Combining Machine Learning with Traditional Reservoir Physics for Predictive Modeling and Optimization of a Large Mature Waterflood Project in the Gulf of San Jorge Basin in Argentina

Abstract

Abstract A novel technology that combines the benefits of speed of data sciences with the predictivity capabilities of traditional simulation is being applied to model two blocks of a large waterflood project in the Gulf of San Jorge basin in southern Argentina. The tool is being used to provide a prescription of injection water redistribution that optimizes production and reserves development and reduces injection cost. The technology used is called DataPhysics* and combines the robustness of reservoir physics with the speed of data sciences techniques. The process solves a limited number of unknowns in a continuous scale making it several orders of magnitude faster than traditional numeric simulation. The reservoir model is created from raw (uninterpreted) data and is updated continuously allowing for close loop reservoir optimization in real time. Long term predictivity is enabled by the fact that the tool honors the reservoir physics. At the time of writing this paper the recommendations of the predictive model have been implemented in the pilot sector of the field and early positive results have been observed.