This paper discusses the application of an optimisation technique to manage a field consisting of wells with Intelligent Well Technology (IWT) completions that allow the operator to reconfigure well architecture without well intervention. There is currently no generally accepted, efficient, optimisation technique available to optimise the IWT operation so as to maximise well production. The Sequential Quadratic Programming (SQP) candidate technique has previously proved its value in small and large-scale optimisation problems involving large-scale (gas-lifted) well production networks. SQP's ability to solve this new non-linear problem in a relatively reasonable time will be explained in this paper.
In the previous publications we used a reservoir simulator controlled by a manual optimisation procedure to develop a value-statement for IWT. This method has proved it could increase value during the plateau period, but efficient optimisation of the decline period was too demanding in terms of engineering time & computer power. This later requires an automatic optimisation tool to be cost effective.
The Reservoir simulator has been linked to a (SQP based) Network optimiser so as to set the well and zone so that the production objectives are maximized, subject to the normal constraints (e.g. pressures, flow rates, water cuts etc. frequently imposed on the individual zonal or well production).
The cumulative production increase achieved with manual optimisation (compared to the field level control) was doubled once automatic optimisation with zone-level control was implemented for the real-field based example studied previously. Automatic optimisation did significantly improve tail-end production, but delivered its greatest value by extending the plateau period.
The results show that the use of automatic optimisation is valuable when building a "value statement" for the implementation of IWT within a particular field.
Production and Injection wells are usually connected together by a surface network contain pipes, chokes, pumps, etc. A number of studies have been performed in order to set the flow rate per well or per group of wells, different tools have been used to meet constraints inherent to this network of these facilities. Reservoir modellers may optimise rate allocation to optimally determine well rate setting so that network objectives and constraints are simultaneously satisfied.