System for building machine learning models to accelerate subsurface model calibration

A computing system, comprising:

• one or more processors;
• and
• a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
• receiving a reservoir model of a reservoir, wherein the reservoir model comprises a static model, a dynamic model, or both, and wherein the reservoir model comprises uncertainty framing inputs that define types and ranges of parameters in the reservoir model that are uncertain;
• simulating ensembles of dynamic cases of the reservoir model to produce simulated dynamic cases, wherein each dynamic case is different, wherein the dynamic cases each comprise a plurality of different levels, wherein the levels comprise a global level, a regional level, a well level, and a completion level, wherein the dynamic cases are simulated by varying values of the parameters on each level, by varying the values of the parameters on the different levels, or both;
• performing assisted history matching between the simulated dynamic cases and observed production data to identify a combination of the parameters and the values of the parameters, wherein the combination of the parameters and the values of the parameters result in one or more mismatch values between the simulated dynamic cases and the observed production data being less than a mismatch threshold, and wherein the assisted history matching is performed using proxy modeling or ensemble Kalman filters;
• and
• generating a display based upon the combination of the parameters or the values of the parameters that result in the one or more mismatch values being less than the mismatch threshold, wherein the display comprises a plurality of dashboard components that expedite subsequent iterations of the assisted history matching.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:

• receiving a reservoir model of a reservoir that includes:
• a static model, and
• a dynamic model, wherein the reservoir model comprises uncertainty framing inputs that define types and ranges of parameters in the reservoir model that are uncertain, and
• wherein the parameters include:
• porosity, permeability, compressibility, and relative permeability;
• simulating ensembles of dynamic cases of the reservoir model to produce simulated dynamic cases,
• wherein each dynamic case is different,
• wherein the dynamic cases each comprise a plurality of different levels,
• wherein the levels comprise a global level, a regional level, a well level, and a completion level,
• wherein the dynamic cases are simulated by varying values of the parameters on each level such that a first of the parameters has a first value in a first of the dynamic cases on a first of the levels, and the first parameter has a second value in a second of the dynamic cases on the first level,
• wherein the first and second values are different,
• wherein the dynamic cases are also simulated by varying the values of the parameters on the different levels such that the first parameter has a third value in a third of the dynamic cases on the first level, and the first parameter has a fourth value in a fourth of the dynamic cases on a second one of the levels, and
• wherein the third and fourth values are different;
• performing assisted history matching between the simulated dynamic cases and observed production data to identify a combination of the parameters and the values of the parameters,
• wherein the combination of the parameters and the values of the parameters result in one or more mismatch values between the simulated dynamic cases and the observed production data being less than a mismatch threshold, and
• wherein the assisted history matching is performed using proxy modeling or ensemble Kalman filters;
• generating a display based upon the combination of the parameters or the values of the parameters that result in the one or more mismatch values being less than the mismatch threshold,
• wherein the display comprises a plurality of dashboard components that expedite subsequent iterations of the assisted history matching;
• and
• performing a wellsite action in response to the one or more mismatch values, the combination of the parameters or the values of the parameters, or the dashboard components,
• wherein the wellsite action comprises generating or transmitting a signal that causes a physical action to occur at a wellsite.