science, engineering and technology series.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- NATURAL GAS AND PETROLEUM. PRODUCTION STRATEGIES, ENVIRONMENTAL IMPLICATIONS AND FUTURE CHALLENGES; Library of Congress Cataloging-in-Publication Data; Contents; Preface; Chapter 1: Insights from Laboratory Studies of Hydraulic Fracturing; Abstract; Introduction; Acoustic Emission Studies; Conclusion; References; Chapter 2: Intelligent Approaches in the Upstream Oil and Gas Industry; Abstract; Introduction; Methods; 1. Artificial Neural Networks (ANN); 1.1. Introduction to ANN; 1.2. Artificial Neural Networks
- Background; 1.3. Artificial Neural Network
- A Three-Step Technology.
- Step 1. Supervised training of the neural network by using a training wellStep 2. Confirmation and validation of the model; Step 3. Application of the model; 1.4. Predicting the Presence of Overpressured Zones in the Anadarko Basin, Oklahoma; 1.5. Conclusion about Using ANN; 2. Gene Expression Programming (GEP); 2.1. Introduction; 2.2. Problem Description and Related Work; 2.3. Symbolic Regression for Compressional Sonic Log (DT) Estimation; 2.4. Gene Expression Programming (GEP)
- Concept, Terms, and General Flow Chart; 2.4.1. Representation; 2.4.2. Genetic Operators; 2.4.3. Selection.
- 2.4.4. Fitness Function2.4.5. The General GEP Algorithm; 2.5. Experiments; A Case Study: The Anadarko Basin, Oklahoma; Step 1. Model Training; Step 2. Model Verification; Step 3. Model Application; 2.6. Conclusion about Using GEP; 3. Support Vector Regression; 3.1. Support Vector Regression (SVR)
- Problem Description and Related Work; 3.1.1. The Dual Formulation; 3.1.2. Extension to the Nonlinear Case; 3.1.3. Parameters; 3.2. Experiments; 3.2.1. Evaluation Measures; 3.2.2. Training, Validation, Testing, and Application; 3.2.3. Data Sets and Features; 3.2.4. Data Normalization.
- 3.2.5. Training Set Selection3.2.6. Parameter Selection; 3.2.7. Predictors Selection; 3.2.7.1. Analysis of the Most Common Well Logging Parameters: GR, REID, SP; 3.2.7.2. Analysis of Other Well Logging Parameters: DEN and PE; 3.2.8. Application of the Proposed Method; 3.3. Estimating the Presence of Overpressured Zones in the Anadarko Basin Based on Sonic Logs; 3.4. Conclusion about Using SVR; References; Chapter 3: Reservoir and Non-Reservoir Rocks from the Anadarko Basin, Oklahoma: A Petrophysical Outline; Abstract; Introduction; Methods; Petrophysical Parameters; Sample Lithology.
- Results and DiscussionReservoir vs. Non-Reservoir Rocks; Relationships between Various Petrophysical Parameters and Lithology; Porosity Relationships; Conclusion; Acknowledgments; References; Chapter 4: Current Situation of Natural Gas and Petroleum; Abstract; Nomenclature; Introduction; 1. Natural Gas; 2. Reserves; 2.1. Production and Consumption; 2.2. Projections; 2.3. Natural Gas and the Environment; 3. Petroleum; 3.1. Reserves; 3.2. Heavy Oil; 3.3. Production and Consumption; 3.4. Projections; 3.5. Petroleum and the Environment; Conclusion; References.