Morales, Adrian (2010-12). A Modified Genetic Algorithm Applied to Horizontal Well Placement Optimization in Gas Condensate Reservoirs. Master's Thesis. Thesis uri icon

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.
  • 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.

publication date

  • December 2010