Porosity estimation and reservoir favorite zone extraction through pre-stack seismic AVO inversion: A case study of the galhak-oil formation in the Rawat Basin, Sudan

Abstract

The Rawat Basin in southwest Sudan has seen significant progress in identifying and drilling numerous structural traps. However, it is essential to acknowledge that exploration efforts in this region have mainly relied on conventional seismic interpretation techniques, without integrating advanced seismic analysis methods. Unfortunately, this limitation has resulted in many recent drilling attempts yielding disappointing outcomes, with dry wells being the predominant result. Recognizing the urgent need to enhance exploration efficacy, this study breaks new ground by presenting the pioneering application of pre-stack simultaneous inversion in the Rawat Basin. By employing this seismic quantitative interpretation approach for the first time in the region, the study aims to overcome existing limitations and uncover previously unidentified stratigraphic reservoirs, ultimately establishing new drilling targets. We applied an integrated approach that combines geological and geophysical methods to estimate porosity and identify favorable zones for potential hydrocarbon presence in the main reservoir target of the Galhak-Oil Formation. The study performed necessary preprocessing of pre-stack seismic data to enhance signal-to-noise ratio and resolution, as well as the compensating for amplitude losses before their use in seismic inversion. During the amplitude versus offset (AVO) inversion process, we estimated reservoir elastic properties and established a relationship between acoustic impedance, Vp/Vs ratio, and effective porosity values based on well logs. This relationship was then applied to the inverted elastic properties to predict porosity far from well locations. The result is a volume representing the distribution of effective porosity, with high porosity values reaching 0.27, which matched the drilling results. Consequently, multiple sand channels with high porosity were identified. Finally, categorical volumes were created by applying cut-offs based on acoustic impedance, Vp/Vs ratio, and porosity volumes. These categorical volumes were then combined using distinct data ranges, resulting in a unique trace value that represents the favored zones of the target reservoir.