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Compressional and Shear Interval Velocity Modeling to Determine Formation Pressures in an Oilfield of SW Iran

Document Type : Original Research Paper

Authors

1 Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Petroleum, Materials and Mining Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

3 Deputy Manager Geoscience Faculty, Research Institute of Petroleum Industry (RIPI), Tehran, Iran

Abstract

In the seismic methods, estimation of the formation pressures is obtained by converting the seismic velocity to the pore pressure, and comparing it with the effective pressure during the well-test program. This work is a new challenge regarding the velocity study domain in an oil field in SW Iran. The reservoir generally consists of carbonate rocks, and contains no shale interbeds. Here, 23 well information, seismic data interpretation, compressional (Vp), and shear velocity (Vs) models are implemented. The models are determined from the combined geo-statistical methods, and the results obtained are compared with the fractal models. The final Vs cube is modeled in order to determine the formation fracture pressure using the exploratory well cores and dipole sonic imager (DSI) Vs logs with a correlation coefficient of 0.95 for the Vs data obtained from the porosity, lithology, and primary DSI data. The vertical seismic profiling (VSP) data introduce a maximum interval velocity of 2760-2900 m/s in the field related to the Gotnia formation. The final amounts ​​of seismic acoustic impedance inversion (AI) at the bottom of the field are mostly in the range of 8000-15000 [(m/s)*(g/cm3)], which can be related to the calcareous formations. Based on the Logratio matrix obtained from the fractal velocity-volume (Vp-V) model, the maximum overall accuracy (OA) in the dominant limestone intervals is 0.74. It indicates a high correlation of the Vp cube model obtained from the combination of sequential Gaussian simulation (SGS) and co-kriging models with AI. The uncertainty studies of Vp model in blind wells are about 50%, which is acceptable considering the large well numbers.

Keywords

References
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Journal of Mining and Environment
Volume 13, Issue 3
July 2022
Pages 851-873
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