Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the authors
1 A comprehensive review of machine application in the oil and gas industry
	1.1 Introduction
	1.2 Contribution of machine learning to the oil and gas industry
		1.2.1 Predictive maintenance
		1.2.2 Spotting digging sites with machine learning
		1.2.3 Machine learning in drilling operations
		1.2.4 Problem-solving with machine learning
		1.2.5 Replacement of manual labor with machine learning tools or automated robots
	1.3 Machine learning in the oil and gas upstream sector
	1.4 Machine learning in the oil and gas midstream sector
	1.5 Machine learning in the oil and gas downstream sector
	1.6 Challenges and future scope
	1.7 Conclusion
	References
2 AI and ML applications in the upstream sector of the oil and gas industry
	2.1 Introduction
		2.1.1 Upstream and downstream in the oil and gas industry
	2.2 Need for ML-based techniques in the oil and gas upstream sector
	2.3 Operations of AI- and ML-based oil and gas fields
	2.4 Accurate modeling and smart rig operations
	2.5 Sensor-based models for well spotting the location
	2.6 AI- and ML-based risk detection system and improved drilling efficiencies
	2.7 Machine learning-based data analysis of the well location
	2.8 Digital models for the extreme paperwork in the field
	2.9 An effective method for providing data to the on-field engineer
	2.10 Challenges faced now and future scope of more development
	References
3 One step further in upstream sector
	3.1 Introduction
	3.2 Digitalization and automation in exploring sector
	3.3 Mapping and analyzing of the field digitally
	3.4 Spotting drilling and pipeline location precisely using ML-based applications (total oil and Google cloud)
	3.5 Digitally monitored production sites
	3.6 Planning and commissioning the onshore and offshore production site based on the ML models
	3.7 Digitally governed production and standardized data collecting
	3.8 SCADA-based network for effective communication in the field
	3.9 Robotization at the dangerous location for drilling for the betterment and safety of workforce
	3.10 Process modeling and simulation for the offshore, onshore and hydraulic fracturing before drilling
	3.11 Future advancements and challenges faced currently
	References
4 Midstream sector with ML models and techniques
	4.1 Introduction to midstream sector and advancement with the ML techniques
	4.2 Transportation with the pipeline and the digital monitoring system
	4.3 Optimizing pipeline scheduling for product flows
	4.4 Improving reliability risk modeling for refining and processing assets
	4.5 Improved storage and processing facilities
	4.6 Maximizing labor productivity and wrench time via employing robotization and ML techniques
	4.7 Predicting the subsea and ground pipeline by ML to optimize lateral buckling mitigations
	4.8 Data management for equipment and facilities along with optimization and process control with automation
	References
5 Downstream sector with machine learning
	5.1 Introduction
	5.2 Smart refining process integrated with the ML
	5.3 Advanced modeling and simulation of the plant and process for better functioning
	5.4 Remote systems operations
	5.5 Risk analysis with the ML during the refining
	5.6 Connecting IoT and other parts of the digital devices
	5.7 Machine vision for safety
	5.8 Energy and asset management
	5.9 Remote operation and performance shutdown using IoT
	References
6 Safety and maintenance with AI and ML
	6.1 Introduction
	6.2 Deep learning risk detecting and predictive diagnostics
	6.3 Boosting productivity with predictive maintenance
	6.4 Digital pre-fire alarming sensor mode
	6.5 Motor vehicle safety and in-vehicle monitoring system
	6.6 Clear vision communication and monitoring model on sites and exploring sites
	6.7 Smart helmets and other safety equipment for the workers
	6.8 Safety with the security of the data with ML models
	6.9 Pipeline leakage monitoring sensor-based system
	References
7 Finance with ML and AI
	7.1 General introduction
	7.2 Factors affecting the finance cost of the industry, end product, and processing
	7.3 Forecasting growth, trends, and market with AI models
	7.4 Price prediction models based on data analysis and ML
	7.5 Financial modeling with the current data for better performance
	7.6 Digitalizing the distribution channel and the end-customer things
	7.7 Smart supply chain management
	7.8 Demand management with computational power
	7.9 Managing the virtual agents and supplier selection with ML and data science
	References
8 Market and trading in oil and gas (petroleum) industry
	8.1 Introduction
	8.2 Oil and gas (petroleum) industry market dynamics
	8.3 Data analysis and market forecasting of prices from the raw material produced in the oil and gas (petroleum) industry
	8.4 Valuation of derivatives or assets of oil and gas (petroleum) industry
	References
9 Future of oil and gas (petroleum) industry with AI
	9.1 Introduction
	9.2 AI in reservoir management
	9.3 AI in drilling
	9.4 AI in exploration
	9.5 AI in production
	References
Index