The Energy Innovators podcast just released this evening, May 6, 2019, an interview I gave to Francis Norman, with NERA, during my recent trip to Perth, Australia.
Link: https://energyinnovatorspodcast.com/alfonso-reyes-petroleum-data-science/
I hope you enjoy it.
Where I talk about:
- How I started my career
- My first contact with data and digital stuff
- How digital helped model our field fracs
- Data was not really a love at first sight; digital controllers were
- What was my Eureka moment in Data Science
- How looking at datasets of many wells instead of one-well-at-a-time (standalone well) turns to increase oil in all the fields that data science touched
- How computer science and data science intersect
- Why analyzing datasets of wells brings you discoveries
- More Eureka moments with statistical analysis
- Why now is the moment of experimenting with new approaches in petroleum engineering
- This is not stopping: as more fields are converted to digital oilfields more data will be flowing
- Of course you cannot detect outliers with one well = one row
- No matter how good is your well modeling software is, it becomes irrelevant when you input bad well data
- Going from standalone wells to networks of wells
- Applying statistical tools to create multiple scenarios in network models
- Automating the selection the best oil production scenarios at the lower cost
- The joy of sending network model programs to production operators
- How I learned data science with real data, a real challenge: increasing oil rates through production optimization
- How to listen your wells through data. Wells speak with data
- Data Science or scratching where it itches
- Data is generally pretty … pretty ugly: not in the format or shape you want; lots of missing data; bunch of outliers …
- Data Science, ML and AI is to today’s world what the PC (personal computer) was to the world in the early 80’s
- Data Science, contrary to the general belief, is not new. Data Science has been around for more than fifty years. It was called Data Analysis then.
- The field of data science owes John Tukey a lot: a visionary statistician that saw data analysis transcending beyond statistics
- How far back in time can we go in SPE papers for the terms of data science, machine learning and artificial intelligence
- What the petroleum engineering industry should be doing on data science the next 25 years
- Data Science means you have to apply the scientific method to data activities
- The petroleum industry should be encouraging writing papers in the data science, reproducible way
- Teaching the younger generation how to fish their own fish
- How I found that statisticians are very good at teaching data science
- Data Science, Machine Learning and Artificial Intelligence are complementary -an addon-, to physics based petroleum engineering
- It is wrong to interpret Data Science as a confrontation between the Physics based world vs Data based solutions (DS, ML, AI)
- Studying Data Science feels like a journey to our first two years in college or university: math, calculus, statistics, linear algebra … even physics