University of Texas

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

This paper introduces new methods to perform reliable permeability and saturation-dependent relative permeability measurements in organic-rich mudrock core samples using a pressure-decay setup.

This paper extends an integrated two-scale continuum model that contemplates mass, momentum, and energy changes to study the acid-stimulation process in complex carbonate acid-stimulation systems with the development of fracture and vug networks.

The authors of this paper describe reservoir-fluid-geodynamics processes that explain the reasons behind varying oil compositions and properties within and across different reservoir compartments.

The paper describes a parameter inversion of reservoirs based on featured points, using a semi-iterative well-test-curve-matching approach that addresses problems of imbalanced inversion accuracy and efficiency.

The authors of this paper describe a study in which performance optimization was demonstrated in thermoplastic sealing systems for oil and gas equipment using 3D printing to manufacture multicomponent composite structures.

Regional pore-pressure variations in the Leonardian- and Wolfcampian-age producing strata in the Midland and Delaware basins are studied using a variety of subsurface data.

From optimizing drilling performance to enhancing worker safety, computer vision can change how the industry works.

Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.