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Soft Computing for Reservoir Characterization and Modeling 1st Edition by PM Wong, F Aminzadeh, M Nikravesh ISBN 379082495X 9783790824957

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Soft Computing for Reservoir Characterization and Modeling 1st Edition P. M. Wong Digital Instant Download

Author(s): P. M. Wong, F. Aminzadeh, M. Nikravesh (auth.), Dr. Patrick Wong, Dr. Fred Aminzadeh, Dr. Masoud Nikravesh (eds.)
ISBN(s): 9783790824957, 379082495X
Edition: 1
File Details: PDF, 26.72 MB
Year: 2002
Language: english
SKU: EB-4625096 Category: Tags: , , , , ,

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Product details:

ISBN 10: 379082495X
ISBN 13: 9783790824957
Author: PM Wong, F Aminzadeh, M Nikravesh

In the middle of the 20th century, Genrich Altshuller, a Russian engineer, analysed hundreds of thousands of patents and scientific publications. From this analysis, he developed TRIZ (G. Altshuller, “40 Principles: TRIZ Keys to Technical Innovation. TRIZ Tools,” Volume 1, First Edition, Technical Innovation Center, Inc. , Worcester, MA, January 1998; Y. Salamatov, “TRIZ: The Right Solution at the Right Time. A Guide to Innovative Problem Solving. ” Insytec B. V. , 1999), the theory of inventive problem solving, together with a series of practical tools for helping engineers solving technical problems. Among these tools and theories, the substance-field theory gives a structured way of representing problems, the patterns of evolution show the lifecycle of technical systems, the contradiction matrix tells you how to resolve technical contradictions, using the forty principles that describe common ways of improving technical systems. For example, if you want to increase the strength of a device, without adding too much extra weight to it, the contradiction matrix tells you that you can use “Principle 1: Segmentation,” or “Principle 8: Counterweight,” or “Principle 15: Dynamicity,” or “Principle 40: Composite Materials. ” I really like two particular ones: “Principle 1: Segmentation,” and Principle 15: Dynamicity. ” “Segmentation” shows how systems evolve from an initial monolithic form into a set of independent parts, then eventually increasing the number of parts until each part becomes small enough that it cannot be identified anymore.

Soft Computing for Reservoir Characterization and Modeling 1st Table of contents:

Chapter 1: Introduction to Reservoir Characterization and Modeling

1.1 Overview of Reservoir Engineering
1.2 Traditional Methods in Reservoir Characterization
1.3 Challenges in Reservoir Characterization and Modeling
1.4 Introduction to Soft Computing
1.5 The Role of Soft Computing in Reservoir Engineering
1.6 Advantages and Limitations of Soft Computing Techniques

Chapter 2: Fundamentals of Soft Computing Techniques

2.1 Overview of Soft Computing
2.2 Artificial Neural Networks (ANNs)
2.3 Fuzzy Logic Systems
2.4 Genetic Algorithms (GAs) and Evolutionary Computation
2.5 Support Vector Machines (SVM) in Reservoir Modeling
2.6 Hybrid Soft Computing Approaches

Chapter 3: Data Acquisition and Preprocessing for Reservoir Modeling

3.1 Sources of Reservoir Data
3.2 Data Acquisition Techniques in Reservoir Engineering
3.3 Data Preprocessing Methods
3.4 Noise Reduction and Signal Processing
3.5 Feature Selection and Dimensionality Reduction
3.6 Preparing Data for Soft Computing Applications

Chapter 4: Artificial Neural Networks in Reservoir Characterization

4.1 Basics of Neural Networks
4.2 Feedforward Neural Networks for Reservoir Data
4.3 Radial Basis Function Networks in Reservoir Characterization
4.4 Training Neural Networks for Reservoir Modeling
4.5 Case Studies in Neural Network-based Reservoir Characterization
4.6 Limitations and Challenges of ANN in Reservoir Modeling

Chapter 5: Fuzzy Logic and Fuzzy Inference Systems in Reservoir Modeling

5.1 Fundamentals of Fuzzy Logic
5.2 Fuzzy Inference Systems (FIS) for Reservoir Modeling
5.3 Fuzzy Clustering in Reservoir Characterization
5.4 Fuzzy Logic for Uncertainty Management
5.5 Applications of Fuzzy Logic in Reservoir Simulation
5.6 Case Studies in Fuzzy Logic Reservoir Modeling

