A method for accelerating the decision making process for reservoir risk management is described. In particular, an ensemble based decisions and filter are used to quickly compare different information scenarios to determine the best strategy for developing a hydrocarbon-containing reservoir.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A ensemble-based method for decision making process, the method comprising: a) framing a decision problem; b) uploading existing frame data into a modeling program; c) generating and combining at least 2 realizations of said existing frame data to create an ensemble model (E0), wherein said ensemble model defines an input uncertainty band; d) deriving at least one system performance output variable and one or more target percentiles of said system performance output variable from an ensemble prediction of the ensemble model; e) determining a first decision (D0) from said derived one or more target percentiles and a utility function for said ensemble model; f) updating said ensemble model with possible outputs of at least one pre-determined information scenario to create at least one temporary ensemble model (En), wherein the temporary ensemble model has a smaller uncertainty band than E0; g) deriving at least one system performance output variable from an ensemble prediction of each temporary ensemble model; h) determining at least one second decision for each of said temporary ensemble model; i) calculating a value of information (VOI) for each information scenario wherein the VOI is the difference of said utility function between said second decision of that information scenario and said first decision; j) comparing said VOI for each information scenario; and, k) selecting an information scenario that matches the one or more target percentiles of said ensemble model, wherein if no information scenario matches the one or more target percentiles, then no selection is made. 2. The method of claim 1, wherein step 1.f) uses an ensemble Kalman filter (EnKF) to update said ensemble of reservoir model. 3. The method of claim 1, wherein said system performance output variable is a financial variable. 4. The method of claim 1, wherein said financial variable is net present value, rate of return, asset value, and uncertainty band of these values. 5. The method of claim 1, wherein said selected information scenario is applied to a reservoir. 6. A ensemble-based method for decision making process, the method comprising: a) uploading existing reservoir data into a modeling program; b) generating and combining at least 2 realizations of said existing reservoir data to create an ensemble of reservoir model (E0), wherein said ensemble of reservoir model defines an input uncertainty band; c) deriving at least one system performance output variable and one or more target percentiles of said system performance output variable from an ensemble prediction of the ensemble of reservoir model; d) determining a first decision (D0) from said derived one or more target percentiles and a utility function for said ensemble of reservoir model; e) updating said ensemble of reservoir model with possible outputs of at least one pre-determined information scenario to create at least one temporary ensemble of reservoir model (En), wherein the temporary ensemble of reservoir model has a smaller uncertainty band than E0; f) deriving at least one system performance output variable from an ensemble prediction of each temporary ensemble of reservoir model; g) determining at least one second decision for each of said temporary ensemble of reservoir model; h) calculating a VOI for each information scenario wherein the VOI is the difference of said utility function between said second decision of that information scenario and said first decision; i) comparing said VOI for each information scenario; j) selecting an information scenario that matches the one or more target percentiles of said ensemble of reservoir model, wherein if no information scenario matches the one or more target percentiles, then no selection is made. 7. The method of claim 6, further comprising step k) repeating steps c-j for said updated ensemble of reservoir model (E0). 8. The method of claim 6, wherein step 6.e) uses an ensemble Kalman filter (EnKF) to update said ensemble of reservoir model. 9. The method of claim 6, wherein said existing reservoir data consist of geological descriptions and/or engineering parameters. 10. The method of claim 6, wherein said existing reservoir data consists of core, well, and production logging data, production data, tracers, seismic data, and/or subsurface reservoir surveillance and illumination data. 11. The method of claim 6, wherein said reservoir data consists of porosity and permeability data. 12. The method of claim 6, wherein said system performance output variable is a financial variable. 13. The method of claim 12, wherein said financial variable is net present value, rate of return, asset value, and uncertainty band of these values. 14. A ensemble-based method for decision-making process, the method comprising: a) uploading existing reservoir data into a modeling program; b) generating and combining at least 2 realizations of said existing reservoir data to create an ensemble of reservoir model (E0), wherein said ensemble of reservoir model defines an input uncertainty band; c) deriving at least one system performance output variable and one or more target percentiles of said system performance output variable from an ensemble prediction of the ensemble of reservoir model; d) determining a first decision (D0) from said derived one or more target percentiles and a utility function for said ensemble of reservoir model; e) applying an ensemble Kalman filter to said ensemble of reservoir model to update said ensemble of reservoir model with possible outputs of at least one pre-determined information scenario to create at least one temporary ensemble of model (En), wherein the temporary ensemble of model has a smaller uncertainty band than E0; f) deriving at least one system performance output variable from an ensemble prediction of each temporary ensemble of reservoir model; g) determining at least one second decision for each of said temporary ensemble of model; h) calculating a VOI for each information scenario wherein the VOI is the difference of said utility function between said second decision of that information scenario and said first decision; i) comparing said VOI for each information scenario; j) selecting an information scenario that matches the one or more target percentiles of said ensemble of reservoir model, wherein if no information scenario matches the one or more target percentiles, then no selection is made; and k) applying said selected information scenario to said existing reservoir. 15. The method of claim 14, wherein said existing reservoir data consist of geological descriptions and/or engineering parameters. 16. The method of claim 14, wherein said existing reservoir data consists of core, well, and production logging data, production data, tracers, seismic data, and/or subsurface reservoir surveillance and illumination data. 17. The method of claim 14, wherein said reservoir data consists of porosity and permeability data. 18. The method of claim 14, wherein said system performance output variable is a financial variable. 19. The method of claim 18, wherein said financial variable is net present value, rate of return, asset value, and uncertainty band of these values.