Contents

Preface

Modeling of Low-Salinity Waterflood with Temperature-Dependent Geochemical Reactions for Heavy Oil Carbonate Reservoirs

Abstract

Introduction

Basic Theory

Modeling

Description of Coreflood Experiment

Result

LSWF (92(C)

Hot LSWF (115(C)

Application in Pilot-Scaled Reservoir

Conclusion

References

SAGD History from Modeling Point of View

Abstract

Nomenclature

Greek Symbols

Introduction

History of Reservoir Modeling Applying the SAGD Method

Original Model

Butler’s Model- a Linear Interface

Reis’s Model- an Inverted Triangle

Reis’s Model- an Inverted Conical Interface

Introduction of HIM

Geomechanics Effect on SAGD

Effect of Drainage Height on SAGD

Latest SAGD Simulation Modifications

Ongoing SAGD Issues

Conclusion

References

Oil Production Optimization via Optimum Artificial Lift Design

Abstract

Introduction

PVT Model

VLP Matching

IPR Matching

Gas Lift Design

Gas Injection Pressure

Injection Gas Characteristics

Tubing Diameter

Design Input Data

Electrical Submersible Pump Design

Economical Evaluation

Gas Lift Installation Cost

ESP Installation Cost

Project Profitability

Conclusion

References

Polyvinyl Alcohol (PVA) as an Emulsifying Agent for Viscosity Reduction of Heavy and Extra-Heavy Oils

Abstract

1. Introduction

2. Definition and Origin of High Viscosity of Heavy Oil

2.1. Definition and Categorization

2.2. Origin of High Viscosity of Heavy Oil

3. Emulsion and Emulsification Process as Related to the Petroleum Industries

3.1. Basic Concept of Emulsification and Types of Emulsion

3.2. Emulsification in the Aspect of Heavy Oil Transportation

3.3. Emulsification in the Aspect of Heavy Oil Recovery

3.4. Important Properties of Emulsion

4. Polyvinyl Alcohol as a Surfactant

4.1. Role of Surfactants or Emulsifying Agents

4.2. Desirability of PVA in Forming Heavy Oil-in-Water Emulsion for Viscosity Reduction

4.3. Chemical Property of PVA

4.3.1. Computational Modeling of PVA

4.3.2. Orbital Energies

4.4. Unique Properties of PVA and Applications

4.5. Reports from Recent Application of PVA in Heavy and Extra-Heavy Oil Emulsification for Viscosity Reduction

Conclusion

References

An Introduction to Asphaltenes Chemistry

Abstract

1. Introduction

2. Asphaltene Definition

3. Factors Influencing Amount and Composition of Precipitated Asphaltene

3.1. Solvent Type

3.2. Solvent-Crude Oil Volume Ratio

3.3. Temperature

3.4. Solvent (Precipitant) – Oil (Sample) Contact Time

3.5. Origin of Crude Oil or Bitumen

4. SARA Analysis

4.1. Analytical Group Analysis Methods [17]

4.2. Fractions Separated During SARA Analysis

4.3. Techniques for SARA Separation and Analysis

4.3.1. Gravimetric Adsorption Chromatography

4.3.2. High Pressure (or Performace) Liquid Chromatography (HPLC)

4.3.2.1. Evaluation of Different Detectors for HPLC Technique [26]

4.3.2.1.1. Refractive Index Detector (RID)

4.3.2.1.2. Flame Ionization Detector (FID)

4.3.2.1.3. Tracor 945 LC-FID

4.3.3. Thin Layer Chromatography (TLC) with FID [29]

4.3.3.1. TLC-FID Advantages [23, 31]

4.3.3.2. TLC-FID Disadvantage

5. Differences Between Asphaltene and Other Heavy Fractions of a Crude Oil

5.1. Resins

5.2. Petroleum Wax (So-Called Wax)

6. Asphaltene Precipitation Problems

7. Chemical Composition of Asphaltenes

8. Chemical Structure of Asphaltenes

8.1. Archipelago Model

8.2. Continental Model

8.3. Modified Yen (Mullins-Yen) Model

9. Molecular Weight

10. Stability of Asphaltene

11. Thermodynamic Models for Prediction of Asphaltene Stability and Instability

11.1. Activity Coefficient-Based Models

11.2. Equation of State (EOS) Models

11.2.1. Cubic Equation of State

11.2.2. Cubic Plus Association Equation of State (CPA-EOS) Based Model

11.2.3. SAFT Equation of State Based Model

11.3. Colloidal/Micellization Models

11.3.1. Colloidal Model

11.3.2. Micellization Model

11.4. Scaling Equation, Mathematical Correlations and Intelligent Models

11.5. Association Models

Conclusion

References

Scaling Equations for Asphaltene Precipitation Modeling

Abstract

1. Introduction

2. Scaling Equations for Asphaltene Precipitation Titration Data

2.1. Application of Genetic Algorithm in Scaling Equations

2.2. Support Vector Machine [32]

2.3. Artificial Neural Network Model

2.4. Fuzzy Logic Model

2.5. Least Square Support Vector Machine

3. Scaling Equations for Asphaltene Precipitation Due to Natural Depletion and Gas Injection

Conclusions

References

On the Estimation of Wax Deposition in Crude Oil Systems

Abstract

Introduction

Artificial Neural Network

Particle Swarm Optimization

Results and Discussion

Conclusion

References

Efficient Estimation of Well Testing Parameters for Naturally Fractured Oil Reservoirs: Application in Reservoir Characterization

