Subject ▸ machine learning

Using application microprocessors for seismic

First practical application that I know of using the next thing after TPUs (Tensor Processing Units): ASICs. Ideal hardware for the massive seismic data. #petroleumengineering #spe #oilandgas #deeplearning LinkedIn References: Post Article

Hinton's Coursera course *Neural Networks for Machine Learning*

The machine learning course is little heavy in math but I would recommend to, at least, taking a look at some of the lectures to find out what AI and ML are about. You can browse as much as you want, for free, in Coursera. LinkedIn link Direct link to toronto.edu

Machine Learning for lithofacies classification from well logs by Paolo Dell'Aversana

Don’t miss reading this paper on an application of machine learning in petroleum engineering by Paolo Dell’Aversana. References Original article in LinkedIn Paper at ResearchGate

Reinforcement Learning. Not an easy subject

Not an easy subject nor as common as supervised and unsupervised learning but key for making better intelligent machines. Book by Andrew Barto and Richard S. Sutton: https://lnkd.in/ewb_MeJ Link to file #petroleumengineering #machinelearning #digitalpetroleum #datascience

Is Windows in the comfort zone?

There is also a very controversial issue in this article such as Windows ecosystem is the comfort zone of developers and people alike. If you are serious about Application Development in Data Science, Machine Learning and Artificial Intelligence -, then you have to be get out of the comfort zone and start doing it in Linux. The terminal means reproducibility. Start with practicing Linux an hour a day. No?

The most used Machine Learning algorithms in Petroleum Engineering

Continuing with the article on machine learning (ML), from the papers extracted in OnePetro, I have been able to detect the use of these ML algorithms: Neural Network Genetic Algorithm Support Vector Machine Principal Component Analysis Linear Regression Fuzzy Logic Hierarchical K-Means Singular Value Decomposition Decision Tree Support Vector Regression Deep Learning Logistic Regression Boosting

A Machine Learning paper research on Petroleum Engineering

So happy to find that OnePetro has improved its search algorithms. Now, lowercase, uppercase and dashed keywords return the same results. This wasn’t the case few months ago. You can see the results below. This is a search of papers using R, #rstats, and the package petro.One. You could see that “neural network”, “neural-network, and”NEURAL NETWORK“, return the same number of papers: 3197. The same with”data driven" and “data-driven”. This makes the paper search much more efficient and fast.

Neural Networks for Petroleum Engineering

Neural Networks papers in OnePetro It is incredible the number of papers on Neural Networks contributed by the petroleum engineering community: an astonishing total of 2,918 papers that mention the keyword “neural networks” . And that’s only Conference Papers. From all these papers, 534 have “neural networks” in their title and they go back as far in time as 1975 (one paper), and start with force by the end of the 80’s (7 papers).

Data Science for Petroleum Engineering: How does someone become good at Deep Learning?

I watched few days ago the interview from professor Andrew Ng to one of the luminaries of deep learning and artificial intelligence, Dr. Youshua Bengio. He has written books and dozens of papers on deep learning and neural networks. I liked the style. Pretty down to earth stuff. Just the way professor Andrew likes to do: bringing machine learning, deep learning to the masses. So the question remains: do petroleum engineers need to learn data science, computer science, statistics, machine learning, neural networks, virtualization and GPU based engineering?