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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 to

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.

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1. A method for identifying an optimum allocation of an applied resource throughout a well network, comprising: validating, according to a predefined schedule, well test data obtained from one or more wells of the well network by data conditioning and quality checking to generate validated well test

1. A method for identifying an optimum allocation of an applied resource throughout a well network, comprising: validating, according to a predefined schedule, well test data obtained from one or more wells of the well network by data conditioning and quality checking to generate validated well test data;updating, using a processor and according to the predefined schedule, a network model of the well network based on the validated well test data to generate an updated network model, wherein the well network comprises the one or more wells and a surface network,wherein the network model comprises static network parameters, the static network parameters comprising a boundary constraint at sources and sinks of the well network and a fluid composition in the one or more wells, andwherein the boundary constraint and the fluid composition are updated based on the validated well test data;updating, in response to a sensitivity analysis of a well test indicating a change in well performance, a well model comprised in the network model based on the validated well test data to generate an updated well model in the updated network model, wherein the sensitivity analysis is performed using the well model to predict a parameter value of the well test based on other parameters of the well test, andwherein the well model comprises a lift configuration of the one or more wells and a plurality of performance curves that relate well performance to one or more levels of the applied resource;diagnosing, according to the predefined schedule, the lift configuration of the one or more wells based on the validated well test data and the updated network model to generate a diagnosed lift configuration; anddetermining, using the processor and according to the predefined schedule, the optimum allocation of the applied resource using the updated network model based at least on the diagnosed lift configuration to maximize an operating parameter of the well network, including:converting a portion of the updated network model having a linear inequality relationship to a modified portion having a single variable and a linear equality constraint; andsolving the modified portion using a modified Newton's method, wherein solving the modified portion using the modified Newton's method comprises determining one or more inverse derivative curves in order to solve a plurality of Karush-Kuhn-Tucker (KKT) conditions for optimality directly. 2. The method of claim 1, wherein determining the 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; andoptimally allocating the applied resource among the one or more wells according to a constrained non-linear problem. 3. The method of claim 1, wherein determining the optimum allocation of the applied resource further includes: coupling an offline solution result with an online solution result. 4. 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. 5. 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 the optimum allocation of the applied resource includes: determining an optimum allocation of a lift gas. 6. 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 the optimum allocation of the applied resource includes: determining an optimum allocation of at least one of an electrical power or an injected material. 7. 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 the 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; anddetermining a second optimum allocation of a second applied resource for the one or more wells of the second type. 8. One or more non-transitory computer-readable media containing computer-readable instructions that, when executed by a processor, perform a method comprising: validating, according to a predefined schedule, well test data obtained from one or more wells of a well network by data conditioning and quality checking to generate validated well test data;updating, according to the predefined schedule, a network model of the well network based on the validated well test data to generate an updated network model, wherein the well network comprises the one or more wells and a surface network,wherein the network model comprises static network parameters, the static network parameters comprising a boundary constraint at sources and sinks of the well network and a fluid composition in the one or more wells, andwherein the boundary constraint and the fluid composition are updated based on the validated well test data;updating, in response to a sensitivity analysis of a well test indicating a change in well performance, a well model comprised in the network model based on the validated well test data to generate an updated well model in the updated network model, wherein the sensitivity analysis is performed using the well model to predict a parameter value of the well test based on other parameters of the well test, andwherein the well model comprises a lift configuration of the one or more wells anda plurality of performance curves that relate performance of the one or more wells to one or more levels of an applied resource;diagnosing, according to the predefined schedule, the lift configuration of the one or more wells based on the validated well test data and the updated network model to generate a diagnosed lift configuration; andcomputing, according to the predefined schedule, an optimum allocation of the applied resource using the updated network model based at least on the diagnosed lift configuration to maximize an operating characteristic of the well network, including: converting a portion of the updated network model having a linear inequality relationship to a modified portion having a single variable and a linear equality constraint; andsolving the modified portion to determine the optimum allocation of the applied resource using a modified Newton's method, wherein solving the modified portion using the modified Newton's method comprises: determining one or more inverse derivative curves in order to solve a plurality of Karush-Kuhn-Tucker (KKT) conditions for optimality directly. 9. The one or more computer-readable media of claim 8, wherein computing the 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;optimally allocating the applied resource among the one or more wells according to a constrained non-linear problem. 10. The one or more computer-readable media of claim 8, wherein computing the optimum allocation of the applied resource further includes: coupling an offline solution result with an online solution result. 11. The one or more computer-readable media of claim 8, wherein the applied resource includes at least one of a lift gas, an electrical power, and an injection chemical. 12. A method for performing operations of an oilfield having at least one wellsite comprising: obtaining a well model comprising a lift configuration of the at least one wellsite and a plurality of performance curves that relate well performance to one or more levels of a lift resource; andoptimally allocating, using a processor, the lift resource to generate a lift resource allocation for the at least one wellsite to maximize a production rate at a sink,wherein optimally allocating the lift resource comprises: converting a portion of the well model having a linear inequality relationship to a modified portion having a single variable and a linear equality constraint; andsolving the modified portion using a modified Newton's method, wherein solving the modified portion using the modified Newton's method comprises determining one or more inverse derivative curves in order to solve a plurality of Karush-Kuhn-Tucker (KKT) conditions for optimality directly. 13. The method of claim 12, further comprising: selectively performing, according to a predefined schedule, corrective action based on a diagnosis of the lift configuration,wherein diagnosing the lift configuration comprises: verifying whether injection timing and injection pressure of a gas lift valve operates as configured; andverifying whether gas is being injected into a correct gas lift valve based on current operating conditions. 14. The method of claim 12, further comprising: monitoring the production rate to generate an actual production rate; andgenerating 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 based on the diagnosis of the lift configuration and the lift resource allocation,wherein at least one selected from a group consisting of the diagnosis of the lift configuration, the lift resource allocation, and the actual production rate are generated according to a pre-determined schedule.