Prediction of Porosity and Water Saturation Using Neural Networks in Shaly Sand Reservoirs, Western Deseret, Egypt | ||||
| Journal of Petroleum and Mining Engineering | ||||
| Article 9, Volume 22, Issue 2, December 2020, Page 80-91 PDF (444.47 K) | ||||
| Document Type: Full-length article | ||||
| DOI: 10.21608/jpme.2020.36116.1040 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ghareb Hamada  1; Ahmed Sakka2; Chaw Nyein2
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| 1Petroleum, American University of Kurdistan, Kurdistan, Iraq | ||||
| 2University Teknologi Petronas, Malaysia | ||||
| Abstract | ||||
| Petrophysical properties evaluation of shaly sandstone reservoirs is a challenging task in comparison to clean sand reservoirs. Logging derived porosity in shaly sands requires shale correction and Archie’s formula cannot be used in shaly sands for the determination of water saturation, therefore many water saturation models were proposed to get accurate water saturation of shaly sand reservoirs. In this paper, three water saturation models were used; two empirical models (Simandoux and total shale) and one theoretical model (effective medium model). Shale corrected density log was used in all models. The use of computer-generated algorithm, fuzzy log neural network is of increasing interest in the petroleum industry. This paper presents artificial neural network (ANN) as an effective tool for determining porosity and water saturation in shaly sand reservoir using well logging data. ANN technique utilizes the prevailing unknown nonlinear relationship in data between input logging data and output petrophysical parameters. Results of this work showed that ANN can be supplement or replacement of the existing conventional techniques to determine porosity and water saturation using empirical or theoretical water saturation models. Two neural networks were presented to determine porosity and water saturation using GR, resistivity and density logging data and adapted cut off for porosity and water saturation. Water saturation and porosity were determined using conventional techniques and neural network approach for two wells in a shaly sand reservoir. Neural network approach was trained for porosity and water saturation using the available well logging data. The predicted porosity and water saturation values have shown good matching with the core data in the two wells in comparison to the porosity and water saturation derived from the conventional techniques. This work showed that developed neural network (ANN) could provide an accurate porosity and water saturation in shaly sands reservoirs, it is subject to volume of available well logging data. | ||||
| Keywords | ||||
| shaly sand reservoir; neural network; porosity; water saturation | ||||
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