Using genetic algorithms for reservoir characterisation

Reservoir characterisation is the process of describing a hydrocarbon reservoir, in terms of the parameters of a numerical model, so that its performance can be predicted. We describe the use of a specially designed genetic algorithm to search for the reservoir description that is most likely to match the measurements made on the reservoir. The genetic algorithm uses six separate chromosomes for different types of reservoir parameters. Three of the chromosomes have multi-dimensional real number structures, while the other three chromosomes are one-dimensional binary bit arrays. Specially designed crossover and mutation operators have been created to work with the non-standard genome structure. The method has been tested on a realistic, complex synthetic reservoir model, and compared with a simulated annealing (SA) algorithm. We have shown that our genetic algorithm produces better results than the simulated annealing algorithm and results which are comparable to what might be achieved by hand. Also, we have shown that the performance of the genetic algorithm is robust to the details of how it was set up. Given the ease with which the method can be cheaply parallelised, its robustness to lost or corrupted solutions, and that it returns a suite of good solutions, it is an ideal method to implement as an automatic reservoir characterisation algorithm.

[1]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[2]  Jonathan Carter,et al.  Genetic Algorithms for Incore Fuel Management and Other Recent Developments in Optimisation , 2002 .

[3]  Jeffery Lewins,et al.  Advances in Nuclear Science and Technology , 1983 .

[4]  A. R. Syversveen,et al.  Methods for quantifying the uncertainty of production forecasts: a comparative study , 2001, Petroleum Geoscience.

[5]  John R. Koza,et al.  Building a Parallel Computer System for $18, 000 that Performs a Half Peta-Flop per Day , 1999, GECCO.

[6]  L. D. Whitley.  A free lunch proof for Gray versus Binary encodings , 1999 .

[7]  L. Darrell Whitley,et al.  Building Better Test Functions , 1995, ICGA.

[8]  Dean S. Oliver,et al.  Conditioning Permeability Fields to Pressure Data , 1996 .

[9]  W. Spears,et al.  On the Virtues of Parameterized Uniform Crossover , 1995 .

[10]  Huang,et al.  AN EFFICIENT GENERAL COOLING SCHEDULE FOR SIMULATED ANNEALING , 1986 .

[11]  LarrañagaP.,et al.  Genetic Algorithms for the Travelling Salesman Problem , 1999 .

[12]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[13]  T. Deschamps,et al.  The Results of Testing Six Different Gradient Optimisers on Two History Matching Problems , 1998 .

[14]  Eric R. Ziegel.  Statistics for Petroleum Engineers and Geoscientists , 1999, Technometrics.

[15]  Terence C. Fogarty,et al.  Use of the Genetic Algorithm for Load Balancing of Sugar Beet Presses , 1995, ICGA.

[16]  Robert Bissell,et al.  Calculating Optimal Parameters for History Matching , 1994 .

[17]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[18]  Dean S. Oliver,et al.  Conditioning Geostatistical Models to Two-Phase Production Data , 1999 .

[19]  Ian C. Parmee,et al.  Preliminary airframe design using co-evolutionary multiobjective genetic algorithms , 1999 .

[20]  M. Boucher,et al.  Interpretation of Interference Tests in a Well Field Using Geostatistical Techniques to Fit the Permeability Distribution in a Reservoir Model , 1984 .

[21]  Tom Manzocchi,et al.  Fault transmissibility multipliers for flow simulation models , 1999, Petroleum Geoscience.

[22]  John Skilling,et al.  Data analysis : a Bayesian tutorial , 1996 .