modeling

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

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

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.

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.