Permeability estimation in heterogeneous oil reservoirs by multi-gene genetic programming algorithm

  • Kaydani H
  • Mohebbi A
  • Eftekhari M
  • 18


    Mendeley users who have this article in their library.
  • 14


    Citations of this article.


Permeability estimation has a significant impact on petroleum fields operation and reservoir management. Different methods were proposed to measure this parameter, which some of them are inaccurate, and some others such as core analysis are cost and time consuming. Intelligent techniques are powerful tools to recognize the possible patterns between input and output spaces, which can be applied to predict reservoir parameters. This study proposed a new approach based on multi-gene genetic programming (MGGP) to predict permeability in one of the heterogeneous oil reservoirs in Iran. The MGGP model with artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and genetic programming (GP) model were used to predict the permeability and obtained results were compared statistically. The comparison of results showed that the MGGP model can be applied effectively in permeability prediction, which gives low computational time. Furthermore, one equation based on the MGGP model using well log and core experimental data was generated to predict permeability in porous media.

Author-supplied keywords

  • Core analysis
  • Multi-gene genetic programming
  • Porous media
  • Rock permeability

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free