In
the development of
Maximus, considerable effort has been
directed towards making the algorithms numerically efficient with
fast execution. This effort
was expended for two principal reasons:
- To make feasible the solution of more complex and
computationally difficult problems (e.g. production optimisation
or stochastic analysis) without the need to ‘dumb down’
the physical models, for example by simplifying the equations
or by introducing approximate methods such as PVT lookup tables.
- To make feasible the solution of large
network systems also without the need to reduce the fidelity of
the physical models.
As
a result, Maximus is able to solve quite large systems very quickly. The following model shows a large gathering
network with 131 wells and 200 flowlines/pipeline branches. The production from these wells is all fed
to a Central Processing Facility. The network problem is completely pressure specified and
takes about 40 seconds to achieve the first solution (i.e. from
internal initial guesses) and about 10 seconds for subsequent simulations
on a standard Laptop with a 2 GHz Pentium 4 processor.
Example of a Large Gathering
Network System
