Rare-earth elements (REEs), including lanthanides, scandium, and yttrium, are important for advanced technologies such as renewable energy systems, electronics, medical diagnostics, and precision agriculture. Despite their relative crustal abundance, REE extraction is impeded by complex geochemical behavior, dispersed distribution, and environmental challenges. This review presents a comprehensive overview of REE geochemistry, mineralogy, and major deposit types including carbonatites, alkaline igneous rocks, laterites, placer deposits, coal byproducts, and marine sediments. It also highlights the global distribution and economic potential of key REE projects. The integration of machine learning has further enhanced exploration by enabling deposit classification and geochemical modeling, especially in data-limited regions. Environmental and health challenges associated with REE mining, processing, and electronic waste (e-waste) recycling are studied, along with the expanding use of REEs in agriculture and medicine. Some recycling efforts offer promise for supply diversification, but significant technological and economic barriers remain. Ensuring a secure and sustainable REE supply will require integrated approaches combining advanced analytics, machine learning, responsible extraction, and coordinated policy efforts. The present review offers a general overview that can be useful for informing future studies and resource-related discussions.

This study aims to evaluate relationships between elemental signatures in calcite and the crystallographic orientation of its planes. The ability of calcite (a widespread calcium carbonate mineral) to entrap various trace and minor elements in its structure is the foundation of multiple methods (also called proxies) to reconstruct paleoenvironment conditions (e.g., temperature, pH, and marine chemistry). Although several element-to-calcium ratios (E/Ca) are routinely measured in marine carbonates and are widely used in paleoclimate studies, some of the controls on the incorporation of these elements are still unclear. Here, we examine the effect of crystallography on (E/Ca)_calcite by growing thin layers of calcite on differently oriented Iceland Spar substrates immersed in modified seawater solution. Newly grown calcite layers were examined with Laser Ablation Inductivity–Coupled Plasma Mass Spectrometry (LA-ICP-MS), Backscattered Electron Imaging (BSE), and Energy Dispersive X-ray Spectroscopy (EDS). We propose that the crystallographic orientation might slightly influence the incorporation of lithium (Li), sodium (Na), magnesium (Mg), sulfur (S), and barium (Ba) into the studied calcite faces and have no impact on the incorporation of boron (B), potassium (K), and strontium (Sr) at least under the conditions of our experiment.

Understanding mechanisms of trace or minor elements (impurities) incorporation ...

Permeability prediction is essential for reservoir characterization, commonly derived from core analysis and mercury injection capillary pressure (MICP) data. Many conventional models, often calibrated for sandstones, are based on parameters such as porosity or specific mercury saturation points, which limits their accuracy in carbonate reservoirs due to differing rock properties. This study addresses these limitations by integrating parameters from 12 existing permeability estimation models and selecting three reliable models for carbonates: Swanson’s, Winland’s, and Pittman’s models. These models incorporate Swanson’s parameter (maximum Sb/Pc, where Sb is non-wetting phase saturation and Pc is capillary pressure), pore-throat radius at specific saturations (Winland’s rx), and porosity (Pittman’s model). An integrated framework was developed using data from 50 carbonate samples and validated with 20 additional samples and log data. The permeability values range from 0.01 to 450 millidarcies (mD), with porosity from 1% to 30%. Multiple linear regression established robust relationships between permeability, porosity, and Swanson’s parameter, improving prediction accuracy. Validation using MICP and Stoneley wave data confirmed the model's reliability, demonstrating significant advancements in permeability estimation for heterogeneous carbonate systems. This approach offers a comprehensive and accurate tool for reservoir characterization.

Permeability estimation plays a crucial role in reservoir characterization and is commonly determined through core analysis. Additionally, permeability can be inferred from mercury injection test data. Several models have been developed for this purpose, with their parameters influenced by factors such as pore structure, rock heterogeneity, and pore throat distribution. The widely recognized permeability prediction models of Winland, Pittman, and Dastidar, have been primarily developed based on sandstone formations, with Winland’s model also applied to both sandstone and carbonate rocks. However, due to the inherent heterogeneity of carbonate reservoirs particularly in terms of petrophysical properties and diagenetic alterations the accuracy of these models becomes debatable, especially in complex carbonate reservoirs such as those in Texas and Oklahoma, United States. To improve permeability predictions in carbonate reservoirs, these models require further adjustments to account for their unique pore structures. Since these permeability models were originally calibrated based on sandstone formations, they need to be recalibrated for carbonate rocks due to the significant differences in heterogeneity, as well as variations in petrophysical properties and diagenetic processes between carbonate and sandstone reservoirs. This study calibrates sandstone-based permeability models for carbonate rock formations through a comprehensive investigation of carbonate rock properties and formations. In this study, a total of 1,367 thin section reports and 70 porosity-permeability tests were conducted. The average sampling interval was 25 cm. Porosity in the plug samples was measured using Boyle’s Law, while permeability was determined based on Darcy’s Law. A thin section was prepared from each plug sample and examined under a polarizing microscope. In this study, permeability ranges from 0.01 to 450 mD, and porosity ranges from 1% to 30%. This model calibrates the widely recognized permeability prediction models of Winland, Pittman, and Dastidar for carbonate reservoirs. This model resolves the limitations of permeability models originally adjusted for sandstone reservoirs by recalibrating them for carbonate reservoirs. It incorporates a comprehensive study of the geological and petrophysical characteristics of carbonate formations to enhance accuracy in permeability prediction.