machine learning

Well spacing optimization is one of the more important considerations in unconventional field development.

The artificial-intelligence application BHC3 Reliability provides early warning of production downtime and process risk to improve operational productivity, efficiency, and safety.

Just like Houston’s summer heat, corrosion of metal surfaces will occur—whether you like it or not. To help you better understand corrosion, these papers describe using water surveys in a production/injection plant, testing the effectiveness of mitigation, and data evaluation using machine learning.

Using machine learning (ML), image recognition, and object detection, the use of ML on algorithms to recognize objects and describe their condition were investigated—offering new possibilities for performing inspection and data gathering to evaluate the technical condition of oil and gas assets.

Blending smart-proxy models with data-driven models to create hybrid models is not always the best idea for physics- and engineering-related applications.

Even the most powerful computers are still no match for the human brain when it comes to pattern recognition, risk management, and other similarly complex tasks. A new approach, however, could enable parallel computation with light, simulating the way neurons respond in the human brain.

At times, it may seem that machine learning can be performed without a sound statistical background, but this does not take in to account many difficult nuances. Code written to make machine learning easier does not negate the need for an in-depth understanding of the problem.

This paper highlights the results of a test campaign for a tool designed to predict the short-term trends of energy-efficiency indices and optimal management of a production plant.

Recently, AI researchers from Microsoft open-sourced the Decentralized & Collaborative AI on Blockchain project that enables the implementation of decentralized machine-learning models based on blockchain technologies.

Merging tried-and-true physics-based models with data science is bolstering the Houston independent’s reservoir-engineering work on its deepwater and shale assets.