DSDE: Features

Digital transformation supported by data analytics and artificial intelligence can be the differentiator that allows the upstream industry to persist in the next 100 years, providing a unique foundation for innovation to enhance general public awareness and life quality.

The application of the industrial Internet of things within oil and gas promises to revolutionize the oil field by enabling the acquisition and accessibility of far greater amounts of data, especially for tasks that previously have been managed through historically analog documentation and storage.

What is the effect of the reservoir type on the application of AI and ML in reservoir and production modeling?

The types of advancements made in real-time drilling data acquisition and processing are now on the doorstep of the North American completions sector. Technology developers are banding together under the umbrella of “coopetition” in a bid to change the way producers fracture tight reservoirs.

Remote and integrated operations can make production safer and more efficient. Despite this, the industry has struggled to implement these clearly effective techniques.

The industrial Internet of Things (IoT) is changing the way the oil and gas industry operates, but are companies leveraging it to its full potential? What strategies are being employed to handle the obstacles to implementation? Finding value in people plays a role in integrating IoT into operations.

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

The combination of digital technologies will enable Chevron—and, eventually, other companies—to process, visualize, interpret, and glean insights from multiple data sources, the companies said.

Serica Energy has become the first operator to digitize safety in the North Sea after signing a deal to adopt the Restrata Platform, a piece of technology that provides real-time monitoring of people and 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.