Application of iso-frequency extractions and spectral frequency blending in hydrocarbon delineation of thin-pay and thick-pay reservoirs, Niger Delta Basin

Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields. This study addressed the existing challenges of delineating a known hydrocarbon region for a thin-pay reservoir using conventional attributes extraction methods. The efficacy of applying iso-frequency extraction and spectral frequency blending in identifying thin-pay and thick-pay reservoirs on seismic was tested by utilizing 3D seismic data and well logs data of Terra field in the Western Niger Delta Basin. Well tops of all the reservoirs in the field were picked and two reservoirs that correspond to thin- and thick-pay reservoirs, namely A and F were identified respectively. The gross pay thickness of reservoir A is 18 ft while that of reservoir F is 96 ft. Conventional attribute extraction such as RMS amplitude, minimum amplitude, and average energy can be used to identify the hydrocarbon-bearing region in reservoir F but was not applicable for identifying the thin-pay reservoir A. This prompted the interest of using iso-frequency extractions and spectral frequency blending of three iso-frequency cubes of 12 Hz, 30 Hz, and 70 Hz to get a spectral frequency RGB cube. The 12 Hz iso-frequency can be used to partially identify hydrocarbon-bearing region in reservoir A while the 30Hz iso-frequency can be used to partially identify hydrocarbon-bearing region in reservoir F. The results show that time slices from the spectral frequency blended cube were able to delineate both the thin-pay and thick-pay hydrocarbon-bearing regions as high amplitude. The extractions also conformed to the structure of the two reservoirs. However, there seems to be a color difference in the amplitude display for both reservoirs. The thick-pay reservoir showed a red color on the time slice while the thin-pay reservoir showed a green color. This study has shown that spectral frequency blending is a more effective tool than conventional attributes extractions in identifying hydrocarbon-bearing region using seismic data. The methodology utilized in this study can be applied to other fields with similar challenges and for identifying prospective hydrocarbon bearing areas.