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This paper is a summary of a study that covers through-life economics for producing green hydrogen from offshore fixed wind turbines.

This paper presents a comprehensive technical review of applications of distributed acoustic sensing.

The authors of this paper analyze a robust, well-distributed parent/child well data set using a combination of available empirical data and numerical simulation outputs to develop a predictive machine-learning model.

In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.

The authors of this paper present the results of an assessment of two regions in Uruguay suitable for bottom-fixed offshore wind technologies.

The authors of this paper describe a technology built on a causation-based artificial intelligence framework designed to forewarn complex, hard-to-detect state changes in chemical, biological, and geological systems.

While innovative solutions are being examined for decarbonization, there is one topic that is rarely emphasized as a key driver in the path toward a more-sustainable future—brownfield optimization.

In this inaugural podcast episode and transcript, Terry Palisch, who will officially begin his 2024 SPE presidency in October, discussed his views of the challenges facing our industry and SPE members, his outlook for our industry, and what his goals will be during his presidency.

Wave power plus a subsea battery can be a reliable source for subsea operations, to a point.

Woodside confirms key contracts for deepwater Mexican Gulf development.

Understanding the subsurface is crucial to the success of carbon capture and storage, and digital solutions are essential for an accurate analysis of the subsurface being considered.

Health, safety, and environment operations can be greatly enhanced by using artificial intelligence (AI) techniques on HSE data. One important aspect inherent in this process is the need to establish trust in the AI system among the users.