Spatial Prediction for Bottom Hole Temperature and Geothermal Gradient in Colombia


Jhon Camilo MATIZ-LEON

Key Words:

geothermal gradient, sedimentary basins, BHT, geostatistical simulation 3D, interpolation, spatial prediction


Stanford Geothermal Workshop







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2758 KB

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The estimation of the geothermal potential of sedimentary basins becomes an essential condition through the representation of temperature and its depth variations. Part of the improvement in the accuracy of the variables of temperature and geothermal gradient (GG) lies in the estimation of the possible values through robust statistics, while knowing their positions in space. The processing and representation environments used for the spatial prediction correspond to 2D and 3D. The exploratory and structural analysis of data was performed using the statistical computing environment R. 2D modeling was executed in Oasis Montaj by Geosoft with pixels as the minimum representation unit. 3D modeling was implemented in GeoModeller by Intrepid Geophysics, using the voxel in the representation of 3D models as a volumetric unit. The techniques used for the 2D and 3D modeling are framed in the deterministic methods (minimum curvature and Inverse Weighted Distance – IDW method), probabilistic methods with Ordinary Kriging, and geostatistical simulation with Sequential Gaussian Simulation (SGS). The analysis was applied in three sedimentary basins with Bottom Hole Temperatures (BHT) values of hydrocarbon wells and was based on a spatial datum known by the observer.

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