Abstract:
The Late Early Cambrian Early Middle Cambrian Jutana Formation, in the Rajian Oil field, which is located in Eastern Potwar basin is considered the main target for a source of hydrocarbons in the Potwar Sub basin. The Rajian structure consists of tight fault-bounded, snake-head anticlinal structure which trends Northeast- Southwest and mainly formed because of the compressional forces in the area due to highly deformed zone crustal restoration process was performed on three seismic dip lines to estimate the length of deformed and shortened crust. This study is intended to predict reservoir properties such as porosity, volume of shale and fluid content using the Artificial Neural Network. Artificial Neural Networks have gained a substantial amount of attention over the past few years, among different linear and nonlinear prediction tools such as curve fitting, regression etc. In this study, 2D seismic data and well logs of the Rajian Oil field are used for the prediction of reservoir properties. To achieve this, the 2D seismic data was inverted through multi-layer feed-forward neural networks (MLFN) to obtain acoustic impedance volume which was then used as part of seismic attribute study. Multi-attribute analysis was performed in order to analyze the effectiveness of specific attributes for training the MLFN. A total of seven attributes were found to provide the best training results, after stepwise regression and validation testing. These attributes proved to show a substantial amount of correlation and thus porosity, volume of shale and fluid content volumes were predicted. Horizon maps for prospective formation of Jutana were extracted from these volumes to analyze the spatial extent of these attributes, on the basis of which, hydrocarbon reserves were estimated using volumetric reserves estimation method which came out to be 8758 (MMSTB).
