Integrating the pre-stack seismic data inversion and seismic attributes to estimate the porosity of Asmari Formation

In this study, the inversion of seismic data has been used in integration with the seismic attributes in order to evaluate the reservoir porosity in the Ghar member of the Asmari Formation for an oil field located in SW Iran. Using the inversion method based on acoustic impedance modelling, the compressional wave velocity and density are extracted and, then, the linear and nonlinear conversion between the seismic attributes and the porosity log is used to obtain the optimum porosity volume for the region. In this study, we have used pre-stack seismic data to estimate reservoir porosity. The combination of selected seismic attributes along with the raw seismic data is used to estimate the porosity by the neural network method. In order to validate the utilised method, the cross-validation technique has been used to compare the accuracy of the calculated petrophysical parameters with the actual values. The correlation coefficient obtained for the estimated porosity is 81%. This value indicates that the training data were appropriate, optimally estimating the actual porosity using the selected attributes.

Automated summary

In this study, seismic inversion was used in combination with seismic attributes to evaluate reservoir porosity in an oil field in SW Iran. The neural network method was applied to estimate porosity, achieving a correlation coefficient of 81%.