Horizontal Well Sticks on Map - Please Help!

[AlexG]

Hello,

 

Is there a way for me to plot horizontal oil wells on map? I saw on the video referenced below the that she was able to do it but I have no idea how she did it. 

 

My data table has a column for strat of the stick location (lat and long ) and end of the stick location (lat and long). 

Can some one please help me out?

 

Thank you!

[Gaia Paolini]

Do you mean plotting them as lines on a map? 

[Gaia Paolini]

You could plot your data as a marker layer, and connect by a line for each WellID, if your data were in the format:
Lat Long Type WellID
where Lat Long are the coordinates of each point, and Type is a flag that says Heel or Toe.

Alternatively, you could transform your points into lines, generating a shapefile (vector) dataset, that can be plotted on the map as a feature layer.
To do this, you can create a data function to run the Python script below, where  "df" is the input data table and "Output" is the output dataset.
I have written a sample dxp file (saved in Spotfire 14.0) to show this. I have generated some sample data to test.

import pandas as pd
import geopandas as gpd
from shapely.geometry import LineString

# Save existing columns
original_columns = df.columns.tolist()

# Convert each row to a LineString
df['geometry'] = df.apply(
    lambda row: LineString([(row['Long Heel'], row['Lat Heel']), (row['Long Toe'], row['Lat Toe'])]), axis=1
)


# Convert DataFrame to GeoDataFrame
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')


# Prepare output for Spotfire
# Calculate bounding coordinates and centers for the resulting GeoDataFrame
shape_bounds=  gdf.bounds
shape_bounds = shape_bounds[['minx','maxx','miny','maxy']]
shape_bounds.columns=['XMin','XMax','YMin','YMax']

#Suppress coordinate warnings as these are only a concern over large distances
import warnings
warnings.filterwarnings("ignore")
shape_centroid_points = gdf.centroid
shape_centroid=pd.DataFrame({'XCenter':[],'YCenter':[]})
shape_centroid["XCenter"] = shape_centroid_points.centroid.map(lambda p: p.x)
shape_centroid["YCenter"] = shape_centroid_points.centroid.map(lambda p: p.y)
warnings.filterwarnings("default")
#Turn geometry to binary and downgrade data frame to simple pandas
wkb_geometry = gpd.GeoSeries.to_wkb(gdf['geometry'])
Output=pd.DataFrame({'geometry':wkb_geometry})
Output = pd.concat([Output,shape_bounds,shape_centroid],axis=1)
# Add back original columns 
Output = pd.concat([Output,df[original_columns]],axis=1)

# Set Spotfire geocoding metadata: this is the last statement
Output["XMax"].spotfire_column_metadata = {"MapChart.ColumnTypeId": ["XMax"]}
Output["YMax"].spotfire_column_metadata = {"MapChart.ColumnTypeId": ["YMax"]}
Output["XMin"].spotfire_column_metadata = {"MapChart.ColumnTypeId": ["XMin"]}
Output["YMin"].spotfire_column_metadata = {"MapChart.ColumnTypeId": ["YMin"]}
Output["geometry"].spotfire_column_metadata = {"MapChart.ColumnTypeId": ["Geometry"], "ContentType": ["application/x-wkb"]}

 

test_14.0.dxp