Filters: Tags: oil and gas potential (X)
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This is the dataset for oil and gas potential as described in Copeland et al. (2009) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species, PLoSOne. The dataset should be cited as: Copeland, H., K. Doherty, D. Naugle, A. Pocewicz, J. Kiesecker (2010) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species. The projection of this dataset is: US NAD83 Lambert Conformal Conic False easting 0 False northing 0 Central meridian -107.0 Standard parallel 1 33.0 Standard parallel 2 45.0 Latitude of origin 41.0
We created a probabilistic classification model using the nonparametric machine learning technique 'Random Forests' for oil and gas development potential from low (0) to high (1) across the western US. The six predictor variables used in the model were: geophysical data showing aeromagnetic, isostatic gravity, and Bouguer gravity anomalies, geology, topography and bedrock depth. Our binary response variable was geospatial point data on producing and non-producing oil and gas wells. Our estimates provide insights into the trajectory and eventual endpoint of oil and gas development, but the rate and exact location of development will be subject to additional factors not considered such as market demand, the capacity...
This is the dataset for oil and gas potential as described in Copeland et al. (2009) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species, PLoSOne. The dataset should be cited as: Copeland, H., K. Doherty, D. Naugle, A. Pocewicz, J. Kiesecker (2010) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species. The projection of this dataset is: US NAD83 Lambert Conformal Conic False easting 0 False northing 0 Central meridian -107.0 Standard parallel 1 33.0 Standard parallel 2 45.0 Latitude of origin 41.0
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