METHODS AND SYSTEMS FOR PERFORMING OILFIELD PRODUCTION OPERATIONS

• US 20100042458A1
• Filed: 08/04/2009
• Published: 02/18/2010
• Est. Priority Date: 08/04/2008
• Status: Active Grant

• Current Assignee: Schlumberger Technology Corporation (Schlumberger NV)
• Original Assignee: Schlumberger Technology Corporation (Schlumberger NV)
• Inventors: Tonkin, Trevor Graham, Huber, Michael, Shand, Andrew Michael, Lucas-Clements, Daniel Colin Nesbitt, Hallquist, Aron Edward, Rashid, Kashif
• Granted Patent: US 8,670,966 B2
• Time in Patent Office: Days
• US Class Current: 705/8
• CPC Class Codes: E21B 43/122 Gas lift
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

Methods and systems for performing well network production optimizations are described. For example, in one embodiment, a method of allocating an applied resource throughout a well network includes receiving topological data into an analytical model of a well network having one or more wells. The topological data includes a plurality of performance curves that relate well performance to one or more levels of an applied resource. The method also includes determining an optimum allocation of the applied resource using the analytical model to maximize an operating parameter of the well network, including converting a portion the analytical model having one or more wells and a linear inequality relationship to a modified portion having a single variable and a linear equality constraint.

Claims

1. A method of allocating an applied resource throughout a well network, comprising:

receiving topological data into an analytical model of a well network having one or more wells, the topological data including a plurality of performance curves that relate well performance to one or more levels of an applied resource; and

determining an optimum allocation of the applied resource using the analytical model to maximize an operating parameter of the well network, including;

converting a portion the analytical model having one or more wells and a linear inequality relationship to a modified portion having a single variable and a linear equality constraint.

2. The method of claim 1, wherein determining an optimum allocation of the applied resource further includes:

solving the modified portion using a modified Newton'"'"'s method.

3. The method of claim 2, wherein solving the modified portion using a modified Newton'"'"'s method includes:

determining one or more inverse derivative curves in order to solve a plurality of Karush-Kuhn-Tucker (KKT) conditions for optimality directly.

4. The method of claim 1, wherein determining an optimum allocation of the applied resource further includes:

solving the modified portion using a genetic algorithm.

5. The method of claim 1, wherein determining an optimum allocation of the applied resource further includes:

establishing an operating curve for each of the one or more wells based on at least one of the plurality of performance curves or the topological data;

optimally allocating the applied resource among the one or more wells according to a constrained non-linear problem; and

after converting, solving the modified problem using a modified Newton'"'"'s method, including;

treating the linear equality constraint as an equality; and

determining one or more inverse derivative curves in order to solve a plurality of Karush-Kuhn-Tucker (KKT) conditions for optimality directly.

6. The method of claim 1, wherein determining an optimum allocation of the applied resource further includes:

coupling an offline solution result with an online solution result.

7. The method of claim 1, wherein the linear equality constraint includes at least one of a total applied resource constraint or a total produced applied resource constraint.

8. The method of claim 1, wherein the one or more wells of the well network include at least one gas-lift well, and wherein determining an optimum allocation of the applied resource includes:

determining an optimum allocation of a lift gas.

9. The method of claim 1, wherein the one or more wells of the well network include at least one electrically-driven well, and wherein determining an optimum allocation of the applied resource includes:

determining an optimum allocation of at least one of an electrical power or an injected material.

10. The method of claim 1, wherein the one or more wells of the well network include one or more wells of a first type and one or more wells of a second type, and wherein determining an optimum allocation of the applied resource includes:

determining a first optimum allocation of a first applied resource for the one or more wells of the first type; and

determining a second optimum allocation of a second applied resource for the one or more wells of the second type.

12. The one or more computer-readable media of claim 1, wherein solving the modified portion to determine the optimum allocation of the applied resource includes:

solving the modified portion using a modified Newton'"'"'s method.

13. The one or more computer-readable media of claim 12, wherein solving the modified portion using a modified Newton'"'"'s method includes:

determining one or more inverse derivative curves in order to solve a plurality of Karush-Kuhn-Tucker (KKT) conditions for optimality directly.

14. The one or more computer-readable media of claim 1, wherein solving the modified portion to determine the optimum allocation of the applied resource includes:

solving the modified portion using a genetic algorithm.

11. One or more computer-readable media containing computer-readable instructions that, when executed, perform a method comprising:

communicating topological data into a computational model of a well network having one or more wells, the topological data including performance data that relate performance of the one or more wells to one or more levels of an applied resource; and

computing an optimum allocation of the applied resource using the computational model to maximize an operating characteristic of the well network, including;

converting a portion the computational model having one or more wells and a linear inequality relationship to a modified portion having a single variable and a linear equality constraint; and

solving the modified portion to determine the optimum allocation of the applied resource.

15. The one or more computer-readable media of claim 11, wherein computing an optimum allocation of the applied resource further includes:

establishing an operating curve for each of the one or more wells based on at least one of the plurality of performance curves or the topological data;

optimally allocating the applied resource among the one or more wells according to a constrained non-linear problem; and

after converting, solving the modified portion using a modified Newton'"'"'s method, including;

treating the linear equality constraint as an equality; and

determining one or more inverse derivative curves in order to solve a plurality of Karush-Kuhn-Tucker (KKT) conditions for optimality directly.

16. The one or more computer-readable media of claim 11, wherein computing an optimum allocation of the applied resource further includes:

coupling an offline solution result with an online solution result.

17. The one or more computer-readable media of claim 11, wherein the applied resourse includes at least one of a lift gas, an electrical power, and an injection chemical.

18. A method for performing operations of an oilfield having at least one wellsite and a surface network operatively connected thereto, each at least one wellsite having a wellbore penetrating a subterranean formation for extracting fluid from an underground reservoir therein, the method comprising:

validating well test data obtained from the at least one wellsite to generate validated well test data;

updating at least a well model comprised in a network model based on the validated well test data to generate an updated network model for modeling a network, wherein the network comprises the at least one wellsite and the surface network;

diagnosing a lift configuration of the at least one wellsite based on the validated well test data and the updated network model to generate a diagnosed lift configuration; and

optimally allocating a lift resource to generate a lift resource allocation for the at least one wellsite based on the validated well test data, the updated network model, and the diagnosed lift configuration to maximize a production rate at a sink of the network.

19. The method of claim 18, further comprising:

selectively performing corrective action based on the diagnosed lift configuration.

20. The method of claim 18, further comprising:

monitoring the production rate to generate an actual production rate; and

generating an alarm upon the actual production rate deviating from a projected production rate to exceed a pre-determined threshold, wherein the projected production rate is generated by the updated network model based on the diagnosed lift configuration and the lift resource allocation,

wherein at least one selected from a group consisting of the validated well test data, the updated network model, the diagnosed lift configuration, the lift resource allocation, and the actual production rate are generated according to a pre-determined schedule.