Abstract

This study is a comparative analysis of three types of post-stack inversion techniques, namely, Band-Limited (BLI), Linear Programming Sparse-Spike (LP-SSI), along with Model-Based (MBI), in addition to a neural network in the Sufyan oil field of the Muglad rift basin, Sudan. It investigates whether a combination of multi-attribute analysis and a neural network could bring about a better definition of unresolved thin reservoir layers. Applied inversion techniques produce accurate and reliable results, while the MBI method brings a higher correlation coefficient (0.988) and higher amplitude spectrum correlation (0.999). Hence, it is better for the Sufyan seismic data. The resulting resolution and precision of multi-attribute rock properties prediction is achieved using multi-layer feed forward neural (MLFN), probabilistic neural (PNN), along with radial basis function neural (RBFN) networks to predict porosity. The combination of PNN and LP-SSI has the highest training correlation of 0.9534 and validation correlation of 0.7998. However, MLFN shows that LP-SSI, when used as an external attribute, produces high-resolution images compared to those estimated with other combinations with training correlation of 0.9400 and validation correlation of 0.7111. The resulting resolution and precision in terms of porosities are higher when the combination of LP-SSI and MLFN approach is used compared to that estimated by other methods. Thus, LP-SSI is more accurate than other techniques to predict petrophysical parameters in the Sufyan oil field.

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