Reservoir characterization

Fig. 1—Proposed methodology with multiple stages of carbon-storage investigation for magnesium-, calcium-, and iron-rich minerals.

Oilfield chemistry

Study Explores Accelerated Carbon Capture and Storage Through Mineral Carbonation

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.

Fig. 1—Workflow for the developed PIML permeability-prediction methodology.

Reservoir characterization

Physics-Informed Machine Learning Enhances Permeability Prediction in Carbonate Reservoirs

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

Fig. 1—Confusion matrix illustrates the ANN model’s performance in predicting valid and invalid test outcomes where darker shades indicate higher classification frequency.

Reservoir characterization

Machine-Learning Approach Optimizes Formation-Pressure Testing in Complex Reservoirs

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.