A general modelling framework for optimization of multiphase flow networks with discrete decision variables is presented. The framework is expressed with the graph, and special attention is given to the convexity properties of the resulting programming formulation. Nonlinear pressure and temperature relations are modelled using multivariate splines and a special mixed-integer nonlinear programming (MINLP) formulation with spline constraints results. A global solution method is devised by combining the framework with a spline-compatible MINLP solver, recently presented in the literature. The solver is able to globally solve the nonconvex optimization problem. The new solution method is benchmarked with several local optimization methods on a set of three realistic subsea production optimization cases provided by the oil company BP.
Managing and optimising BP's integrated operations presents an increasingly complex challenge with the move to deeperwater production and the use of sub-sea processing and multiphase tiebacks from satellite fields. Production system optimisation requires the simultaneous assessment of flow stability and well draw-down maximisation whilst honouring the constraints of reserves recovery though the reservoir management depletion strategy.BP has a strong track record of applying fundamental mechanistic models to support operational decision making and optimisation. When reconciled with real-time and historical production data, physical models of a production system increase operators understanding of the system and allow changes to operating strategy to be made with increased confidence. Moreover, inferred model parameters can be use to track the health and efficiency of component parts of the system feeding an enhanced predictive maintenance capability.Mechanistic models of a production system are themselves complex and challenging to use and maintain. Normally their use is restricted to modelling experts primarily for design purposes or to support troubleshooting following a production upset condition. Delivering such models for use within an operational environment requires a large number of issues to be addressed, in particular how such models can be applied quickly and robustly with assurance, and how they are scoped and maintained.This paper outlines how BP E&P is tackling these issues through the Field of the Future TM Model Based Operational Support programme as the capability is deployed further within the Upstream organisation.
Recent technological advances in the oil and gas industry such as extended reach drilling, subsea processing and tiebacks from satellite fields have added to the complexity of daily operations. With a myriad of flow conditions, the dynamic interactions between different components of the gathering system can be significant. Deciphering these flow interactions can be difficult. Optimising them is a real challenge. Whilst steady-state and dynamic modelling can provide a valuable insight into the occurring flow dynamics, the limitations of multiphase modelling and the level of reservoir uncertainty renders it difficult to determine an "optimal" operating point. Within BP, an alternative paradigm for system optimisation has evolved from the desire to generate an operability map of the process. By combining data analytics with first principle-modelling and visualisation techniques, it is possible to generate operating maps for our oil and gas installations that are analogous to the "weather map". Process mapping provides a valuable insight into the operability of the process that can be accessed in a quick and easy manner. Operators can immediately relate to the encapsulated information by comparing their own observations with those plotted in front of them. Over time their knowledge of the process is enhanced and the process is manoeuvred towards the "optimal point". Process mapping has led to significant increases in production within BP. Expressing the virtual insight of a model in a format that both the onshore and offshore teams can relate to has naturally led to an impressive level of asset engagement. In this paper, the people, process and technical implications of process mapping will be discussed alongside the value experienced to date.
Managing and optimising BP's integrated operations presents an increasingly complex challenge with the move to deeperwater production and the use of sub-sea processing and multiphase tiebacks from satellite fields. Production system optimisation requires the simultaneous assessment of flow stability and well draw-down maximisation whilst honouring the constraints of reserves recovery though the reservoir management depletion strategy.BP has a strong track record of applying fundamental mechanistic models to support operational decision making and optimisation. When reconciled with real-time and historical production data, physical models of a production system increase operators understanding of the system and allow changes to operating strategy to be made with increased confidence. Moreover, inferred model parameters can be use to track the health and efficiency of component parts of the system feeding an enhanced predictive maintenance capability.Mechanistic models of a production system are themselves complex and challenging to use and maintain. Normally their use is restricted to modelling experts primarily for design purposes or to support troubleshooting following a production upset condition. Delivering such models for use within an operational environment requires a large number of issues to be addressed, in particular how such models can be applied quickly and robustly with assurance, and how they are scoped and maintained.This paper outlines how BP E&P is tackling these issues through the Field of the Future TM Model Based Operational Support programme as the capability is deployed further within the Upstream organisation.
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