Abstract
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.
Highlights
The neural network analysis estimates the target log by making use of several attributes chosen from a suite of attributes
Geophysical applications were used in the beginning of the 1990s in the development of early neural networks for the description and prediction of lithology of wells by using a propagation of MultiLayer Feed Forward Network (MLFN) [9]
Probabilistic Neural Network (PNN) analysis was used for predicting P-wave velocity from several 3D seismic attributes
Summary
The neural network analysis estimates the target log by making use of several attributes chosen from a suite of attributes. The estimation of logs using seismic data in neural networks was proposed in geophysical applications for interpretation frameworks [10]. This field of research was applied for sonic inversion of the content of shale logs using both of seismic data and well logs [11]. Predicting subsurface properties, such as P-wave, has always been a fundamental problem for geologists and geophysicists. Probabilistic Neural Network (PNN) analysis was used for predicting P-wave velocity from several 3D seismic attributes
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