Neural network application to petrophysical and lithofacies analysis based on multi-scale data: An integrated study using conventional well log, core and borehole image data

Abstract

Abstract Application of artificial neural network (ANN, e.g., Multi-layer perceptron, MLP) became widespread in the petroleum industry, especially in formation evaluation, reservoir characterization and modeling studies. In this study, the MLP technique is applied to estimate the lithofacies and petrophysical parameters including permeability and rock typing for the upper unit of Abu Roash G reservoir (ARG), Azhar field in Beni Suef basin. Major steps of the workflow started with lithofacies analysis using core and borehole image data to identify five lithofacies as follows; bioturbated mudstone, lenticular-bedded siltstone, massive laminated sandstone, flaser bedded sandstone, and faintly laminated sandstone, which were deposited in tidal conditions. A fully connected neural network was trained and tested on data for lithofacies prediction in wells with no core data and borehole image. Core permeability was estimated by using different variables including effective porosity, bulk density, volume of shale and volume of sand, it was found that neural network prediction for permeability was very satisfactory and can be applied on wider scale in other uncored wells. Rock typing was constructed in cored wells using several methods including porosity-permeability plot integrated with the Winland R35, flow zone indicator-reservoir quality index (FZI-RQI). Neural network classification method was trained and tested on data for rock type estimation in the uncored zones based on calculated petrophysical log data including permeability, flow zone indicator, reservoir quality index and effective porosity. To get more accurate predicted data, the log data was calibrated firstly using porosity and permeability core data.