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Corrosion is a pervasive issue that affects oil and gas production. It poses a significant threat to the safety and integrity of oil and gas equipment, and it can lead to costly repairs and downtime. Mitigating corrosion is a crucial part of maintaining the productivity and safety of oil and gas operations.

This paper describes a work flow that integrates data analysis, machine learning, and artificial intelligence to unlock the potential of large relative permeability databases.

As the demand for energy continues to increase, so does the sociopolitical demand that these resources be produced in a sustainable way. This reflective thought has defined this month’s selection of technical papers, each speaking to a different facet of this feature’s theme: unconventional reservoir development for a sustainable energy transition.

This paper describes the material selection methodology and corrosion studies performed in a CO2 sequestration project to optimize well costs and improve overall project economics without jeopardizing the CO2-injector-well integrity.

This paper describes natural fractures and their effect on hydrocarbon productivity in the Vaca Muerta shale formation.

Because the basics of CCS and CCUS are mostly familiar to a large part of the readership, I am choosing to bring to your attention the summary of those articles that are devoted to approaches other than or beyond CCS, even if they have to climb further on the development ladder. These include bio-based approaches, geothermal, and use of hydrogen as a substitute fuel.

The objective of this paper is to present a fundamentals-based model of three-phase flow consistent with observation that avoids the pitfalls of conventional models such as Stone II or Baker’s three-phase permeability models.

This paper discusses an effort to prevent permanent loss of mangrove habitats using a long-term restoration strategy based on the transplantation of fertilized seedlings produced at mangrove nurseries in partnership with local communities.

The authors of this paper describe a solution using machine-learning techniques to predict sandstone distribution and, to some extent, automate the process of optimizing well placement.

This paper presents an intelligent tube solution that combines data retrieved by the sensors with the actual resistance of each pipe in the well to allow adjustment of production parameters while ensuring installation safety.