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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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308 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Enhanced Recovery Opportunities in the Appalachian Basin

The Midwest Regional Carbon Sequestration Partnership (MRCSP) has incorporated the work of geologic research teams (Geoteams) in its regional characterization, project planning and carbon dioxide (CO2) injection implementation work since the partnership was established by the U.S. Department of Energy (DOE) in 2003. Over this 16-year period, the cohort of Geoteams has grown from five to ten states and has contributed to the characterization of geologic sequestration opportunities, refinement of reservoir and seal data, and supported injection efforts through both predictive and post-injection assessments. The Appalachian Basin Geoteam consists of geologists from the state geological surveys of Kentucky, Maryland, New York, Ohio, Pennsylvania, and West Virginia. Pennsylvania and West Virginia led the research related to enhanced recovery opportunities in oil and gas fields, while Kentucky led the work associated with enhanced gas recovery (EGR) opportunities in organic-rich shales. The Appalachian Basin Geoteam evaluated reservoirs amenable to CO2-EOR and EGR using a combination of data sources and technical approaches. The Geoteam characterized the subsurface geology of the area in stepwise fashion, starting with existing geologic maps and fields/pools data generated as part of previous MRCSP regional characterization research. With this data, the Geoteam correlated stratigraphy and generated cross sections to delineate the extent, depths and thicknesses of those formations that may serve as reservoirs for either miscible or immiscible enhanced recovery projects. In addition, the team prepared structure and isopach maps for prospective formations, as well as assessments of gross and net porosity of these units. Once prospective localities were identified, the Geoteam selected a short list of oil fields in each of the three states for assessment for case study preparation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