Inversion of seismic data to modeling the Interval Velocity in an Oilfield of SW Iran

Abstract

Seismic velocity is considered the best attribute related to formation pressure changes. Integrating seismic attributes and well-logging data through seismic inversion predicts the reservoir characteristics across the field with the highest accuracy. This study especially presents seismic velocity for the whole south Azadegan Field in SW Iran for carbonate formations. The considered dataset includes 3D seismic data, vertical seismic profiling (VSP), logging data of 23 wells, and geological information. Here, we estimated the interval velocity using post-stack migration velocity, seismic inversion, and the relationship between the acoustic impedance (AI) model and the sonic log to predict formation pressure. As a result, the correlation coefficient of 0.71 and a high inversion accuracy (8.76% relative error) is concluded. The actual and predicted P-wave (Vp) correlation coefficient is calculated as 0.74 and all sevens as 0.79 using an AI seismic attribute. Thus, the estimated Vp agrees with the original well-log values. Inverted AI cubes in the deeper formations of the field are about 8000-15000 [(m/s)*(g/cm³)], which could be referred to as calcareous formations. The correlation of the Vp cube resulting from the Sequential Gaussian simulation (SGS) considering co-kriging with the AI, with the initial velocity cube using the inverse distance weighted (IDW) method being 0.54 is more than the same method applied with interval migration velocity trend in co-kriging. The anisotropy of the final Vp cube for the vertical variogram range is 96m, and for major and minor directions is 11850 m.