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This paper sheds light on newer frontiers of tracer applications with unconventional uses to gain flow insights from an oil and gas reservoir.

The authors’ work states that the qualification approach for offshore hydrogen pipeline systems should include material properties testing under various conditions.

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.

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

The selected papers and suggested additional reading provide valuable insights into the evolving field of development optimization using physics-based simulation models and machine learning.

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.

While rapid production growth comes with proportional risks, it becomes crucial for reservoir surveillance engineers to quickly identify, derisk, and mitigate subsurface performance uncertainties. In this demanding scenario, the need for high-frequency reservoir performance surveillance is more critical than ever. Fortunately, advanced technologies have emerged as a r…

This paper summarizes a collaborative industry study to compare observations between shale-play data sets and basins, develop general insights into parent/child interactions, and provide customized economic optimization recommendations.

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.