Production strategy optimization based on iterative discrete Latin hypercube


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.

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Designed exploration stage and controlled evolution


Discrete Latin hypercube sampling


Iterative discrete Latin hypercube sampling


Net present value


Objective function


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The authors are grateful for the support of the Center of Petroleum Studies (CEPETRO-UNICAMP/Brazil), the Department of Energy (DE-FEM-UNICAMP/Brazil), PETROBRAS S/A, Foundation CMG and Research Group in Reservoir Simulation and Management (UNISIM-UNICAMP/Brazil). In addition, special thanks to CMG for software licenses.

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Correspondence to João Carlos von Hohendorff Filho.

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Technical Editor: Celso Kazuyuki Morooka.

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von Hohendorff Filho, J.C., Maschio, C. & Schiozer, D.J. Production strategy optimization based on iterative discrete Latin hypercube. J Braz. Soc. Mech. Sci. Eng. 38, 2473–2480 (2016).

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  • Reservoir simulation
  • Production strategy
  • Optimization
  • Latin hypercube sampling