From the onset, we recognized that this project would present a great interplay between 3D models of structures such as drilling rigs and powerplants, as well as point cloud data and illustrations of the depth of various layers of the earths surface . Immediately, we were excited by the thought of how compelling SketchUp and Google Earth would be in helping us tackle this sort of visualization. The results make for a far more compelling presentation than one that would have required having to flip through slides of static images and graphs attempting to tell the same story.
Once we located the site in Google Earth, we were able to import topography and satellite imagery into SketchUp as a base for the models. Then we imported a series of CAD files and images reference materials to create models of the drilling rig, power plant, and a model of the Earth's Surface. A big thanks goes out to the folks at Geodynamics - an Australian geothermal energy company that helped out by providing us with a bunch of information which aided us in creating a realistic model for our simulation.
The trickier part of this project - was figuring out how to take thousands of data points that mapped out a fracture cloud and add them to our 3D model? An integral aspect of our answer to that question is the ruby-driven application enhancement aspect fostered by Ruby developers around the world who greatly contribute to SketchUp's ability to be used seamlessly within a collaborative environment. In our case, we were able to leverage the work of Didier Bur, a talented Ruby developer who had created a ruby script that helps solve the data cloud problem we faced.
While a larger list of Didier's scripts can be found in the Ruby Library Depot, we ended up using his cloud.rb script to import a bunch of data from a CSV file which was given to us by Geodynamics. The CSV file contained measurements of each stimulation event in the geothermal site, including the location, and time of each event that occurred. Using the cloud.rb script, we were able to tell SketchUp to represent each data point as a 3D box. The data was parsed out and imported into separate layers based on the time-stamp info from the CSV file. Then the data from each layer was colored according to a timeline diagram. Ultimately, this strategy allowed us to create an animation entirely within SketchUp that took the timing of the stimulation events into consideration.
Thanks to Google.org for inviting us to participate in this exciting project. It was a great chance to learn more about enhanced geothermal energy production and Google's Develop Renewable Energy Cheaper than Coal initiative. We plan to create an episode of 'The Sketchup Show' soon that outlines more of the details of visualizing this project in SketchUp, so stay tuned for that.
While a larger list of Didier's scripts can be found in the Ruby Library Depot, we ended up using his cloud.rb script to import a bunch of data from a CSV file which was given to us by Geodynamics. The CSV file contained measurements of each stimulation event in the geothermal site, including the location, and time of each event that occurred. Using the cloud.rb script, we were able to tell SketchUp to represent each data point as a 3D box. The data was parsed out and imported into separate layers based on the time-stamp info from the CSV file. Then the data from each layer was colored according to a timeline diagram. Ultimately, this strategy allowed us to create an animation entirely within SketchUp that took the timing of the stimulation events into consideration.
Thanks to Google.org for inviting us to participate in this exciting project. It was a great chance to learn more about enhanced geothermal energy production and Google's Develop Renewable Energy Cheaper than Coal initiative. We plan to create an episode of 'The Sketchup Show' soon that outlines more of the details of visualizing this project in SketchUp, so stay tuned for that.
The SketchUp Models for this project are available for download from Google's 3D Warehouse.
