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Integrated Production Strategy Optimization Based On Iterative Discrete Latin Hypercube
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery, Sep 2018, Volume 2018, p.1 - 15
Abstract
Most problems of production strategy optimization in the oil industry are characterized by a large number of discrete random variables in discontinuous search spaces with non-necessarily monotonic objective functions, usually net present value or oil recovery, with many local maximums within a maximization problem. It demands a large number of simulations to adequately evaluate search space, what becomes more complex when integrating reservoir and production system. This paper evaluates a new iterative discrete Latin Hypercube (IDLHC) sampling based method to maximize the objective function in integrated production strategy optimization.
Inside decision making study, to evaluate the best placement of wells in the reservoir, we have used an optimization process evaluating some objective function. We compared the optimization between the IDLHC and the genetic algorithm method. We used both methodologies to maximize the net present value objective function for the same variable set and search space. We used the benchmark case UNISIM-II-D (carbonate field in Brazil) reservoir model as an application case. And we applied our explicit methodology to integrate reservoir and production system simulators during optimization process.
IDLHC adequately treated posterior frequency distributions of discrete random variables and maximizes nonnecessarily monotonic objective functions within great discontinuous search space and many local optimums set by the well placement problem.
Population based optimization using iterative discrete Latin Hypercube sampling best suited this problem, with consistent convergence to global optimum, few objective function evaluations and the simultaneous multiple numeric reservoir simulations runs.
The IDLHC method showed the advantage of being a simple methodology to maximize the objective function, reducing the search space gradually with each iteration, while addressing posterior frequency distributions of discrete variable levels.
The method successfully maximized the net present value in the well placement step of production strategy optimization, and more faster when compared with a well-established optimization methodology (genetic algorithm).
This easy to use, reliable methodology with lower computational time costs is an interesting option for optimization methods in problems of integrated production strategy design related to the oil industry.