Quantitative Evaluation of Fluvial Reservoir Geologic Model Uncertainties Using Virtual Outcrops

Abstract

Abstract The uncertainties of fluvial reservoir geologic model are notably high due to complicated geological conditions and unknown strong heterogeneity. However, previous uncertainty analysis approaches mainly focus on qualitative evaluation. In this work, we proposed a novel workflow to quantify and optimize the geologic model uncertainties using the virtual outcrops. First, the 3D geologic model is built by the virtual outcrops and geology database is established by the fine description of modern sedimentations, geologic outcrops and dense spacing areas. Next, geologic modeling algorithm is optimally selected based on the complexities of target reservoirs, computational speed and the shape of sand bodies. In this work, three indicators are proposed to evaluate the accuracy of geologic models, including matching coefficient of digital grey images to virtual outcrops, consistency of braided stream facies morphology and connectivity of inter-well effective sand bodies. For the braided channel in Sulige field examples, the width of channel belt is 1500~3500m and the average sand thickness is 5m. For channel bar, the width is 350~650m, the length is 800~1500m and the thickness is 3~6m. The virtual outcrops help determine the vertical sequence and planar characteristics of sedimentary facies and sand bodies. The comparisons between established model and virtual outcrops indicate that the accuracy of geologic models increases as the denseness of hard data becomes smaller and the optimal well spacing and row spacing match up with the sand body size and average well spacing of studied area. The evaluation system proposed in this work demonstrates the degree of geologic reproduction, reasonability and partial uncertainty of the models to the real reservoir. The value of this work is to provide a novel practical approach to optimize and quantify the uncertainties of geologic model. Furthermore, the established workflow can further be applied to identify the most significant controlling factor to determine geologic modeling in unconventional reservoirs.