| dc.description.abstract | Hydrocarbon use has been increasing and will continue to increase for the
foreseeable future in even the most pessimistic energy scenarios. Over the past few
decades, natural gas has become the major player and revenue source for many countries
and multinationals. Its presence and power share will continue to grow in the world
energy mix. Much of the current gas reserves are found in gas condensate reservoirs.
When these reservoirs are allowed to deplete, the pressure drops below the dew point
pressure and a liquid condensate will begin to form in the wellbore or near wellbore
formation, possibly affecting production.
A field optimization includes determining the number of wells, type (vertical,
horizontal, multilateral, etc.), trajectory and location of wells. Optimum well placement
has been studied extensively for oil reservoirs. However, well placement in gas
condensate reservoirs has received little attention when compared to oil. In most cases
involving a homogeneous gas reservoir, the optimum well location could be determined
as the center of the reservoir, but when considering the complexity of a heterogeneous reservoir with initial compositional variation, the well placement dilemma does not
produce such a simple result.
In this research, a horizontal well placement problem is optimized by using a
modified Genetic Algorithm. The algorithm presented has been modified specifically for
gas condensate reservoirs. Unlike oil reservoirs, the cumulative production in gas
reservoirs does not vary significantly (although the variation is not economically
negligible) and there are possibly more local optimums. Therefore the possibility of
finding better production scenarios in subsequent optimization steps is not much higher
than the worse case scenarios, which delays finding the best production plan. The second
modification is developed in order to find optimum well location in a reservoir with
geological uncertainties. In this modification, for the first time, the probability of success
of optimum production is defined by the user.
These modifications magnify the small variations and produce a faster
convergence while also giving the user the option to input the probability of success
when compared to a Standard Genetic Algorithm. | en |
