machine learning

Random Forest and Neural Network are the two widely used machine-learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest?

Malaysia’s Petronas, Shell Malaysia, and Thailand’s PTTEP are now in the midst of full-scale digital adoption. The companies are beginning to see results, but none is counting on a “big bang” in development of the technology soon.

Arundo Analytics has built an integrated industrial Internet of things platform that allows data scientists to productize data-science solutions and accelerate feedback/improvement iterations between end-users and data scientists effectively.

Microsoft  announced three new services that aim to simplify the process of machine learning—an interface for a tool that automates the process of creating models; a new no-code visual interface for building, training, and deploying models; and hosted Jupyter-style notebooks for advanced users.

This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale.

This paper presents an analytics solution for identifying rod-pump failure capable of automated dynacard recognition at the wellhead that uses an ensemble of ML models.

Machine learning (ML) finds patterns in data. "AI bias" means that it might find the wrong patterns. Meanwhile, the mechanics of ML might make this hard to spot.

Rapid advances in deep learning continue to demonstrate the significance of end-to-end training with no a priori knowledge. However, when models need to do forward prediction, most AI researchers agree that incorporating prior knowledge with end-to-end training can introduce better inductive bias. 

A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically.

In collaboration with Stanford University and Brown University, Google explores how existing knowledge in an organization can be used as noisier, higher-level supervision—or, as it is often termed, weak supervision—to quickly label large training data sets.