Chapter 6: Genetic Algorithms and Evolutionary Computing for Optimization

6.1 Basics of Genetic Algorithms (GAs)
6.2 GAs for Reservoir Optimization Problems
6.3 Genetic Programming in Reservoir Characterization
6.4 Multi-objective Optimization using Genetic Algorithms
6.5 Hybrid Genetic Algorithms for Reservoir Simulation
6.6 Case Studies in Genetic Algorithm-based Reservoir Management

Chapter 7: Support Vector Machines in Reservoir Characterization and Prediction

7.1 Introduction to Support Vector Machines (SVM)
7.2 SVM for Reservoir Classification and Prediction
7.3 SVM in Well Log Interpretation
7.4 Kernel Methods in Reservoir Modeling
7.5 Hybrid SVM Models in Reservoir Engineering
7.6 Case Studies and Applications of SVM in Reservoir Characterization

Chapter 8: Ensemble Models and Hybrid Soft Computing Techniques

8.1 Introduction to Ensemble Learning Methods
8.2 Combining Soft Computing Models for Improved Accuracy
8.3 Hybrid Models in Reservoir Characterization
8.4 Neural Network and Fuzzy Logic Hybrid Systems
8.5 Genetic Algorithm-based Ensemble Models
8.6 Case Studies of Hybrid Soft Computing Techniques

Chapter 9: Machine Learning Approaches in Reservoir Simulation

9.1 Machine Learning Overview and Types
9.2 Supervised Learning in Reservoir Simulation
9.3 Unsupervised Learning for Data Clustering
9.4 Reinforcement Learning in Reservoir Management
9.5 Deep Learning Applications in Reservoir Engineering
9.6 Practical Examples of Machine Learning in Reservoir Simulation

Chapter 10: Uncertainty Quantification and Risk Assessment in Reservoir Modeling

10.1 Introduction to Uncertainty in Reservoir Engineering
10.2 Soft Computing Techniques for Uncertainty Analysis
10.3 Fuzzy Logic for Uncertainty Modeling
10.4 Monte Carlo Simulations and Soft Computing
10.5 Risk Assessment in Reservoir Characterization
10.6 Case Studies on Uncertainty Management using Soft Computing

Chapter 11: Advanced Reservoir Characterization using Soft Computing

11.1 Multi-scale and Multi-physics Reservoir Modeling
11.2 Soft Computing for Integrated Reservoir Characterization
11.3 Hybrid Reservoir Simulation Models
11.4 Data-Driven Reservoir Characterization
11.5 Real-Time Reservoir Modeling with Soft Computing
11.6 Advanced Case Studies in Reservoir Characterization

Chapter 12: Soft Computing for Enhanced Oil Recovery (EOR)

12.1 Introduction to Enhanced Oil Recovery
12.2 Modeling EOR Processes with Soft Computing
12.3 Machine Learning for EOR Process Optimization
12.4 Genetic Algorithms in EOR Parameter Tuning
12.5 Neural Networks for EOR Decision Support
12.6 Case Studies in Soft Computing for EOR

Chapter 13: Big Data and Soft Computing in Reservoir Engineering

13.1 The Role of Big Data in Reservoir Engineering
13.2 Integrating Soft Computing with Big Data Analytics
13.3 Data Mining for Reservoir Characterization
13.4 Deep Learning with Big Data in Reservoir Simulation
13.5 Cloud Computing and Soft Computing for Reservoir Management
13.6 Future Trends in Big Data and Soft Computing

Chapter 14: Real-Time Reservoir Monitoring and Control using Soft Computing

14.1 Real-Time Reservoir Data Acquisition Systems
14.2 Soft Computing for Real-Time Reservoir Decision Making
14.3 Adaptive Control Systems in Reservoir Engineering
14.4 Predictive Maintenance using Soft Computing
14.5 Remote Sensing and IoT for Reservoir Monitoring
14.6 Case Studies of Real-Time Reservoir Management

Chapter 15: Challenges, Future Trends, and Applications

15.1 Current Challenges in Soft Computing for Reservoir Engineering
15.2 Future Trends in Soft Computing Applications
15.3 Integrating Soft Computing with Traditional Reservoir Engineering
15.4 Advances in Artificial Intelligence for Reservoir Modeling
15.5 Closing Remarks on Soft Computing for Reservoir Engineering

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Tags: PM Wong, F Aminzadeh, M Nikravesh, Soft Computing, Reservoir Characterization