In 2014 the Alberta Energy Regulator (AER) initiated a Play-Based Regulation (PBR) pilot project as a step towards implementation of the Unconventional Regulatory Framework. One of the goals of the PBR pilot is to encourage companies in the unconventional play area to work together on plans for surface development to minimize the numbers of facilities and surface impacts. This data set is one of a series created using earth observation imagery to assess surface change caused by energy exploration.
The PBR area extends from Twp. 52, Rge. 7, W 5th Mer. to Twp. 70, Rge. 5, W 6th Mer., covering the towns of Edson, Fox Creek, Mayerthorpe, Whitecourt, Swan Hills and Valleyview.
The anthropogenic footprints shapefile contains a compilation of developed and mixed developed footprint classes extracted from the land-use/land-cover classifications data for the PBR area from 2005 to 2013, published by AGS. They can be used as a baseline for planning, managing and monitoring surface infrastructure needs and impacts.
1. Using ArcMap, extract the ‘mixed-developed’ and ‘developed’ classes from the Alberta Geological Survey’s DIG 2015-0028 & DIG 2015-0064 to 2015-0071 datasets to create raster grids of the anthropogenic footprints for each year, 2005 to 2013.
2. Convert each of the footprint grids into a separate polygon shapefile using the ‘no simplified polygon’ option. For each of these polygon shapefiles apply steps 3-7.
3. Using the ‘selection by location’ function and the polygon shapefile, create a point shapefile of non-confidential status oil and gas wells from the Alberta Energy Regulator’s geodatabase that are located inside the footprint polygons.
4. Create an oil and gas well pad raster grid by buffering the points in the point shapefile with a 100 m radius, an average size for a well pad. Then apply these buffered points using a ‘clip’ function to identify well pad pixels in the matching annual grid from step 1.
5. Convert the resulting well pad grid to a polygon shapefile and append it to the footprint polygon shapefile from step 2 to identify the well pad polygons.
6. Add Type and Year columns to the footprint polygon shapefile’s attribute table.
7. Populate the Year attribute with the year the imagery was acquired for the footprint. Populate the Type attribute with ‘well pad’ or ‘other’ based on the value of the GRIDCODE attribute. Dissolve individual polygons based on Type and Year attributes.
8. Append the annual footprint polygon shapefiles from 2005 to 2013 to create this digital data set.