Rare-earth elements (REEs) supply raw materials that constitute many of our modern critical infrastructure, defense, technology, and electrification needs. Despite REE accumulations occurring in conventional bedrock and ion-adsorption deposits sourced from weathering of igneous rocks, unconventional host materials such as coal and related sedimentary strata have been identified as promising sources of REEs to meet growing demand. To maximize the potential of unconventional resources such as REE-coal systems, new approaches are needed overcome challenges from mineral systems with no known deposits and areas with sparse geochemical data. This article presents a systematic knowledge-data resource assessment method for predicting and identifying REE resource potential and occurrence in these unconventional systems. The method utilizes a geologic and geospatial knowledge-data approach informed and guided by REE accumulation mechanisms to systematically assess and identify areas of higher enrichment. An assessment of the Powder River Basin is presented as a test case to demonstrate the method workflow and results. The key output is a potential enrichment score map reported with varying confidence levels based on the amount of supporting evidence. Results from the test case indicate several locations with promising potential for different types of coal-REE deposits, demonstrating the viability of the method for exploration and assessment of unconventional REE resources. The method is flexible by design and, with sufficient applicable knowledge and data, can be adapted for assessing critical mineral systems in other sedimentary systems as well.

As human exploration of the subsurface increases, there is a need for better data- and knowledge-driven methods to improve prediction of subsurface properties. Present subsurface predictions often rely upon disparate and limited a priori information. Even regions with concentrated subsurface exploration still face uncertainties that can obstruct safe and efficient exploration of the subsurface. Uncertainty may be reduced, even for areas with little or no subsurface measurements, using methodical, science-driven geologic knowledge and data. We have developed a hybrid spatiotemporal statistical-geologic approach, subsurface trend analysis (STA), that provides improved understanding of subsurface systems. The STA method assumes that the present-day subsurface is not random, but is a product of its history, which is a sum of its systematic processes. With even limited data and geologic knowledge, the STA method can be used to methodically improve prediction of subsurface properties. To demonstrate and validate the improved prediction potential of the STA method, it was applied in an analysis of the northern Gulf of Mexico. This evaluation was prepared using only existing, publicly available well data and geologic literature. Using the STA method, this information was used to predict subsurface trends for in situ pressure, in situ temperature, porosity, and permeability. The results of this STA-based analysis were validated against new reservoir data. STA-driven results were also contrasted with previous studies. Both indicated that STA predictions were an improvement over other methods. Overall, STA results can provide critical information to evaluate and reduce risks, identify and improve areas of scarce or discontinuous data, and provide inputs for multiscale modeling efforts, from reservoir scale to basin scale. Thereby, the STA method offers an ideal framework for guiding future science-based machine learning and natural language processing to optimize subsurface analyses and predictions.

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Recent natural and anthropogenic events, such as Hurricanes Katrina and Rita and the Deepwater Horizon oil spill, have identified significant gaps in our ability to predict risks associated with offshore hydrocarbon production as well as our capabilities to respond to deleterious events of varying scope, magnitude, and duration. As offshore hydrocarbon development in the Gulf of Mexico continues to push into new territory, there is a need to develop computational tools that enable the rapid prediction of outcomes associated with unexpected hydrocarbon release events from deepwater and ultra-deepwater systems in the Gulf of Mexico. To date, no comprehensive system-wide tool exists that can simulate the complexities of engineered-natural systems and provide the baseline data that is required to drive the simulations.To address this gap, we are developing the Gulf of Mexico Integrated Assessment Model (GOM IAM), the first coordinated platform that will allow for independent, rapid-response, and science based predictions providing the capabilities to assess risks and potential impacts associated with deep and ultra-deep water drilling in the Gulf of Mexico. This predictive model and its analyses allow for the assessment and quantification of risks and environmental impacts from deepwater and ultra-deepwater oil and gas drilling and production, as well as provide a robust tool and database that can provide crucial information necessary for the response and recovery following future loss of control events. Once the GOM IAM is developed, it can be utilize to: if) identify potential risks; ii) identify technology gaps, iii) improve our understanding of the degree of uncertainty relative to key systems and interactions associated with deep and ultra-deep water offshore hydrocarbon development to promote safer development and operations, and iv) run scenarios to serve as a baseline rapid response tool for any future oil spill events.