Methods and Systems For Machine - Learning Based Simulation of Flow

A method for producing a hydrocarbon from a hydrocarbon reservoir, comprising:

• generating a reservoir model comprising a plurality of sub regions;
• simulating at least one sub region of the plurality of sub regions using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the at least one sub region;
• using a machine learning algorithm to approximate, based on the set of training parameters, an inverse operator of a matrix equation that provides a solution to fluid flow through a porous media;
• simulating the hydrocarbon reservoir using the inverse operator approximated for the at least one sub region;
• and
• producing a hydrocarbon from the hydrocarbon reservoir based, at least in part, upon the results of the simulation.

A system for modelling reservoir properties, comprising:

• a processor;
• a non-transitory machine readable medium comprising code configured to direct the processor to:
• generate a reservoir model comprising a plurality of sub regions;
• simulate at least one sub region of the plurality of sub regions using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the at least one sub region;
• use a machine learning algorithm to approximate, based on the set of training parameters, an inverse operator of a matrix equation that provides a solution to fluid flow through a porous media;
• simulate the reservoir using the inverse operator approximated for the at least one sub region;
• and
• generate a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable, medium based, at least in part, on the results of the simulation.

A non-transitory, computer readable medium comprising code configured to direct a processor to:

• create a simulation model of a hydrocarbon reservoir, wherein the simulation model comprises a plurality of sub regions;
• simulate at least one sub region of the plurality of sub regions using a training simulation to obtain a set of training parameters comprising state variables and boundary conditions of the at least one sub region;
• use a machine learning algorithm to approximate, based on the set of training parameters, an inverse operator of a matrix equation that provides a solution to fluid flow through a porous media;
• simulate the hydrocarbon reservoir using the inverse operator approximated for the at least one sub region;
• and
• generate a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable, medium based, at least in part, on the results of the simulation.