Machine Learning and Flow Assurance in Oil and Gas Production (Hardcover, 2023)

Chapter 1 Machine Learning and Flow Assurance Issues

 Abstract

1.1 Introduction

1.2 Flow assurances challenges

1.3 Machine learning vocabulary

References

 

Chapter 2 Machine Learning in Oil and Gas Industry

Abstract

2.1 Introduction

2.2. Machine Learning in Upstream

2.3 Machine Learning advancements in the oil and gas industry

2.4 Challenges

2.5 COVID-19's impact on the oil and gas industry, and AI as a solution Companies

2.6 Summary

References

 

Chapter 3 Multiphase Flow Systems and Potential of Machine Learning Approaches in Cutting Transport and Liquid Loading Scenarios

Abstract

3.1 Introduction to multiphase

3.2 Flow Assurance issues in Drilling Applications (Cutting Transport)

3.3 Introduction of Cutting transport issues

3.4 Evolution of various cutting transport models

3.5 Empirical model

3.6 Transient model

3.7 Machine Learning approaches for cutting transport

3.8 Flow Assurance issues in Liquid Loading Applications

3.9 Case Studies in Multiphase Flow Assurance

3.10 Conclusion

References

 

Chapter 4 Machine Learning in Corrosion

Abstract

4.1 Introduction

4.2 Corrosion In Oil and Gas Industry

4.3 Mitigation Procedures

4.4 Corrosion Prediction Models

4.5 Machine Learning in Corrosion

4.6 Case study

References

 

Chapter 5 Machine Learning in Asphaltenes Mitigation

Abstract

5.1 Introduction

5.2 Asphaltene Precipitation and Deposition in oil and gas industry

5.3 Asphaltene Mitigation Procedures

5.4 Asphaltene Prediction Models

5.5 Machine Learning Application in Asphaltenes Precipitation and Deposition Control

5.6 Conclusion

References

 

Chapter 6 Machine learning for Scale deposition in oil and gas industry

Abstract

6.1 Introduction

6.2 Source of Scaling in Oil and Gas Industry

6.3 Mechanism of Scale Deposition

6.4 Effect of scaling to equipment pipelines

6.5 Scale Inhibition Placement

6.6 Prediction Models Available for Scale Formation Detection

6.7 Machine Learning for Scale Deposition

6.8 Applications of Artificial Intelligence in Oil Desalination Systems

6.9 Case Studies on Scaling Measurement Using Machine Learning

6.10 Conclusion

References

 

Chapter 7 Machine Learning in CO2 sequestration

Abstract

7.1 Introduction

7.2 Conventional CO2 sequestration techniques

7.3 Machine Learning in CO2 sequestration

7.4 Conclusion

References

 

Chapter 8 Machine Learning in Wax Deposition

Abstract

8.1 Introduction

8.2 Wax Deposition Mitigation Techniques

8.3 Prediction’s Models in Wax Deposition

8.4 Machine learning in Wax Deposition

8.5 Wax Deposition Case Studies

8.6 Conclusion

References

 

Chapter 9 Machine Learning Application in Gas Hydrates

Abstract

9.1 Introduction to Gas Hydrates

9.2 Application of Gas Hydrates

9.3 Conventional Gas Hydrate Mitigation Method

9.4 Chemical Inhibition of Gas Hydrates

9.5 Flow Assurance Challenge.

9.6 Machine Learning in Gas Hydrates

9.7 Case Study

9.8 Conclusion

References

 

Chapter 10 Machine Learning Application Guidelines in Flow Assurance

Abstract

10.1 Introduction

10.2 Data

10.3 Representation

10.4 Model

References

 

Bhajan Lal (엮은이)    정보 더보기

Jai Krishna Sahith Sayani (엮은이)    정보 더보기

Cornelius Bavoh (엮은이)    정보 더보기

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