Geological models consistent with the physics of sediment transport and deposition are increasingly becoming popular for the evaluation and development of water, oil and gas reservoirs. Several statistical methods have been used for the geologic modeling but they are based on very scarce information which does not consider the underlying physical principles that control the process of sediment deposition. Flow-based sedimentary models that consider depositional and physical principles for the distribution of sediments in space can be used to help generate geologically realistic models. However, these models have an important limitation, in that, it is impossible to assimilate reservoir specific information in these models. In order to overcome this limitation, an approach utilizing the Ensemble Kalman Filter (EnKF) technique for sequentially conditioning the sedimentary process model to chrono-stratigraphic information recorded at wells is presented in this paper. The EnKF technique allows the conditioning of sedimentary models to petrophysical and poro-elastic acoustic measurements from seismic and well-logs. Starting from initial probability distributions representing the uncertainty in state variables, an iterative updating process is implemented within the EnKF framework. The ensemble of state variables is adjusted so that the updated model parameters, when used in the sediment deposition model will yield a match to the conditioning data. This method was initially tested with a 1-Dimensional sedimentary deposit model for quasi-steady state turbidity currents. The geologic model was then extended to a 2-Dimensional space and the probabilistic method of Lattice Boltzmann Simulation was implemented using the three-speed D2Q9 lattice.<br><br>Besides presenting a novel application of EnKF for arriving at data-conditioned, sedimentary process models, the paper also sheds light on the capabilities and limitations of the EnKF approach for data conditioning.<br>


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