Direct Bayesian seismic inversion for porosity estimation in a hard rock carbonate reservoir

It has done a study of porosity prediction by using neural network. The study uses 2D seismic data post-stack time migration (PSTM) and 2 well data at field “T”. The objective is determining distribution of porosity. Porosity in carbonate reservoir is actually heterogeneous, complex and random. To face the complexity the neural network method has been implemented. The neural network algorithm uses probabilistic neural network based on best seismic attributes. It has been selected by using multi-attribute method with has high correlation. The best attributes which have been selected are amplitude envelope, average frequency, amplitude weighted phase, integrated absolute amplitude, acoustic impedance, and dominant frequency. The attribute is used as input to probabilistic neural network method process. The result porosity prediction based on probabilistic neural network use non-linear equation obtained high correlation coefficient 0.86 and approach actual log. The result has a better correlation than using multi-attribute method with correlation 0.58. The value of distribution porosity is 0.05–0.3 and it indicates the heterogeneous porosity distribution generally from the bottom to up are decreasing value.

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Seismic inversion to map the hard streak layer and porosity distribution in carbonate reservoir‐A case study from western offshore basin of India

10.1190/1.3059262 ◽

2008 ◽

Author(s):

S. K. Pokhriyal ◽

Sucheta Dotiwala ◽

A. Sanyal ◽

P. S. Basak ◽

Abhijit Deb

Keyword(s):

Seismic Inversion ◽

Carbonate Reservoir ◽

Porosity Distribution

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Enhanced Carbonate Reservoir Characterization Based on a Dual Constrains Provided by Forward Stratigraphic Modeling and Seismic Inversion

10.1190/ice2015-2202621 ◽

2015 ◽

Author(s):

Cedric Pellan* ◽

Mudaham Zen ◽

Noelia Rodriguez Morillas ◽

Taoufik Ait-Ettajer ◽

Laurent Fontanelli

Keyword(s):

Reservoir Characterization ◽

Seismic Inversion ◽

Carbonate Reservoir ◽

Carbonate Reservoir Characterization

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Improving Porosity–Velocity Relationships Using Carbonate Pore Types

Journal of Computational Acoustics ◽

10.1142/s0218396x15400068 ◽

2015 ◽

Vol 23 (04) ◽

pp. 1540006 ◽

Cited By ~ 6

Author(s):

Tingting Zhang ◽

Yuefeng Sun ◽

Qifeng Dou ◽

Hanrong Zhang ◽

Tonglou Guo ◽

...

Keyword(s):

Pore Structure ◽

Acoustic Impedance ◽

Carbonate Rocks ◽

Seismic Inversion ◽

Carbonate Reservoir ◽

Reservoir Rocks ◽

Fluid Content ◽

Fracture Fluid ◽

Poroelastic Model ◽

Pore Types

Acoustic impedance in carbonates is influenced by factors such as porosity, pore structure/fracture, fluid content, and lithology. Occurrence of moldic and vuggy pores, fractures and other pore structures due to diagenesis in carbonate rocks can greatly complicate the relationships between impedance and porosity. Using a frame flexibility factor ([Formula: see text]) derived from a poroelastic model to characterize pore structure in reservoir rocks, we find that its product with porosity can result in a much better correlation with sonic velocity ([Formula: see text] = [Formula: see text]) and acoustic impedance ([Formula: see text] = [Formula: see text], where A, B, C and D is 6.60, 0.03, 18.3 and 0.09, respectively for the deep low-porosity carbonate reservoir studied in this paper. These new relationships can also be useful in improving seismic inversion of ultra-deep hydrocarbon reservoirs in other similar environments.

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