Prediction of porosity of the Zubair reservoir at Rumaila oil field, Southern Iraq, using the seismic inversion technique

• Main Authors: ALateya, Wisam H., Handhal, Amna M., Hashem, Hassan A.
• Format: Conference Proceeding
• Language: English
• Subjects: Acoustic impedance; Acoustic properties; Data acquisition; Density; Gamma rays; Oil fields; Porosity; Reservoirs; Rock properties; Seismic response; Seismic surveys
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0143531

Rock porosity represents the main property that controls the fluid distribution and hydrocarbon extraction from the reservoir rock. In this study, information from five wells was integrated with post stack seismic data acquired from a 3D seismic survey of the northern part of Rumaila Oil Field to perform seismic inversion to derive porosity. The used well log data includes gamma ray, resistivity, density, acoustic impendence (from compressional velocity and density) logs, check shot, and VSP surveys, in addition to the tops of formations (which are converted to time-based logs) after performing check shot correction and generating a synthetic seismogram. Seismic inversion analysis is an attempt to correlate seismic attributes like acoustic impedance (A.I) to rock properties (porosity). The inversion analysis is used in this paper to predict the porosity distribution. 3D seismic surveys (migrated post-stack) of the northern part of Rumaila oil field and well data used for this study. Information from five wells integrated with post-stack seismic data to perform seismic inversion through the area surveyed seismically. Four horizons picked within the seismic 3D volume that Shaw maximum crest at the northern part in the field and guide initial model for low frequency in addition to using it in the extraction of slices through converted 3D A.I. and calculated porosity data, the porosity slice guided by the picked target Shaw enhancing porosity with upward direction. The zones with low A.I. in the slices extracted through A.I. volume indicate high porosity zones. Integration of well and seismic data show a more accurate distribution of reservoir property than using well data alone.