Reach Us +44 7460312890


Artificial Neural Network (ANN) Prediction of Porosity and Water Saturation of Shaly Sandstone Reservoirs

This paper introduces a successful application of neural networks in predicting porosity, fluid saturation and identifying lithofacies using well log data. This technique utilizes the prevailing unknown nonlinear relationship in data between well logs and the reservoir properties, to determine accurately certain petrophysical properties of the reservoir rocks under different compaction conditions. In heterogeneous reservoirs classical methods face problems in determining the relevant petrophysical parameters accurately. Applications of artificial intelligence have recently made this challenge a possible practice. This paper presents successful achievement in applying two trained NN, one for porosity prediction and second training for one for water saturation using 5 log data inputs: (Gamma Ray)GR, (Laterolog Deep)LLD, (density) RHOB, (Neutron) NPHI.

Author(s): Hamada Ghareb M, Elsakka Ahmed and Nyein Chaw Y

Abstract | PDF

Share this  Facebook  Twitter  LinkedIn  Google+
30+ Million Readerbase
Recommended Conferences
Flyer image
Abstracted/Indexed in
  • Chemical Abstracts Service (CAS)
  • Index Copernicus
  • Google Scholar
  • Genamics JournalSeek
  • China National Knowledge Infrastructure (CNKI)
  • CiteFactor
  • Electronic Journals Library
  • Directory of Research Journal Indexing (DRJI)
  • WorldCat
  • Proquest Summons
  • Publons
  • Serials Union Catalogue (SUNCAT)
  • Geneva Foundation for Medical Education and Research
  • Secret Search Engine Labs