Quantitative Use of Different Seismic Attributes in Reservoir Modeling

Accurate reservoir models are essential for reservoir management. Optimal use of all available data is crucial. Traditionally, reservoir properties have been conditioned to the dynamic production data from the wells. Seismic data, on the other hand, is only used in a qualitative manner. Quantitative use of seismic data is sparse and research based. To use seismic data quantitatively in the reservoir-modeling process, an integrated workflow need to be established such that the forward modeling of the synthetic seismic data and the preferable measured seismic data can be incorporated in the conditioning process. The different modeling regimes, such as reservoir flow simulation, rock physics, and seismic wave propagation, are involved in getting from reservoir flow properties to seismic signals. Hence, different seismic attributes from different levels can be used in the conditioning process. In this work, our focus is to test and demonstrate an integrated workflow for quantitative use of different seismic attributes in history matching. The history matching concept will be formulated in a Bayesian setting through ensemble based algorithms. The uncertainty of model is represented with an ensemble of realizations. A field case study is used to demonstrate the importance of different seismic attributes in the conditioning process.