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Determining Hydraulic Characteristics of Production Wells using Genetic Algorithm

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  • Madan Jha
  • Gaurav Nanda
  • Manoj Samuel

Abstract

Proper well management requires the determination of characteristic hydraulic parameters of production wells such as well loss coefficient (C) and aquifer loss coefficient (B), which are conventionally determined by the graphical analysis ofstep-drawdowntest data. However, in the present study, the efficacy of a non-conventional optimization technique called Genetic Algorithm (GA), which ensures near-optimal or optimal solutions, is assessedin determining well parameters from step-drawdown test data. Computer programs were developed to optimize the well parametersby GA technique for two cases: (i) optimization of ‘B’ and ‘C’ only, and (ii) optimization of ‘B’, ‘C’ and ‘p’ (exponent) as well as to evaluate the well condition. The reliability and robustness of the developed computer programs were tested usingnine sets of published and unpublished step-drawdown data from varying hydrogeologic conditions. The well parameters obtained by the GA technique were compared with those obtained by the conventional graphical method in terms of root mean square error(RMSE) and visual inspection. It was revealed that the GA technique yielded more reliable well parameters with significantlylow values of RMSE for almost all the datasets, especially in caseof three-variable optimization. The optimal values of the parameters‘B’, ‘C’ and ‘p’ for the nine datasets were found to range from 0.382 to 2.292 min m -2 , 0.091 to 3.262, and 1.8 to 3.6, respectively. Because of a wide variation of ‘p’, the GA techniqueresulted in considerably different but dependable and robust well parameters as well as well specific capacity and well efficiency compared to the graphical method. The condition of three wells was found to be ‘good’, one well ‘bad’ and that of the remaining five wells ‘satisfactory’. The performance evaluation of the developed GA code indicated that a proper selection of generation number and population size is essential to ensure efficient optimization. Furthermore,a sensitivity analysis of the obtained optimal parameters demonstrated that the GA technique resulted in a unique set ofthe parameters for all the nine datasets. It is concluded thatthe GA technique is an effective and reliable numerical tool for determining the characteristic hydraulic parameters of production wells. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Madan Jha & Gaurav Nanda & Manoj Samuel, 2004. "Determining Hydraulic Characteristics of Production Wells using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(4), pages 353-377, August.
    • Handle: RePEc:spr:waterr:v:18:y:2004:i:4:p:353-377
    DOI: 10.1023/B:WARM.0000048485.62254.1c
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    References listed on IDEAS

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    1. Dragan Savic & Godfrey Walters & James Davidson, 1999. "A Genetic Programming Approach to Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 219-231, June.
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    Cited by:

    1. Dedi Liu & Xiaohong Chen & Zhanghua Lou, 2010. "A Model for the Optimal Allocation of Water Resources in a Saltwater Intrusion Area: A Case Study in Pearl River Delta in China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 63-81, January.
    2. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.

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