In a study that applied alternative carbon carrier technology to enhanced oil recovery (EOR) scenarios, researchers at The University of Texas at Austin found that the new method recovered up to 19.5% more oil and stored up to 17.5% more carbon than conventional EOR methods.

This paper presents a novel methodology for assessing the rapid mineral carbonation of carbon dioxide through geochemical interactions with carbon-, magnesium-, and iron-rich minerals abundant in geological formations.

Casing deformation has emerged as a major challenge in China’s unconventional oil and gas fields, prompting the development of new solutions to address the issue.

The US supermajor is using one of its lowest-value hydrocarbon products to generate double-digit production increases in its most prolific US asset.

This study assesses the advantages, constraints, and necessary enhancements of both passive and active electromagnetic techniques in the context of carbon capture and storage.

This paper presents a novel workflow with multiobjective optimization techniques to assess the integration of pressure-management methodologies for permanent geological carbon dioxide storage in saline aquifers.

This paper reviews lean construction management processes adopted in the Apani Field development, from facility design to construction management and drilling-location preparation.

The transaction adds 267,000 net acres and nearly 140,000 BOE/D from Vital Energy, lifting Crescent into the top 10 largest US independents.

The latest acquisition strengthens Cenovus Energy’s position as Canada’s largest SAGD producer.

The collaboration has announced Closed Loop Fracturing, which combines real-time subsurface data with automated surface control.

Shale’s slowdown leaves room for OPEC+ gains as tensions rise between the US and India over Russian oil imports.

The number of high-impact wells drilled across the globe this year are expected to be on trend with the most recent 5-year average.

This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.

This paper introduces a machine-learning approach that integrates well-logging data to enhance depth selection, thereby increasing the likelihood of obtaining accurate and valuable formation-pressure results.

This study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data.

A compelling triptych of recent research showcases the burgeoning capacity of machine learning to unlock substantial efficiencies and enhance decision-making across the exploration and production lifecycle.

This study presents the development of a novel modeling tool designed to predict condensate emulsions, focusing on key factors causing emulsions such as pH, solid content, asphaltene concentration, droplet size, and organic acids.

This study explores enhancing gas production through a novel combination of prestimulation using a coiled tubing unit and high-rate matrix acidizing.

This work introduces a fast, methodical approach to detect liquid loading using easily available field data while avoiding traditional assumptions and to determine critical gas rates directly from field data.

Bad vibes are being addressed by contractors as operators push to go faster, deeper, and longer with unconventional wells.