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As the oil and gas industry moves more into the machine learning space, Python-conversant petroleum domain specialists will prove to be increasingly valuable to organizations.

An EIA report shows natural gas exports reaching 4.6 Bcf/D in February, the 13th consecutive month in which the country's natural gas exports exceeded its imports. Exports are projected to reach 7.5 Bcf/D by 2020.

A merger with KeyW and the completion of the sale of its energy and chemical segment to WorleyParsons align with the service company’s decision to focus on its higher-growth, higher-margin lines of business.

Indigo Natural Resources, Aethon Energy, and Rockcliff Energy are among the most active operators in the revived Haynesville Shale of North Louisiana and East Texas. And most people outside of the region likely have never heard of them.

Virtual metering technology has been in use for years as a cost-effective means of monitoring production, but what else can it do? How reliable is it as a backup to physical multiphase meters?

The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields.

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.

A growing number of oil industry leaders are saying that data sharing across the industry is needed, but change is coming slowly.

Electrical-submersible-pump (ESP) technology predominates available artificial-lift options. The risk of ESP failures can be reduced greatly with the right combination of advanced technologies, such as combining artificial intelligence with a cloud-based autonomous surveillance system.

The developed world must no longer tolerate the buildup of mountains of waste while other places in the world, especially in underdeveloped countries, lack access to energy, food, clean water, and clean air.

These most recent deals bring the services company's divestiture total to $1 billion over the past 1 1/2 years.

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.