Physics-driven deep learning inversion coupled to fluid flow simulators

A system, comprising:

• a drilling system;
• a logging system comprising a plurality of drill bit logging tools, wherein the logging system is coupled to the drilling system;
• a control system coupled to a plurality of sensors;
• and
• a reservoir simulator comprising a computer processor, wherein the reservoir simulator is coupled to the logging system and the drilling system and comprises functionality for:
• obtaining, by a computer processor, measured data for a subsurface region;
• obtaining, by the computer processor, prior subsurface data for the subsurface region;
• obtaining, by the computer processor, a physics-driven standard regularized joint inversion for at least two model parameters;
• obtaining, by the computer processor, a case-based deep learning inversion characterized by a contracting path and an expansive path;
• forming, by the computer processor, the physics-driven deep learning inversion with the physics-driven standard regularized joint inversion, the case-based deep learning inversion, and a coupling operator based on a penalty function;
• forming, by the computer processor, a feedback loop between the physics-driven standard regularized joint inversion and the case-based deep learning inversion for re-training the case-based deep learning inversion;
• and
• generating an inversion solution for reservoir monitoring, by the computer processor, using a hybrid coupled approach of physics-based and deep learning-based inversions with the feedback loop to converge to a true model distribution through an iterative approach.

A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for:

• obtaining measured data for a subsurface region;
• obtaining prior subsurface data for the subsurface region;
• obtaining a physics-driven standard regularized joint inversion for at least two model parameters;
• obtaining a case-based deep learning inversion characterized by a contracting path and an expansive path;
• forming the physics-driven deep learning inversion with the physics-driven standard regularized joint inversion, the case-based deep learning inversion, and a coupling operator based on a penalty function;
• forming a feedback loop between the physics-driven standard regularized joint inversion and the case-based deep learning inversion for re-training the case-based deep learning inversion;
• and
• generating an inversion solution for reservoir monitoring using a hybrid coupled approach of physics-based and deep learning-based inversions with the feedback loop to converge to a true model distribution through an iterative approach.