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Reviewing North American Gas and Oil Field Distribution

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Justin Napolitano

2022-05-05 18:40:32.169 +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

  • North American Gas and Oil Fields

    North American gas and oil fields contain spent wells that can be used to store carbon and green hydrogen. They may also potentially yield more resources with the injection of super critical co2 into the wells.

    An analysis of the capcity of wells requires an overview of the fields in general. In this post, I identify the gas fields to later create a model that will predict the price of storing carbon and possibly hyrdogen in spent wells.

    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
    
    /Users/jnapolitano/venvs/finance/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
      warnings.warn(
    

    Oil and Natural Gas Field Data

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

    OBJECTID PR_OIL PR_GAS SHAPE_Length SHAPE_Area
    count 224.000000 224.000000 224.000000 224.000000 224.000000
    mean 112.500000 1530.496585 87.685987 16.473665 10.386605
    std 64.807407 17219.764834 644.145040 43.284473 45.170003
    min 1.000000 0.000000 0.000000 0.100238 0.000594
    25% 56.750000 0.000000 0.000000 2.511389 0.221885
    50% 112.500000 0.000000 0.000000 6.563044 1.225873
    75% 168.250000 0.000000 0.000000 14.317886 5.378536
    max 224.000000 238050.000000 8446.000000 485.692251 448.052251
    
    .. index::
       single: Oil/Gas Fields Map by Commodity
    

    Oil Gas Field Map by Commodity

    fields_map =fields_df.explore(
        column="COMMODITY", # 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', 'COMMODITY' ],
         legend =True, # use black outline)
         categorical=True,
        )
    
    
    fields_map
    
    Make this Notebook Trusted to load map: File -> Trust Notebook