Porosity Prediction Using Neural Network Based on Seismic Inversion and Seismic Attributes

Taufik Mawardi Sinaga, M. Syamsu Rosid, M. Wahdanadi Haidar

Research output: Contribution to journalConference articlepeer-review

7 Citations (Scopus)


It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field "T". The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05-0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.

Original languageEnglish
Article number15006
JournalE3S Web of Conferences
Publication statusPublished - 28 Oct 2019
Event4th International Conference on Energy, Environment, Epidemiology and Information System, ICENIS 2019 - Semarang, Indonesia
Duration: 7 Aug 20198 Aug 2019


  • Multi-attribute
  • Porosity
  • Probabilistic Neural Network
  • Seismic Attributes


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