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As more hydrocarbon production from hydraulic fracturing and other methods produce large volumes of water, innovative methods must be explored for treatment and reuse of these waters. However, understanding the general water chemistry of these fluids is essential to providing the best treatment options optimized for each producing area. Machine learning algorithms can often be applied to datasets to solve complex problems. In this study, we used the U.S. Geological Survey’s National Produced Waters Geochemical Database (USGS PWGD) in an exploratory exercise to determine if systematic variations exist between produced waters and geologic environment that could be used to accurately classify a water sample to a given...
Categories: Data; Tags: Alabama, Alaska, Alaska Region, Arizona, Arkansas, All tags...
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These data are presented to geochemically characterize glass from the informally named Khonkho tephra, an ashfall deposit that crops out at the Khonkho Wankane archaeological site in Jesus de Machaca, Bolivia. The Khonkho tephra appears to have been emplaced by a major explosive eruption from a currently unknown volcanic source. The unit forms a marker bed at an archaeologically important stratigraphic level. These data are to accompany a publication that describes the tephra-fall deposit and its implications and will eventually be used to correlate the unit to samples from other locations where it crops out and to identify its source volcano and eruption. Any use of trade, firm, or product names is for descriptive...


    map background search result map search result map Input Files and Code for: Machine learning can accurately assign geologic basin to produced water samples using major geochemical parameters Electron microprobe geochemical data for glass from the Khonkho tephra Electron microprobe geochemical data for glass from the Khonkho tephra Input Files and Code for: Machine learning can accurately assign geologic basin to produced water samples using major geochemical parameters