Prediction of porosity and water saturation using a probabilistic neural network for the Bahariya Formation, Nader Field, north western desert, Egypt

Abstract

Predicting petrophysical parameters, particularly saturation, is a common challenge due to the lack of direct relationships with seismic elastic attributes. Therefore, in this paper, we used one of the Artificial Intelligence (AI) algorithms for seismic reservoir characterization to overcome this challenge. A new trend is the use of Artificial Neural Network (ANN) algorithms to predict reservoir characteristics. The capacity to construct nonlinear relationships between petrophysical logs and seismic data is the fundamental advantage of the ANN algorithm. A probabilistic neural network (PNN) was the selected algorithm. The input data includes seismic full-stack and log data for four wells. Multi-attribute linear regression and PNN were applied to the Nader field north western desert to predict water saturation (Sw) and effective porosity (PHIE) for the Bahariya reservoir. Stepwise regression, which derives subsets of attributes from internal and external attributes, is used in a multi-attribute analysis. External attributes are represented in acoustic impedance generated from seismic inversion, whereas internal attributes are represented in well logs and seismic data. The seismic inversion technique has several types, but model-based inversion is the focus of this study. The training of a neural network (NN) using three attributes in Sw prediction and using six attributes in PHIE prediction showed a substantial number of correlations. The actual and predicted water saturations and porosities of the PNN have correlation coefficients of 97.5% and 91%, respectively. Then, Sw and PHIE parameters were extended over the Bahariya reservoir to describe the distribution of these parameters in Nader field. The results prove the validity of the workflow to accurately predict Sw and PHIE with a higher accuracy than ever before in the north western desert. Hence, this workflow can be implemented in the analogue basins in the future.