Data Science and Machine Learning in Petroleum Engineering

Introduction

Dear colleagues and students! My name is Tran Nguyen Thien Tam, and I am a lecturer at the Department of Drilling and Production Engineering, Faculty of Geology and Petroleum Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam. For more than ten years, from 2010 to the present (2023), I taught many subjects related to petroleum engineering, such as Geostatistics, Petroleum Reservoir Engineering, Introduction to Petroleum Engineering, Drilling Engineering, Well Testing, Petroleum Reservoir Simulation, Production Analysis and Forecast, Oil and Gas Gathering and Transportation, Applied Computing in Petroleum Engineering, Hydraulic Machinery - Compressors, and Health and Safety in the Engineering Industry.

In the course of my work, alongside petroleum engineering-related specialties, I have a passion for numerical methods, data science, and machine learning. As you all know, in the explosive era of artificial intelligence and big data, almost all people doing science and engineering work today more or less have to do research on data science and machine learning. Of course, I am no exception. However, most scientists or technicians, including me, are not trained in data science and machine learning. They have to educate themselves through books, papers, forums, websites, etc. that specialize in this subject, and this process is not easy. The algorithms such as gradient descent, artificial neural networks, and support vector machines can be confusing for newbies, and deep learning algorithms like long short-term memory and convolutional neural networks can challenge the wisdom of many people who have a certain understanding of machine learning. In our university environment, the students I teach and supervise also have a lot of difficulty understanding the knowledge related to machine learning. Empathetic those difficulties, I came up with the idea of why not write articles to spread knowledge about the applications of data science and machine learning in petroleum engineering? And that's the reason for this post. To be honest, I'm just a newbie in data science and machine learning, but as the Vietnamese say, "Mua vui c?ng ???c m?t vài tr?ng canh" or “I hope it's also a bit helpful”. This sentence reminds me of the slogan "Just do it" of the world-famous shoe company Nike. Hopefully, with my small effort, it can also help some people, at least my students. Therefore, I look forward to your company on this knowledge-sharing journey. So, here is the detailed content that I plan to write about data science and machine learning in petroleum engineering: ?

Contents

Part 1: Fundamentals of Petroleum Engineering

1. Introduction to petroleum

2. The origin of petroleum

3. Petroleum system

4. Field life cycle

5. Upstream, midstream, and downstream sectors of the oil and gas industry

6. Petroleum exploration

7. Petroleum drilling

8. Well completion

9. Petroleum production

10. Oil and gas field processing

11. Oil and gas gathering and transportation

12. Main subjects of petroleum engineering

Part 2: Fundamentals of Data Science

1. Introduction to data science

2. Essential math for data science

3. Microsoft Excel for data science

4. Python for data science

Part 3: Fundamentals of Machine Learning

1. Introduction to machine learning

2. Essential math for machine learning

3. Microsoft Excel for machine learning

4. Python for machine learning

Part 4: Data Science and Machine Learning in Petroleum Engineering

1. Data science and machine learning in petroleum geostatistics

2. Data science and machine learning in petroleum geoscience

3. Data science and machine learning in petroleum geophysics

4. Data science and machine learning in drilling engineering and well completion

5. Data science and machine learning in reservoir engineering

6. Data science and machine learning in petrophysics

7. Data science and machine learning in well testing and pressure transient analysis

8. Data science and machine learning in petroleum production engineering

9. Data science and machine learning for multiphase flow in pipes

10. Data science and machine learning in flow assurance

Notes

These contents may change, but there are basically four parts, including Fundamentals of Petroleum Engineering, Fundamentals of Data Science, Fundamentals of Machine Learning, and Data Science and Machine Learning in Petroleum Engineering. It should also be added that, in the writing process, I will not follow the order because writing in order can be very difficult and slows down the writing process considerably. After I finish writing an article, I will come back here and attach a link to the relevant title. So you can just follow this main post to access and read all related content. Depending on the content, articles can be written on LinkedIn or Medium.

I will probably start my series of articles with the topic Data science and machine learning in petroleum geostatistics, because statistics is the foundation of data science and machine learning, and geostatistics is, in my opinion, one of the most important foundations of Earth science and petroleum engineering.

It's not allowed, but please also note that you are free to share the content, but please cite the source. Thank you so much for this kindness.

Information

In order for readers to follow and interact effectively, I would like to attach my "personal and academic information" link to this post.

It has information related to the author including email, social network, etc and academic pages such as social networking site for scientists and researchers ResearchGate, online publishing platform Medium, Internet hosting service for software development and version control GitHub, etc. to best serve readers.

Last but not least, I look forward to receiving feedback and suggestions later to make the articles better. Thank you so much for reading these lines, and I'll see you in the next post.