Applying Genetic Programming to Reservoir History Matching Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Yu:2006:GPTP,
  author =       "Tina Yu and Dave Wilkinson and Alexandre Castellini",
  title =        "Applying Genetic Programming to Reservoir History
                 Matching Problem",
  booktitle =    "Genetic Programming Theory and Practice {IV}",
  year =         "2006",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  volume =       "5",
  series =       "Genetic and Evolutionary Computation",
  pages =        "187--201",
  address =      "Ann Arbor",
  month =        "11-13 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-387-33375-4",
  URL =          "http://www.cs.mun.ca/~tinayu/Publications_files/gptp2006.pdf",
  DOI =          "doi:10.1007/978-0-387-49650-4_12",
  size =         "14 pages",
  abstract =     "History matching is the process of updating a
                 petroleum reservoir model using production data. It is
                 a required step before a reservoir model is accepted
                 for forecasting production. The process is normally
                 carried out by flow simulation, which is very
                 time-consuming. As a result, only a small number of
                 simulation runs are conducted and the history matching
                 results are normally unsatisfactory.

                 In this work, we introduce a methodology using genetic
                 programming (GP) to construct a proxy for reservoir
                 simulator. Acting as a surrogate for the computer
                 simulator, the cheap GP proxy can evaluate a large
                 number (millions) of reservoir models within a very
                 short time frame. Collectively, the identified
                 good-matching reservoir models provide us with
                 comprehensive information about the reservoir.
                 Moreover, we can use these models to forecast future
                 production, which is closer to the reality than the
                 forecasts derived from a small number of computer
                 simulation runs.

                 We have applied the proposed technique to a West
                 African oil field that has complex geology. The results
                 show that GP is able to deliver high quality proxies.
                 Meanwhile, important information about the reservoirs
                 was revealed from the study. Overall, the project has
                 successfully achieved the goal of improving the quality
                 of history matching results without increasing the
                 number of reservoir simulation runs. This result
                 suggests this novel history matching approach might be
                 effective for other reservoirs with complex geology or
                 a significant amount of production data.",
  notes =        "part of \cite{Riolo:2006:GPTP} Published Jan 2007
                 after the workshop",
}

Genetic Programming entries for Tina Yu Dave Wilkinson Alexandre Castellini

Citations