Gas production rate optimization by genetic algorithm

Guyaguler B., Gumrah F.

ENERGY SOURCES, vol.23, no.3, pp.295-304, 2001 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 23 Issue: 3
  • Publication Date: 2001
  • Doi Number: 10.1080/00908310151134040
  • Journal Name: ENERGY SOURCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.295-304
  • Middle East Technical University Affiliated: No


In order to satisfy the excessive demand during the heating days, the excessive supply during the nonheating days should be stored and used then. Underground gas storage is the process accomplishing this task. A detailed study of the reservoir and model construction will enable correct forecasts and thus the successful operation of the underground storage facility. In the event that the demand exceeds the field's top production capacity optimization has to be made in order to maximize the gas production. Genetic algorithm is used as the optimization tool in this study. A population is initially randomly generated and manipulated by processes analog to natural operators in the effort to find the optimum. Each population consists of individuals which each represent a different set of well production rates for the gas storage field. In this study, a real gas reservoir in Turkey having the potential for being an underground gels storage unit is evaluated. A preutilization design procedure is applied to determine the field's working renditions. interactive software (GASOPT) is developed which utilizes the three-dimensional gas reservoir simulator as the generic algorithms evaluation function. A probable demand curve is put forward. Forecast and optimization is made with GASOPT.