3D seismic attributes analysis and inversions for prospect evaluation and characterization of Cherokee sandstone reservoir in the Wierman field, Ness County, Kansas - Boumaaza, Bouharket (Kansas State University, 2017)
This work focuses on the use of advanced seismically driven technologies to estimate the distribution of key reservoir properties which mainly includes porosity and hydrocarbon reservoir pay. These reservoir properties were estimated by using a multitude of seismic attributes derived from post-stack high resolution inversions, spectral imaging and volumetric curvature. A pay model of the reservoir in the Wierman field in Ness County, Kansas is proposed. The proposed geological model is validated based on comparison with findings of one blind well. The model will be useful in determining future drilling prospects, which should improve the drilling success over previous efforts, which resulted in only few of the 14 wells in the area being productive. The rock properties that were modeled were porosity and Gamma ray. Water saturation and permeability were considered, but the data needed were not available. Sequential geological modeling approach uses multiple seismic attributes as a building block to estimate in a sequential manner dependent petrophysical properties such as gamma ray, and porosity. The sequential modelling first determines the reservoir property that has the ability to be the primary property controlling most of the other subsequent reservoir properties. In this study, the gamma ray was chosen as the primary reservoir property. Hence, the first geologic model built using neural networks was a volume of gamma ray constrained by all the available seismic attributes. The geological modeling included post-stack seismic data and the five wells with available well logs. The post-stack seismic data was enhanced by spectral whitening to gain as much resolution as possible. Volumetric curvature was then calculated to determine where major faults were located. Several inversions for acoustic impedance were then applied to the post-stack seismic data to gain as much information as possible about the acoustic impedance. Spectral attributes were also extracted from the post-stack seismic data. After the most appropriate gamma ray and porosity models were chosen, pay zone maps were constructed, which were based on the overlap of a certain range of gamma ray values with a certain range of porosity values. These pay zone maps coupled with the porosity and gamma ray models explain the performance of previously drilled wells.
Keywords: Seismic attributes; Stochastic inversion; Sequential geological modelling; Volumetric curvature; Spectral attributes; Neural networks