METHODS, SYSTEMS, AND COMPUTER-READABLE MEDIA FOR FAST UPDATING OF OIL AND GAS FIELD PRODUCTION MODELS WITH PHYSICAL AND PROXY SIMULATORS

Abstract

Methods, systems, and computer readable media are provided for fast updating of oil and gas field production optimization using physical and proxy simulators. A base model of a reservoir, well, or a pipeline network is established in one or more physical simulators. A decision management system is used to define uncertain parameters for matching with observed data. A proxy model is used to fit the uncertain parameters to outputs of the physical simulators, determine sensitivities of the uncertain parameters, and compute correlations between the uncertain parameters and output data from the physical simulators. Parameters for which the sensitivities are below a threshold are eliminated. The decision management system validates parameters which are output from the proxy model in the simulators. The validated parameters are used to make production decisions.

Description

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram of an operating environment which may be utilized in accordance with the illustrative embodiments of the present invention;

FIG. 2 is a simplified block diagram illustrating a computer system in the operating environment of FIG. 1, which may be utilized for performing various illustrative embodiments of the present invention; and

FIG. 3 is a flow diagram showing an illustrative routine for fast updating of oil and gas field production models with physical and proxy simulators, according to an illustrative embodiment of the present invention.

Claims

1. A method for fast updating of oil and gas field production models using a physical and proxy simulator, comprising: establishing a base model of a physical system in at least one physics-based simulator, wherein the physical system comprises at least one of a reservoir, a well, a pipeline network, and a processing system and wherein the at least one simulator simulates the flow of fluids in the at least one of a reservoir, a well, a pipeline network, and a processing system;defining boundary limits including extreme levels and an uncertainty distribution for each of a plurality of uncertain parameters of the physical system through an experimental design process, wherein the uncertain parameters as defined by the boundary limits and the uncertainty distribution comprise a set of design parameters;fitting data comprising a series of inputs, the inputs comprising the values associated with the set of design parameters, to outputs of the at least one simulator utilizing a proxy model, wherein the proxy model is a proxy for the at least one simulator, the at least one simulator comprising at least one of the following: a reservoir simulator, a pipeline network simulator, a process simulator, and a well simulator; andutilizing an optimizer with the proxy model to determine design parameter value ranges for which outputs from the proxy model match observed data.

2. The method of claim 1 further comprising: utilizing the proxy model to calculate derivatives with respect to the design parameters of the physical system to determine sensitivities;utilizing the proxy model to compute correlations between the design parameters and the outputs of the at least one simulator;ranking the design parameters from the proxy model; andutilizing validated selected parameters from the simulator for production decisions.

3. The method of claim 2 further comprising: utilizing a decision management system to define a plurality of control parameters of the physical system for matching with the observed data;automatically executing the at least one simulator over the set of design parameters to generate a series of outputs, the outputs representing production predictions; andcollecting characterization data in a relational database, the characterization data comprising values associated with the set of design parameters and values associated with the outputs from the at least one simulator.

4. The method of claim 3 further comprising: placing the design parameters for which the sensitivities are not below a threshold and their ranges from the proxy model into the decision management system, the design parameters for which the sensitivities are not below the threshold being selected parameters; andrunning the decision management system as a global optimizer to validate the selected parameters in the simulator.

5. The method of claim 1, wherein establishing a base model of a physical system in at least one physics-based simulator comprises creating a data representation of the physical system, wherein the data representation comprises the physical characteristics of the at least one of the reservoir, the well, the pipeline network, and the processing system including dimensions of the reservoir, number of wells in the reservoir, well path, well tubing size, tubing geometry, temperature gradient, types of fluids, and estimated data values of other parameters associated with the physical system.

6. The method of claim 1, wherein defining boundary limits including extreme levels and an uncertainty distribution for each of the plurality of uncertain parameters of the physical system through an experimental design process comprises defining boundary limits including extreme levels and an uncertainty distribution for permeability, fault transmissibility, pore volume, and well skin parameters, utilizing at least one of Orthogonal Ray experimental design, factorial, and Box-Behnken experimental design processes.

7. The method of claim 1, wherein utilizing the proxy model to calculate derivatives with respect to the design parameters to determine sensitivities comprises determining a derivative of an output of the at least one simulator with respect to one of the series of inputs.

8. The method of claim 1, further comprising removing the design parameters from the proxy model which are determined by a user to have a minimal impact on the physical system.

9. The method of claim 1, wherein utilizing an optimizer with the proxy model to determine design parameter value ranges comprises utilizing the optimizer with at least one of the following: a neural network, a polynomial expansion, a support vector machine, and an intelligent agent.

