Abstract

The precise determination of a velocity model is of utmost importance in comprehending the subsurface topography, identifying hydrocarbon bearing zones and converting data domain. Although conventional velocity models are sufficient for data domain conversion, they lack the vertical resolution necessary to effectively resolve small-scale anomalies. As a result, seismic data exhibits limitations in highlighting small-scale topographical features. Furthermore, uncertainties in the structural interpretation can lead to erroneous structures or the oversight of hydrocarbon bearing zones. To address these challenges, statistical methods can be employed to reduce uncertainties and enhance resolution. These methods leverage the spatial similarity of geological properties to interpolate data. This paper introduces a 3D Geostatistical Velocity modeling approach that utilizes the Sequential Gaussian Simulation (SGS) algorithm to estimate a wellcalibrated geostatistical interval velocity model in a shallow offshore area of KG basin. The methodology involves manual residual velocity picking at a fine grid resolution of 200m X 200m, followed by Time Preserving Tomography (TPT) and geostatistical modelling. The dominant reservoir in the area consists of thin hydrocarbon-bearing sands from the Cretaceous age, which are interspersed with silt and shale, making them indistinguishable in seismic data. However, the geostatistical velocity model, characterized by higher resolution, successfully resolves two thin sands (zone 1 and zone 2) measuring approximately 4-6m in thickness, which were inseparable on seismic data. This geostatistical high-resolution velocity model, when combined with other datasets, finds applications in identifying small-scale anomalies and predicting pore pressure, among other uses.