Aplication of genetics algorithms as an optimization tool in optimizing infill wells and the development of channels in fluvial deposit reservoirs

  • Héctor Hugo Pérez Vega Ecopetrol S.A. – Instituto Colombiano del Petróleo, A.A. 4185 Bucaramanga, Santander, Colombia
  • Eduardo Alejandro Idrobo UIS – Universidad Industrial de Santander - Grupo de Modelamiento de Procesos Hidrocarburos (GMPH), Bucaramanga, Santander
  • Nicolás Santos Santos UIS – Universidad Industrial de Santander - Grupo de Modelamiento de Procesos Hidrocarburos (GMPH), Bucaramanga, Santander
Keywords: genetic algorithm, geostatistical, fluvial reservoir


Optimization of the exploitation of an oil field requires implementing the most advanced techniques aimed at the increase of its production. Among them, drilling new development wells (infill wells) stands out. Defining the most adequate location for such wells is a complex process, due to diverse geological characteristics of the reservoir, and to the high uncertainty associated to the spatial distribution of the hydrocarbon storing flow units. This article presents the development of an alternative and innovative simulation alternative, which allows locating flow channels by means of integrating geo-statistical modeling and evolutional computation. The architecture of the geological model is defined by variables which are coded in a binary system, which represent the chromosomes of the genetic algorithm, and represent the characteristic facies of a reservoir with fluvial origin (channel sand, point bars, natural levee, crevasse splay, and floodplain shale). As the product of the genetic algorithm optimization, a facies model is obtained, in which the best channel layout is obtained within the reservoir, allowing better knowledge of the spatial distribution of flow units and the hydrocarbon accumulation zones. The correct implementation of this simulation tool facilitates the location of the most adequate sites for the implementation of infill well drilling, new zone perforating, re - perforating programs and enhanced oil recovery process, carrying to maximization of hydrocarbon recovery factor in mature reservoirs.


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How to Cite
Pérez Vega, H. H., Idrobo, E. A., & Santos Santos, N. (2005). Aplication of genetics algorithms as an optimization tool in optimizing infill wells and the development of channels in fluvial deposit reservoirs. CT&F - Ciencia, Tecnología Y Futuro, 3(1), 139–149. https://doi.org/10.29047/01225383.512


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