Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods

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• 作者：Hadi Fattahi ; Sadegh Karimpouli
• 关键词：Porosity ; Water saturation ; Carbonate reservoir ; Support vector regression ; Particle swarm optimization ; ANFIS ; subtractive clustering method ; Bayesian inversion
• 刊名：Computational Geosciences
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：20
• 期：5
• 页码：1075-1094
• 全文大小：4,479 KB
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Mathematical Modeling and IndustrialMathematics
  Geotechnical Engineering
  Hydrogeology
  Soil Science and Conservation
  
• 出版者：Springer Netherlands
• ISSN：1573-1499
• 卷排序：20

文摘

Rock physical parameters such as porosity and water saturation play an important role in the mechanical behavior of hydrocarbon reservoir rocks. A valid and reliable prediction of these parameters from seismic data is essential for reservoir characterization, management, and also geomechanical modeling. In this paper, the application of conventional methods such as Bayesian inversion and computational intelligence methods, namely support vector regression (SVR) optimized by particle swarm optimization (PSO) and adaptive network-based fuzzy inference system-subtractive clustering method (ANFIS-SCM), is demonstrated to predict porosity and water saturation. The prediction abilities offered by Bayesian inversion, SVR-PSO, and ANFIS-SCM were presented using a synthetic dataset and field data available from a gas carbonate reservoir in Iran. In these models, seismic pre-stack data and attributes were utilized as the input parameters, while the porosity and water saturation were the output parameters. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-SCM model has strong potential for indirect estimation of porosity and water saturation with high degree of accuracy and robustness from seismic data and attributes in both synthetic and real cases of this study.