10. A method for fast updating of oil and gas field exploration models using a physical and proxy simulator, comprising: establishing a base model of a physical system in at least one physics-based simulator, wherein the base model comprises at least one of an earth model, a geologic model, a petrophysical model, a drilling model, and a fluid model;defining boundary limits including extreme levels and an uncertainty distribution for each of a plurality of uncertain parameters of the base model through an experimental design process, wherein the uncertain parameters as defined by the boundary limits and the uncertainty distribution comprise a set of design parameters;fitting data comprising a series of inputs, the inputs comprising the values associated with the set of design parameters, to outputs of the at least one simulator utilizing a proxy model, wherein the proxy model is a proxy for the at least one simulator; andutilizing an optimizer with the proxy model to determine design parameter value ranges for which outputs from the proxy model match observed data.

11. The method of claim 10 further comprising: utilizing the proxy model to calculate derivatives with respect to the design parameters of the base model to determine sensitivities;utilizing the proxy model to compute correlations between the design parameters and the outputs of the at least one simulator;ranking the design parameters from the proxy model; andutilizing validated selected parameters from the simulator for production decisions.

12. The method of claim 11 further comprising: utilizing a decision management system to define a plurality of control parameters of the base model for matching with the observed data;automatically executing the at least one simulator over the set of design parameters to generate a series of outputs, the outputs representing production predictions; andcollecting characterization data in a relational database, the characterization data comprising values associated with the set of design parameters and values associated with the outputs from the at least one simulator.

13. The method of claim 12 further comprising: placing the design parameters for which the sensitivities are not below a threshold and their ranges from the proxy model into the decision management system, the design parameters for which the sensitivities are not below the threshold being selected parameters; andrunning the decision management system as a global optimizer to validate the selected parameters in the simulator.

14. The method of claim 10, wherein establishing a base model in at least one physics-based simulator comprises creating a data representation of the physical system.

15. The method of claim 10, wherein utilizing the proxy model to calculate derivatives with respect to the design parameters to determine sensitivities comprises determining a derivative of an output of the at least one simulator with respect to one of the series of inputs.

16. The method of claim 10, further comprising removing the design parameters from the proxy model which are determined by a user to have a minimal impact on the base model.

17. The method of claim 10, wherein utilizing an optimizer with the proxy model to determine design parameter value ranges comprises utilizing the optimizer with at least one of the following: a neural network, a polynomial expansion, a support vector machine, and an intelligent agent.

18. A method for fast updating of oil and gas field production models using a physical and proxy simulator, comprising: establishing a base model of a physical system in at least one physics-based simulator, wherein establishing the base model comprises creating a data representation of the physical system, wherein the data representation comprises the physical characteristics of at least one of a reservoir, a well, a pipeline network, and a processing system including dimensions of the reservoir, number of wells in the reservoir, well path, well tubing size, tubing geometry, temperature gradient, types of fluids, and estimated data values of other parameters associated with the physical system, wherein the physical system comprises the at least one of a reservoir, a well, a pipeline network, and a processing system, and wherein the at least one simulator simulates the flow of fluids in the at least one of a reservoir, a well, a pipeline network, and a processing system;utilizing a decision management system to define a plurality of control parameters of the physical system for matching with observed data;defining boundary limits including extreme levels and an uncertainty distribution for each of a plurality of uncertain parameters of the physical system through an experimental design process, wherein the uncertain parameters as defined by the boundary limits and the uncertainty distribution comprise a set of design parameters;automatically executing the at least one simulator over the set of design parameters to generate a series of outputs, the outputs representing production predictions; andcollecting characterization data in a relational database, the characterization data comprising values associated with the set of design parameters and values associated with the outputs from the at least one simulator.fitting data comprising a series of inputs, the inputs comprising the values associated with the set of design parameters, to outputs of the at least one simulator utilizing a proxy model, wherein the proxy model comprises at least one of a neural network, a polynomial expansion, a support vector machine, and an intelligent agent, and wherein the proxy model is a proxy for the at least one simulator, the at least one simulator comprising at least one of the following: a reservoir simulator, a pipeline network simulator, a process simulator, and a well simulator;utilizing the proxy model to calculate derivatives with respect to the design parameters of the physical system to determine sensitivities;utilizing the proxy model to compute correlations between the design parameters and the outputs of the at least one simulator;ranking the design parameters from the proxy model;utilizing an optimizer with the proxy model to determine design parameter value ranges for which outputs from the proxy model match the observed data;placing the design parameters for which the sensitivities are not below a threshold and their ranges from the proxy model into the decision management system, the design parameters for which the sensitivities are not below the threshold being selected parameters;running the decision management system as a global optimizer to validate the selected parameters in the at least one simulator; andutilizing the validated selected parameters from the at least one simulator for production decisions.

19. The method of claim 18, wherein using the proxy model to calculate derivatives with respect to the design parameters to determine sensitivities comprises determining a derivative of an output of the at least one simulator with respect to one of the series of inputs.

20. The method of claim 18, further comprising removing the design parameters from the proxy model which are determined by a user to have a minimal impact on the physical system.