modeling

In today’s era of asset management, digital twins are changing risk management, optimizing operations, and benefitting the bottom line.

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

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

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.