An Integrated Reservoir Predicting Approach with Geological Constraints from Stratigraphic Forward Modeling of the Miocene Fan Delta in Albert Basin, Uganda

Abstract

Abstract Reservoir prediction is a core area of research in oilfield exploration and development, and it is generally constructed on a combination of well data, seismic attributes or inversion. However, reservoir prediction in sparse well areas poses great challenges due to insufficient well control. If the quality of seismic data is poor, the spatial distribution characteristics of reservoirs cannot be effectively characterized through inversion or attribute analysis, which seriously affects the prediction accuracy. This paper proposes a new method to solve the difficulty in reservoir prediction of oilfields with sparse data and poor quality seismic cube, which evolves from depositional models, forward stratigraphic modeling (FSM) to geocellular modeling. First, based on the comprehensive analysis of core, seismic, grain size, heavy minerals, dip data, it is believed that a special fan delta developed in the Miocene strata in the south of Albert Basin. The reservoirs are dominated by distributary channels, which are in medium-coarse grains, and the provenance is from the southwest to flowing to the northeast. The formation thickness of the stratum decreases from the boundary fault to the direction of the basin. Then, the input parameters of FSM modeling are quantitatively expressed based on the sedimentary model research, including model boundary conditions, basic input information, sediment supply and transportation. FSM results were used to quantitatively characterize the deposition process. The FSM simulation results are compared with the depositional model and well data to verify the reliability. Finally, the shale content model in FSM results is resampled to the geocellular grids and used as the constraint for facies model and property model in geological modeling. This model is used for well pattern design and optimization. This new approach integrates the conceptual depositional model with quantitative FSM results. It improves the accuracy of reservoir prediction and provides a new technical workflow for reservoir characterization. Furthermore, it helps to obtain more insight into the sedimentary process and reduces the risk of oilfield exploration and development.