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Existing Carbon and Gas Storage Facilities

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Justin Napolitano

2022-05-06 12:30:32.169 +0000 UTC


Table of Contents


Series

This is a post in the North American Energy series.
Other posts in this series:

  • Existing Carbon and Gas Storage Facilities
  • Potential Carbon and Hydrogen Storage Facilities Near Import/Export Ports
  • Potential Carbon and Hydrogen Storage Wells Near Pipelines
  • Reviewing North American Gas and Oil Field Distribution

  • Natural Gas Storage Facilities

    In this post, I identify existing gas storage facilities in the united states. The data can be used to build locate storage facilities nearest to power plants.

    Data Import

    import pandas as pd
    import matplotlib.pyplot as plt
    import geopandas as gpd
    import folium
    import contextily as cx
    import rtree
    from zlib import crc32
    import hashlib
    from shapely.geometry import Point, LineString, Polygon
    

    Natural Gas Storage Facility Data

    ## Importing our DataFrames
    
    gisfilepath = "/Users/jnapolitano/Projects/data/energy/Natural_Gas_Storage_Facilities.geojson"
    
    ng_storage_df = gpd.read_file(gisfilepath)
    
    na = ng_storage_df.PROPMAX.min()
    ng_storage_df.replace(na, 0 , inplace=True)
    
    ng_storage_df = ng_storage_df.to_crs(epsg=3857)
    
    
    ng_storage_df.describe()
    

    OBJECTID POPULATION LATITUDE LONGITUDE OWNERPCT MAXDEL WORKCAP BASEGAS TOTALCAP PROPMAX PROPWORK PROPTOTAL
    count 486.000000 486.0 486.000000 486.000000 486.000000 4.860000e+02 4.860000e+02 4.860000e+02 4.860000e+02 486.0 486.0 486.0
    mean 243.500000 0.0 40.104496 -90.940442 94.232510 1.014176e+06 1.267850e+07 8.983322e+06 2.178277e+07 0.0 0.0 0.0
    std 140.440379 0.0 5.410305 13.266648 22.942722 1.587148e+07 2.116152e+07 1.701687e+07 3.496403e+07 0.0 0.0 0.0
    min 1.000000 0.0 28.984463 -151.273674 0.000000 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.0 0.0 0.0
    25% 122.250000 0.0 37.670592 -95.947958 100.000000 2.751325e+04 1.451850e+06 6.282835e+05 2.765000e+06 0.0 0.0 0.0
    50% 243.500000 0.0 40.248852 -87.046458 100.000000 1.000000e+05 4.191552e+06 2.983754e+06 7.905500e+06 0.0 0.0 0.0
    75% 364.750000 0.0 42.699620 -82.009671 100.000000 3.080000e+05 1.374581e+07 9.253025e+06 2.427000e+07 0.0 0.0 0.0
    max 486.000000 0.0 61.263013 -71.568969 100.000000 3.500000e+08 1.698000e+08 1.417339e+08 2.872000e+08 0.0 0.0 0.0
    
    .. index::
       single: Natural Gas Storage Facility Map
    

    Natural Gas Storage Facility Map by Type

    ng_storage_map =ng_storage_df.explore(
        column="TYPE", # make choropleth based on "PORT_NAME" column
         popup=False, # show all values in popup (on click)
         tiles="Stamen Terrain", # use "CartoDB positron" tiles
         cmap='Reds', # use "Set1" matplotlib colormap
         #style_kwds=dict(color="black"),
         marker_kwds= dict(radius=6),
         tooltip=['NAICS_DESC','REGION', 'TYPE', 'OWNER', 'BASEGAS', 'TOTALCAP','PROPTOTAL', 'RESERVNAME' ],
         legend =True, # use black outline)
         categorical=True,
        )
    
    
    ng_storage_map
    
    Make this Notebook Trusted to load map: File -> Trust Notebook