modeling

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 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.

This article explores how the pursuit of a "perfect" reservoir model may be hindering progress in an industry increasingly shaped by data, uncertainty, and AI.

This paper describes the development of a method of predicting drillstring-friction coefficient during tripping operations that can be used for early warning of stuck pipe.

This paper proposes a time-series analysis approach to build a reliable, easy-to-use tool to automatically detect stuck pipe accurately and early.

The authors present an efficient workflow using an embedded discrete fracture model to simulate carbon-dioxide flow by use of conductive faults.

This paper provides guidelines for thermal modeling for carbon capture and storage projects in a depleted gas field.

The authors of this paper present a workflow designed to achieve maximum integration between analytical and modeling activities in carbon capture and storage projects.