Reservoir Characterization in Balal Oil Field by Means of Inversion, Attribute, and Geostatistical Analysis Methods

Abstract

Listen

Translate article

The authors focus on the Arab reservoir of Balal oil field in the Persian Gulf. The major reservoir unit in the interested area is composed of alteration of thin dolomite with anhydrite layers. In the available 3D seismic data, these layers are not resolved and generate a composite detectable seismic response, making reservoir characterization difficult through conventional seismic attribute analysis (using single-attribute transforms). In addition to resolving the major reservoir units the main aim of this study is porosity prediction and making 3D porosity volume for detailed reservoir characterization. For this purpose first the 3D seismic volume was inverted to obtain an acoustic impedance cube using 3 different inversion algorithms. Inversion integrates log and seismic data to a full band acoustic impedance model of Earth. This process improves the vertical resolution and resolving the interested reservoir layers; it is also an important layer property, which usually has close relation with reservoir properties such as porosity, water saturation, and clay content. Among the different inversion methods applied, according to cross-validation results, acoustic impedance derived from model-based inversion has been selected as the main seismic attribute. In the next step, the acoustic impedance attribute besides other attributes extracted from seismic volume were analyzed using multiattribute regression and neural networks (probabilistic, multilayer feed forward, and radial basis function). These linear or nonlinear combinations of attributes for porosity prediction result in the improved match between the derived and predicted porosity. To estimate the reliability of the derived multiattribute transforms, cross-validation is used. Finally, multiattribute transform using stepwise regression for selection of attributes with minimum cross-validation error is selected for porosity prediction. In addition to making a 3D cube of porosity, several porosity maps from different reservoir layers were prepared.