Impedance inversion based reservoir property prediction using Neural Law, Kandra Area, Middle Indus Basin, Pakistan (T-1522) (MFN 6270)

Abstract

Kandra block (Block 2768-6) covers an area of about 456 km2 and is situated in the northern region of the province of Sindh. It is located in the Khairpur and Sukkur districts. Seismic lines KDR89-01, KDR89-02, KDR89-03, KDR89-04, and KDR89-05 of Kandra block were obtained from LMK Resources for the purpose of seismic interpretation and well log analysis of Kandra 01, Kandra 02 and Kandra 03. Kandra block is basically a dome sequence with negative flower structure, mainly formed because of the transtentio na l regime of the area. Two Horizons were marked which were Sui Main Limestone and Chiltan Limestone. Based on seismic data Major faults were identified, which extended through these two formations. Two way travel time and depth contour maps were generated for these above mentioned horizons. Source rock is Sembar Formation in this area. Hydrocarbon traps were most possibly developed in the anticlines and are the prospective zones for hydrocarbon accumulation. Well log analysis was carried out on Kandra 01, Kandra 02 and Kandra 03 to identify reservoir area and evaluate the values for saturation of water, porosities present in the subsurface formation. On the basis of this analysis two probable hydrocarbon zones in Sui Main Limestone and Chiltan formation were identified. This study is intended to estimate reservoir properties through Porosity and Fluid content prediction using the Artificial Neural Network. To achieve this, the 2D seismic data was inverted through Probabilistic neural networks to obtain acoustic impedance profile which was then used as part of seismic attribute study applied to the data set. Multi attribute analysis was performed in order to analyze the effectiveness of specific attributes for training the ANN. Different attributes were found to provide the best training results, after stepwise regression and validation testing. These attributes proved to show a substantia l amount of correlation and thus Porosity and Fluid content were predicted. Horizon maps for two potentially prospective formations Sui Main Limestone and Chiltan were extracted from these profiles, to analyze the spatial extent of these attributes, on the basis of which, potentially prospective zones were defined by a probability analysis.