Energy resource exploration with the application of machine learning in a GIS environment

Abstract

The exploration of energy resources, in particular geothermal energy, has evolved towards the quantitative evaluation of information produced during reconnaissance and at advanced stages. The introduction of the concepts of geothermal plays and play-fairway have provided the parameters used in the new statistical integration models. Knowledge-based integration models have been displaced by datadriven models using statistical methods and machine learning in the exploration and evaluation process, thus increasing the efficiency of geothermal reservoir discovery.