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Reservoir Delineation in Fluvial Architecture via Post-Stack Seismic Attributes


A well-known challenge of characterizing fluvial systems is identifying the location, connectivity, and potential heterogeneity associated with channel complexes in order to optimize drilling locations. Spectral decomposition and other seismic attributes are useful tools in discerning this information, but this alone is often not sufficient, especially with noisy data or small reflectivity. Post-stack inversion is commonly used to alleviate these problems by incorporating information from the wells and extending from reflectivity to acoustic impedance (AI). However, AI alone is not enough to discern lithology in all geologic settings, and high-quality seismic gathers are not always available. Synergy between geologic information and interpretation and geophysical analysis is a recommended practice to overcome these troubles. In this study, the onshore conventional fields under consideration show little contrast between different lithologies in the AI domain. Fluvial depositional environments are typically complex and, in this particular case, extremely difficult to map channels for reservoir sand distribution. In order to understand the intricacies of the internal reservoir architecture, well connectivity and their impacts on production, we implement both constrained least-squares spectral decomposition and post-stack model-based inversion to improve seismic interpretation. Using well data, we build an impedance-porosity crossplot to clearly illustrate the sand- and shale-lithology trends. We generate a linear regression for each lithology-zone, and consequently, we build an upper- and lower-bound porosity volume. Detailed mapping of both the reflectivity data and the impedance/porosity volumes coupled with geologic understanding of the region successfully overcomes the outlined obstacles associated with channel identification and internal structure. This work provides guidance to optimize well-placement and confirm the suitability of identified water and water-alternating-gas injector well candidates.