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Bangert P. Machine Learning And Data Science...Oil And Gas..2021

TORRENT SUMMARY

	Status:	This torrent has NOT been verified.
Category:	Other
Infohash	1729c2dc9298d79fac3e339383e32fd61a7a8481
Size:	17.9 MB in 1 file
Release Date(s):	Torrent Release: 04-10-2021 by user
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DESCRIPTION

Textbook in PDF format

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.
Introduction
Who this book is for
Preview of the content
Oil and gas industry overview
Brief history of oil exploration
Oil and gas as limited resources
Challenges of oil and gas
Data Science, Statistics, and Time-Series
Measurement, uncertainty, and record keeping
Correlation and timescales
The idea of a model
First principles models
The straight line
Representation and significance
Outlier detection
Residuals and statistical distributions
Feature engineering
Principal component analysis
Practical advice
Machine Learning
Basic ideas of machine learning
Bias-variance complexity trade-off
Model types
Training and assessing a model
How good is my model?
Role of domain knowledge
Optimization using a model
Practical advice
Introduction to Machine Learning in the Oil and Gas Industry
Forecasting
Predictive maintenance
Production
Modeling physical relationships
Optimization and advanced process control
Other applications
Data Management from the DCS to the Historian
Introduction
Sensor data
Time series data
How sensor data is transmitted by field networks
How control systems manage data
Historians and information servers as a data source
Data visualization of time series data—HMI (human machine interface)
Data management for equipment and facilities
Simulators, process modeling, and operating training systems
How to get data out of the field/plant and to your analytics platform
Conclusion: do you know if your data is correct?
Getting the Most Across the Value Chain
Thinking outside the box
Costing a project
Valuing a project
The business case
Growing markets, optimizing networks
Integrated strategy and alignment
Case studies: capturing market opportunities
Digital platform: partner, acquire, or build?
What success looks like
Project Management for a Machine Learning Project
Classical project management in oil & gas—a (short) primer
Agile—the mindset
Scrum—the framework
Project execution—from pilot to product
Management of change and culture
Scaling—from pilot to product
The Business of AI Adoption
Defining artificial intelligence
AI impacts on oil and gas
The adoption challenge
The problem of trust
Digital leaders lead
Overcoming barriers to scaling up
Confronting front line change
Doing digital change
Global Practice of AI and Big Data in Oil and Gas Industry
Introduction
Integrate digital rock physics with AI to optimize oil recovery
The molecular level advance planning system for refining
The application of big data in the oil refining process
Equipment management based on AI
Soft Sensors for NOx Emissions
Introduction to soft sensing
NOx and SOx emissions
Combined heat and power (CHP)
Soft sensing and machine learning
Setting up a soft sensor
Assessing the model
Conclusion
Detecting Electric Submersible Pump Failures
Introduction
ESP data analytic
Principal Component Analysis
PCA diagnostic model
Case study: diagnosis of the ESP broken shaft
Conclusions
Predictive and Diagnostic Maintenance for Rod Pumps
Introduction
Feature engineering
Project method to validate our model
Results
Forecasting Slugging in Gas Lift Wells
Introduction
Methodology
Focus projects
Data structure
Outlook
Conclusion

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Filename		Size
	Bangert P. Machine Learning and Data Science...Oil and Gas..2021.pdf	17.9 MB

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