Abstract

Nomenclature

Introduction

Overview of Artificial Intelligence

Overview of MLP Artificial Neural Networks

Overview of Genetic Algorithm (GA)

Overview of PSO

Case Study

Data Analysis

Methodology

Results and Discussion

Conclusion

References

On the Prediction of Well Productivity Index for Horizontal Oil Wells

Abstract

1. Introduction

2. An Overview of Artificial Intelligence Methods

2.1. Multilayer Perceptron Networks (MLP)

2.2. Method of Genetic Algorithm Optimization (GA) and Its Combination with Artificial Neural Network

2.3. Particle Swarm Optimization Algorithm (PSO) and Its Combination with Artificial Neural Network

3. Results and Discussion

3.1. Data Analyzing and Procedures and Methods of Creating Anns

3.2. Modeling Using MLP Network

Conclusion

References

Evaluation of Petro-Physical Properties (Porosity and Permeability) of Carbonate Oil Reservoirs

Abstract

1. Introduction

2. Model

2.1. Multilayer Perceptron Networks (MLP)

2.2. Radial Basis Function Neural Networks (RBF)

2.3. Genetic Algorithm Optimization (GA)

2.4. Particle Swarm Optimization (PSO)

3. Case Study

3.1. Data Analysis and ANN Building Methods

4. Results and Discussion

4.1. MLP Method for Porosity Prediction

4.2. RBF Method for Porosity Prediction

4.3. MLP Method for Permeability Prediction

4.4. RBF Method for Permeability Prediction

Conclusion

References

Prediction and Elimination of Drill String Sticking Using Artificial Intelligence Technique

Abstract

Nomenclature

Acronyms

Variables

1. Introduction

2. Geographical Location and Structural Characteristics of Maroun Oilfield

3. Artificial Neural Network

4. Methaheuristic Optimization Algorithms

4.1. Genetic Algorithms (GA)

4.2. Particle Swarm Optimization (PSO)

4.3. Open Source Development Model Algorithm (ODMA)

4.4. Hybrid Genetic Algorithm and Particle Swarm Optimization

4.4.1. Initialization

4.4.2. Evaluation and Ranking

4.4.3. GA Method

4.4.3.1. (Selection)

4.4.3.2. (100% Crossover)

4.4.3.3. (20% Mutation)

4.4.4. PSO method

4.4.4.1. (Updates)

4.5. Imperialist Competitive Algorithm (ICA)

5. Methodology and Results

Conclusion

References

Integrated Lost Circulation Prediction in Drilling Operation

Abstract

1. Introduction

2. Modeling Approach

3. Model Development

3.1. Support Vector Machine

3.1.1. Linear Learning Machines

3.1.2. Kernel-Induced Feature Spaces

3.1.3. Generalization Theory

3.1.4. Optimization Theory

3.1.5. Reducing Ternary SVM to Binary SVM

3.2. Feature Selection

4. Results and Discussion

Conclusion and Recommendation

References

Optimization of Drilling Penetration Rate in Oil Fields Using Artificial Intelligence Technique

Abstract

Nomenclature

Introduction

Model

Oil Field

Methodology and Results

Discussion

1. Drilling Bit

2. Rate of Penetration and Mud Circulation Rate

3. Pump Pressure and Total Flow Area

4. Weight on Bit and the Rotation Speed of the Drill String

5. Mud Viscosity

Conclusion

Metric Conversion Factors

References

Improvement of Drilling Penetration Rate in Oil Fields Using a PSO - GA - MLP Hybrid Network

Abstract

1. Introduction

2. Multilayer Perceptron (MLP) Neural Network

3. PSO-GA Hybrid Algorithm

4. Case Study

5. Methodology and Results

5.1. Modeling

5.1.1. MLP

5.1.2. Training of MLP with PSO-GA (MLP-PSO-GA)

6. Optimization of Drilling ROP

7. Discussion on the Results

7.1. Drilling Bit

7.2. Drilling ROP

Conclusion

References

Estimating the Drilling Fluid Density in the Mud Technology: Application in High Temperature and High Pressure Petroleum Wells

Abstract

1. Introduction

2. Model Development

2.1. Data Gathering

2.2. Support Vector Machine Strategy

4. Results and Discussion

Conclusion

References

Index