Potential Carbon and Hydrogen Storage Facilities Near Import/Export Ports

Potential Carbon Storage Facilities Near Import/Export Ports

Import and Procedural Functions

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
import numpy as np
from scipy.spatial import cKDTree
from shapely.geometry import Point
from haversine import Unit
from geopy.distance import distance

Restrictions

• Must be near a pipeline terminal
• Must be Near a petrolium Port

Query Plan

Imports

• Import LNG terminal Data
• Import well data

Filtering

• for each well calculate nearest terminal
• for each well calculate distance from nearest terminal
• eliminate wells further than 15 miles from a terminal

Data Frame Import

Wells DataFrame

## Importing our DataFrames

gisfilepath = "/Users/jnapolitano/Projects/data/energy/non-active-wells.geojson"


wells_df = gpd.read_file(gisfilepath)

wells_df = wells_df.to_crs(epsg=3857)

Terminal DataFrame

## Importing our DataFrames

gisfilepath = "/Users/jnapolitano/Projects/data/energy/Liquified_Natural_Gas_Import_Exports_and_Terminals.geojson"


terminal_df = gpd.read_file(gisfilepath)

terminal_df = terminal_df.to_crs(epsg=3857)

Eliminating SUSPENDED Terminal from the DataFrame

terminal_df.drop(terminal_df[terminal_df['STATUS'] == 'SUSPENDED'].index, inplace = True)
terminal_df.rename(columns={"NAME": "TERMINAL_NAME"})
terminal_df['TERMINAL_GEO'] = terminal_df['geometry'].copy()
terminal_df.columns

Index(['OBJECTID', 'TERMID', 'NAME', 'ADDRESS', 'CITY', 'STATE', 'ZIP', 'ZIP4',
       'TELEPHONE', 'TYPE', 'STATUS', 'POPULATION', 'COUNTY', 'COUNTYFIPS',
       'COUNTRY', 'LATITUDE', 'LONGITUDE', 'NAICS_CODE', 'NAICS_DESC',
       'SOURCE', 'SOURCEDATE', 'VAL_METHOD', 'VAL_DATE', 'WEBSITE', 'EPA_ID',
       'ALT_NAME', 'OWNER', 'POSREL', 'JRSDCTN', 'CONTYPE', 'IE_PORT',
       'BERTHS', 'STORAGE', 'STORCAP', 'CURRENTCAP', 'APPCAP', 'OPYEAR',
       'IMPEXPCTRY', 'VOLUME', 'PRICE', 'geometry', 'TERMINAL_GEO'],
      dtype='object')

Plotting Terminal by TYPE

terminal_map =terminal_df.explore(
    column="TYPE", # make choropleth based on "PORT_NAME" column
     popup=True, # show all values in popup (on click)
     tiles="Stamen Terrain", # use "CartoDB positron" tiles
     cmap='Set1', # 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,
    )


terminal_map

Terminal Impressions

According to the data there is not an export nor import location on The Western Side of the United States.

East Asian import or carbon capture export demands could justfity port development. Another study must be conducted.

Filtering Wells by Terminal Distance in Scipy

Edit

This method does not accuraletly calculate distance. The algorith used below calculates distance on a euclidan plane. In order to calculate a correct answer we must account for sphericity.

I include the code below as reference and a learning opportunity

def ckdnearest(gdA, gdB):

    nA = np.array(list(gdA.geometry.apply(lambda x: (x.x, x.y))))
    nB = np.array(list(gdB.geometry.apply(lambda x: (x.x, x.y))))
    btree = cKDTree(nB)
    dist, idx = btree.query(nA, k=1)
    gdB_nearest = gdB.iloc[idx].drop(columns="geometry").reset_index(drop=True)
    gdf = pd.concat(
        [
            gdA.reset_index(drop=True),
            gdB_nearest,
            pd.Series(dist, name='dist')
        ], 
        axis=1)

    return gdf

c = ckdnearest(wells_df, terminal_df)

c.describe()

nearest_wells_df= wells_df.sjoin_nearest(terminal_df, distance_col="distance_euclidian")

nearest_wells_df.describe()

Calculating Distance in Kilometers from Import/Export Terminal

#df.geopy.distance.distance(coords_1, coords_2).km
#df.apply(lambda row: distance(row['point'], row['point_next']).km if row['point_next'] is not None else float('nan'), axis=1)
# Thanks to https://stackoverflow.com/questions/55909305/using-geopy-in-a-dataframe-to-get-distances

nearest_wells_df['true_distance_km'] = nearest_wells_df.apply(lambda row: distance((row['LATITUDE_left'], row['LONGITUDE_left']), (row['LATITUDE_right'], row['LONGITUDE_right'])).km if row['geometry'] is not None else float('nan'), axis=1)

nearest_wells_df.describe()

Filtering Wells within 50 KM of a Terminal

filtered_wells = nearest_wells_df.loc[nearest_wells_df['true_distance_km'] < 50].copy()

filtered_wells.describe()

Map of Wells within 50 km of an Import/Export Terminal by Type

filtered_wells.explore(
    column="STATUS_left", # make choropleth based on "PORT_NAME" column
     popup=True, # show all values in popup (on click)
     tiles="Stamen Terrain", # use "CartoDB positron" tiles
     cmap='Set1', # 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,)