Potential Carbon and Hydrogen Storage Wells Near Pipelines

Potential Carbon Storage Wells Near Pipelines

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

Query Plan

Restrictions

• Must be near a pipeline terminal

Imports

• Pipeline Data
• Well Data

Filtering

• For each well calculate nearest pipeline

• For each well calculate geographic distance from pipeline

• eliminate wells further than 2 km from a pipeline

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)

wells_df.columns

Index(['index', 'OBJECTID', 'ID', 'NAME', 'STATE', 'TYPE', 'STATUS', 'COUNTY',
       'COUNTYFIPS', 'COUNTRY', 'LATITUDE', 'LONGITUDE', 'NAICS_CODE',
       'NAICS_DESC', 'SOURCE', 'SOURCEDATE', 'VAL_METHOD', 'VAL_DATE',
       'WEBSITE', 'WELLIDNO', 'API', 'PERMITNO', 'OPERATOR', 'OPERATORID',
       'PRODTYPE', 'COORDTYPE', 'SURF_LAT', 'SURF_LONG', 'BOT_LAT', 'BOT_LONG',
       'POSREL', 'FIELD', 'COMPDATE', 'TOTDEPTH', 'STAUTS_CAT', 'geometry'],
      dtype='object')

Pipeline DataFrame

## Importing Pipeline Dataframe

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


pipeline_df = gpd.read_file(gisfilepath)

pipeline_df = pipeline_df.to_crs(epsg=3857)

Removing Gathering Pipes from the Data

pipeline_df.drop(pipeline_df[pipeline_df['TYPEPIPE'] == 'Gathering'].index, inplace = True)

Adding PipeGeometry Column

pipeline_df['PipeGeometry'] = pipeline_df['geometry'].copy()

pipeline_df.columns

Index(['FID', 'TYPEPIPE', 'Operator', 'Shape_Leng', 'Shape__Length',
       'geometry', 'PipeGeometry'],
      dtype='object')

Joining Well and Pipeline Data

nearest_wells_df= wells_df.sjoin_nearest(pipeline_df, how = 'left', distance_col="distance_euclidian")
nearest_wells_df.columns

Index(['index', 'OBJECTID', 'ID', 'NAME', 'STATE', 'TYPE', 'STATUS', 'COUNTY',
       'COUNTYFIPS', 'COUNTRY', 'LATITUDE', 'LONGITUDE', 'NAICS_CODE',
       'NAICS_DESC', 'SOURCE', 'SOURCEDATE', 'VAL_METHOD', 'VAL_DATE',
       'WEBSITE', 'WELLIDNO', 'API', 'PERMITNO', 'OPERATOR', 'OPERATORID',
       'PRODTYPE', 'COORDTYPE', 'SURF_LAT', 'SURF_LONG', 'BOT_LAT', 'BOT_LONG',
       'POSREL', 'FIELD', 'COMPDATE', 'TOTDEPTH', 'STAUTS_CAT', 'geometry',
       'index_right', 'FID', 'TYPEPIPE', 'Operator', 'Shape_Leng',
       'Shape__Length', 'PipeGeometry', 'distance_euclidian'],
      dtype='object')

nearest_wells_df

Adding a Distance Km Column

nearest_wells_df['distance_km'] = nearest_wells_df.distance_euclidian.apply(lambda x: x / 1000)

filtered_wells = nearest_wells_df.loc[nearest_wells_df['distance_km'] < 2].copy()

filtered_wells.describe()

Wells Base Map

well_map_ax = filtered_wells.plot(figsize=(15, 15), alpha=0.5, edgecolor='k', markersize = .5)
cx.add_basemap(well_map_ax, zoom=6)
#filtered_wells.plot()

Pipelines Base Map

pipeline_map = pipeline_df.plot(figsize = (15,15), alpha=0.5,)
cx.add_basemap(pipeline_map, zoom=6)

Pipeline and Potential Carbon Storage Well Map

combined_map = wells_df.plot(ax = pipeline_map, alpha=0.5, figsize = (20,20), edgecolor='k', markersize = .5)

#cx.add_basemap(well_map, zoom=6)
#plt.show()

<Figure size 432x288 with 0 Axes>

combined_map.figure