Amiri, M., Ghiasi-Freez, J., Golkar, B. and Hatampourd, A. (2015) Improving Water Saturation Estimation in a Tight Shaly Sandstone Reservoir Using Artificial Neural Network Optimized by Imperialist Competitive Algorithm—A Case Study. Journal of Petroleum Science and Engineering, 127, 347-358.
https://doi.org/10.1016/j.petrol.2015.01.013

has been cited by the following article:

• TITLE: New Methods to Calculate Water Saturation in Shale and Tight Gas Reservoirs

  AUTHORS: Arash Kamari, James J. Sheng

  KEYWORDS: Shale, Petrophysical Properties, Water Saturation, GEP, LSSVM

  JOURNAL NAME: Open Journal of Yangtze Oil and Gas, Vol.3 No.3, July 17, 2018

  ABSTRACT: The determination of water saturation is a key step for the reservoir characterization and prediction of future reservoir performance in terms of production. The importance of water saturation has been further identified when the reservoirs refer to rocks with low porosity and permeability such as shale and tight formations. In this communication, two advanced artificial intelligence strategies consisting of least square support vector machine (LSSVM) and gene expression programming (GEP) have been applied in order to develop reliable predictive models for the calculation of water saturation of shale and tight reservoirs. To this end, an extensive core and log data bank has been analysed from 12 wells of a Mesaverde group tight reservoir located in the largest Western US. The results indicate that the estimated water saturation data by the models developed in this study are in satisfactory agreement with the actual log data. Furthermore, new methods proposed in this study are useful for the characterization of shale and tight reservoirs and can be applied to the relevant software.