DSDE: Features

The nascent technology uses a new way to store, process, and measure information in computer systems. It is expected to introduce drastic changes in the development of technology, the discovery of algorithms, and the advancement of computer architectures.

Artificial intelligence tools present many opportunities for the energy industry, and, as technological concepts leave the realm of science fiction, companies have started to grasp what is possible. What roles do culture and ethics play in helping companies understand the digital revolution?

Partnerships with big tech, tech startups, and innovative service companies—and the merging of their data, cloud, and software applications—are proving essential for operators in the scaling phase of digital deployment. Equinor, Microsoft, and Halliburton are among those joining forces.

This article discusses the role of data management in the context of exploration and production from the 1990s, when building centralized databases was the mainstream, to the end of the second decade of the 2000s.

A fracturing test site in West Virginia has quietly made a data trove available on the website of the Marcellus Shale Energy and Environment Lab.

Often, I receive questions from colleagues asking for tips on data science and machine learning as applied to petroleum engineering. This column address some of those questions I have collected. Here is my advice on becoming a petroleum engineer and a data science wizard.

To analyze the status of digital transformation strategies and the pace of implementation in the Middle East, an SPE Applied Technology Workshop brought together operating and service companies and consulting firms for a discussion.

Digital transformation: It’s a phrase that seems to be on the lips of everyone in the oil and gas industry, and that was certainly true at the inaugural Energy in Data conference held in Austin. The conference, however, showed that the transformation is more than on its way. It’s here.

Usually, field engineers manually pick events such as start and end times out of hydraulic-fracturing pumping data. This manual process is time-consuming and prone to error. Now, a Denver-based company is using machine learning to identify these events more accurately and consistently.

Unlike structured data, unstructured data is information that either does not have predefined labels or is not organized in a predefined template, and inefficient management of this data is holding back the industry.