Production strategy optimization based on iterative discrete Latin hypercube

Abstract

This paper proposes a new iterative discrete Latin hypercube sampling based method to maximize the objective function (OF) in production strategy optimization. This methodology adequately treats posterior frequency distributions of discrete random variables and maximizes non-necessarily monotonic objective functions within discontinuous search spaces and many local optimums. To validate the method, we used an exhaustive process with an net present value (NPV) proxy, as the objective function, to be maximized. Using as an application case, the benchmark UNISIM-I-D reservoir model, based on Namorado field, Campos basin, Brazil, the method successfully maximized the NPV in the intermediate phase of production strategy optimization, and even compared favorably with a well-established optimization methodology. Population based optimization using discrete Latin hypercube sampling best suited this methodology, with consistent convergence to global optimum, few OF evaluations and the simultaneous multiple numeric reservoir simulations runs. This easy to use, reliable methodology with low computational time costs is an interesting option for optimization methods in problems of production strategy design related to the oil industry.