Conversion of 3D seismic attributes to reservoir hydraulic flow units using a neural network approach: An example from the Kangan and Dalan carbonate reservoirs, the world’s largest non-associated gas reservoirs, near the Persian Gulf

Abstract

This study presents an intelligent model based on probabilistic neural networks (PNN) to produce a quantitative formulation between seismic attributes and hydraulic flow units (HFUs). Neural networks have been used for the last several years to estimate reservoir properties. However, their application for hydraulic flow unit estimation on a cube of seismic data is an interesting topic for research. The methodology for this application is illustrated using 3D seismic attributes and petrophysical and core data from 6 wells from the Kangan and Dalan gas reservoirs in the Persian Gulf basin. The methodology introduced in this study estimates HFUs from a large volume of 3D seismic data. This may increase exploration success rates and reduce costs through the application of more reliable output results in hydrocarbon exploration programs. 4 seismic attributes, including acoustic impedance, dominant frequency, amplitude weighted phase and instantaneous phase, are considered as the optimal inputs for predicting HFUs from seismic data. The proposed technique is successfully tested in a carbonate sequence of Permian-Triassic rocks from the studied area. The results of this study demonstrate that there is a good agreement between the core and PNN-derived flow units. The PNN used in this study is successful in modeling flow units from 3D seismic data for which no core data or well log data are available.