The current oil price scenario is strengthening industry's attention towards a more efficient exploitation of resources. Low rates of return and marginal new field economics enhance the need for existing assets production optimization, especially for deep-water applications. This paper shows the results obtained from a real application on a deep-water asset of an innovative tool for the integrated production optimization of surface facilities based on a genetic algorithm. The described tool, coupled with its fluid-dynamic check workflow, has been applied to investigate the optimum configuration of the asset.
The presented tool integrates well performances, gathering system calculation, and process plant simulation in order to optimize the field configuration with a global perspective. Conflicts and interactions between variables, constraints, and operational limitations are balanced and solved holistically by the optimization tool.
The tool computes the "fitness" of each solution, combines the properties of them in order to obtain new candidate solutions, and then selects the best individuals to allow the evolution of the population and detect the optimum.
The optimum configuration identified by the tool has been tested with a fluid-dynamic simulator in order to check its stability and identify the best operating procedure to reach the optimized configuration.
The integrated production optimization tool has been applied on a FPSO with the aim to increase the production of the assets respecting all the operative and flow-assurance constraints typical of a deepwater application.
The subsea network consists of seven oil wells connected to an FPSO with two identical parallel flowlines. Subsea manifolds allow to produce each well either through a flowline or the other. Furthermore, additional four wells are connected through a separate flowline to the same FPSO.
Wells re-routing is treated by the tool as an optimization variable, allowing to explore all the possible available network configurations simultaneously.
Additionally, a fluid-dynamic check workflow was implemented, in order to define the best procedure to switch from the current configuration to the optimized one, respecting all system constraints also during transient conditions.
Finally, the optimization actions identified by the tool has been implemented successfully in the field.
The paper describes an innovative approach for production optimization on a deep-water application. The integrated production optimization tool was able to explore all the possible network configuration simultaneously, thanks to the limited computational efforts required by the tool. Field application showed its benefits in terms of production enhancement, respecting all system constraints without running into any flow-assurance issue typical for deep-water. The first deepwater application of the tool confirms its robustness for production optimization purposes.
