Poro-Acoustic Impedance (PAI) as a new and robust seismic inversion attribute for porosity prediction and reservoir characterization

Leisi, Ahsan; Aftab, Saeed; Shad Manaman, Navid

doi:10.1016/j.jappgeo.2024.105351

Journal of Applied Geophysics

2024

DOI: 10.1016/j.jappgeo.2024.105351

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Poro-Acoustic Impedance (PAI) as a new and robust seismic inversion attribute for porosity prediction and reservoir characterization

Ahsan Leisi,

Saeed Aftab,

Navid Shad Manaman

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2024

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Cited by 8 publications

References 32 publications

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Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data

Nyakilla,

Guanhua,

Hongliang

et al. 2024

Nat Resour Res

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Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data

Nyakilla,

Guanhua,

Hongliang

et al. 2024

Nat Resour Res

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A prediction method for favorable hydrocarbon accumulation areas in carbonate oil reservoirs based on machine learning and driven by formation water chemical characteristic data

Chen,

Gao,

Pang

et al. 2024

Carbonates Evaporites

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Three-dimensional shear wave velocity prediction by integrating post-stack seismic attributes and well logs: application on Asmari formation in Iran

Leisi,

Shad Manaman

2024

J Petrol Explor Prod Technol

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Understanding the distribution of shear wave velocity (VS) in hydrocarbon reservoirs is a crucial concern in reservoir geophysics. This geophysical parameter is utilized for reservoir characterization, calculating elastic properties, assessing fractures, and evaluating reservoir quality. Unfortunately, not all wells have available VS data due to the expensive nature of its measurements. Hence, it is crucial to calculate this parameter using other relevant features. Therefore, over the past few decades, numerous techniques have been introduced to calculate the VS data using petrophysical logs in wells with limited information. Unfortunately, the majority of these methods have a drawback they only offer insight into the location of the wells and do not provide any details regarding the distribution of VS in the space between the wells. In this article, we employed three-dimensional post-stack seismic attributes and well-logging data integration to predict the distribution of VS in the Asmari formation in an Iranian oil field. To accomplish this objective, the model-based seismic inversion algorithm was utilized to convert the seismic section into the acoustic impedance (AI) section. Then, AI and seismic data were utilized in the cross-validation method to determine the relevant attributes for predicting the spatial distribution of VS throughout the entire reservoir area, using an artificial neural network. The proposed method was shown to provide 94% correlation and 109 m/s error between the actual and estimated VS. Also, the calculated VS section has a high correlation with the actual logs at the location of the wells.

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Poro-Acoustic Impedance (PAI) as a new and robust seismic inversion attribute for porosity prediction and reservoir characterization

Cited by 8 publications

References 32 publications

Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data

Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data

A prediction method for favorable hydrocarbon accumulation areas in carbonate oil reservoirs based on machine learning and driven by formation water chemical characteristic data

Three-dimensional shear wave velocity prediction by integrating post-stack seismic attributes and well logs: application on Asmari formation in Iran

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