Application of Neural Network Analysis for Seismic Data to Differentiate Reservoir Units of Yamama Formation in Nasiriya Oilfield A Case Study in Southern Iraq
The EMERGE application from Hampsson-Russell suite programs was used in the present study. It is an interesting domain for seismic attributes that predict some of reservoir three dimensional or two dimensional properties, as well as their combination. The objective of this study is to differentiate reservoir/non reservoir units with well data in the Yamama Formation by using seismic tools. P-impedance volume (density x velocity of P-wave) was used in this research to perform a three dimensional seismic model on the oilfield of Nasiriya by using post-stack data of 5 wells. The data (training and application) were utilized in the EMERGE analysis for estimating the reservoir properties of P-wave velocity, in addition to the neural network analysis and deriving relations between them at well locations. P- wave velocity slices of reservoir units (Yb1, Yb2, and Yc) of Yamama Formation were prepared to determine the enhancement trends within these units. From a general economic point of view, due to good prospecting in Cretaceous rocks, especially in Nasiriya oilfield, , Yamama Formation was found to contain hydrocarbon accumulation and can be considered as one of the most important reservoirs in southern Iraq.