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Reservoir-simulation-model inputs are numerous, and uncertainty is pervasive—before, during, and after development. With the pressure to deliver results quickly, how do we find the right balance?

Data-driven, or top-down, modeling uses machine learning and data mining to develop reservoir models based on measurements, rather than solutions of governing equations.

Tiny bubbles, called nanobubbles, are the focal point of a new innovation aimed at transforming produced water from a costly byproduct into a valuable asset.

In upstream oil and gas, cloud computing is very immature because the industry has always been challenged by storage and computational capability. However, high-performance cloud computing may create an opportunity for smaller companies lacking infrastructure for scientific applications.

Saudi Aramco's Manifa field demonstrates that our industry can operate in harmony with the environment. Industry needs to minimize the impact of drilling and producing unconventional resources.

The renewable energy sector is showing strong global growth, especially wind and solar power, but oil shows no sign of losing market share in the global energy mix.

A new well intervention tool can solve annular isolation problems using a proprietary epoxy sealant placed at predefined locations, which can result in significant rig time savings. Field trial results are shared in this article.

DNV’s new training center will run full-scale experiments for a joint industry project aimed at investigating cost-efficient explosion load descriptions for process areas.

This paper analyzes differing well-factory approaches to unconventional assets, with examples from the Wolfcamp unconventional oil play in the Permian Basin.

Our problem is that we are dealing with multiphase, multicomponent fluids whose compositions change spatially and temporally inside imperfectly understood heterogeneous reservoirs whose characteristics also change spatially and temporally.