Stochastic programming-based decision support tool for reservoir development planning

• US 8,775,361 B2
• Filed: 03/09/2009
• Issued: 07/08/2014
• Est. Priority Date: 04/21/2008
• Status: Active Grant

• Current Assignee: Exxonmobil Upstream Research Company (Exxon Mobil Corporation)
• Original Assignee: Exxonmobil Upstream Research Company (Exxon Mobil Corporation)
• Inventors: Goel, Vikas, Furman, Kevin C.
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Kim, David H
• Application Number:
  US12/864,987
• Publication Number:
  US 20100332442A1
• Time in Patent Office: 1,947 Days
• Field of Search: 703/10
• US Class Current: 706/52
• CPC Class Codes: G01V 20/00 Geomodelling in general
• CPC Class Codes: G01V 2210/667 Determining confidence or uncertainty in parameters
• CPC Class Codes: G06N 7/02 using fuzzy logic computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A stochastic programming-based decision support tool for reservoir development planning can comprise a source of input data, an optimization model, a high fidelity model for simulating the reservoir, and one or more solution routines interfacing with the optimization model. The optimization model can consider unknown parameters having uncertainties directly within the optimization model. The model incorporates the flexibility that a decision-maker has in the real world and allows the decision-maker to adjust the decisions based on new information. The model can systematically address uncertain data, for example comprehensively or even taking all uncertain data into account. Accordingly, the optimization model can provide flexible or robust solutions that remain feasible over an uncertainty space. Once the reservoir model is optimized, final development plans may be generated.

Claims

1. A computer-based method of optimizing development planning for a hydrocarbon reservoir, comprising:

providing input data representative of the hydrocarbon reservoir, the input data having an uncertainty associated therewith;

generating a first simulation of the reservoir using a high fidelity model for reservoir or surface facility behavior utilizing the input data, wherein the first simulation generates first high fidelity output data;

generating a low fidelity model for reservoir or surface facility behavior using the input data and the first high fidelity output data, wherein the low fidelity model generates a prediction;

optimizing a reservoir development planning model utilizing the input data and the low fidelity model for reservoir or surface facility behavior, wherein the reservoir development planning model generates reservoir development planning output data and the reservoir development planning model comprises a stochastic programming model that incorporates uncertainty;

performing a second simulation of the reservoir using the high fidelity model for reservoir or surface facility behavior utilizing the input data and the reservoir development planning output data, wherein the second simulation generates second high fidelity output data;

comparing the second high fidelity output data with the prediction of the low fidelity model;

repeating the steps comprising generating a low fidelity model for reservoir or surface facility behavior, optimizing a reservoir development planning model, performing a second simulation of the reservoir using the high fidelity model for reservoir or surface facility behavior, and comparing the second high fidelity output data with the prediction of the low fidelity model until the second high fidelity output data is substantially consistent with the prediction of the low fidelity model.

2. The method of claim 1, further comprising:

generating a development plan in response to the second high fidelity output data being substantially consistent with the prediction of the low fidelity model.

3. The method of claim 1, wherein a solution routine, interfacing with the stochastic programming model, assists the stochastic programming model to optimize development planning for the reservoir.

4. The method of claim 1, wherein the stochastic programming model comprises a solution routine for assisting with optimizing development planning for the reservoir.

5. The method of claim 1, wherein the input data includes one or more of fluid dynamics of the reservoir, size of the reservoir, a current state of reservoir development, a price of petroleum, a cost to drill into the reservoir, a cost of rig time for a rig to operate with the reservoir, and a capital cost relevant to drilling or managing the reservoir.

6. A method of producing hydrocarbons from a subterranean reservoir, comprising:

generating a reservoir development planning system based on input data representative of the reservoir, wherein the reservoir development planning system comprises;

the stochastic programming model comprising a low fidelity reservoir model; and

a high fidelity reservoir model, wherein the high fidelity model accepts one or more parameter input data from the stochastic programming model and provides one or more reservoir or surface facility property input data to the stochastic programming model;

optimizing the reservoir development planning system via a stochastic programming model and according to an uncertainty space; and

producing hydrocarbons from the reservoir according to output from the optimized reservoir development planning system.

7. The method of claim 6, wherein the input data comprises deterministic components and nondeterministic components, and

wherein the step of optimizing the reservoir development planning system according to the uncertainty space comprises considering each of the nondeterministic components with the stochastic programming model.

8. The method of claim 6, wherein the stochastic programming model incorporates uncertainty of the input data.

9. The method of claim 6, wherein the uncertainty space specifies inherent uncertainty of the input data.

10. The method of claim 6, wherein optimizing the reservoir development planning system comprises:

simulating operation of the reservoir using a high fidelity model; and

simulating operation of the reservoir using a low fidelity model.

11. The method of claim 6, wherein the data includes one or more of fluid dynamics of the reservoir, size of the reservoir, a current state of reservoir development, a price of petroleum, a cost to drill into the reservoir, a cost of rig time for a rig to operate with the reservoir, and a capital cost relevant to drilling or managing the reservoir.