Determining Depth-Dependent Reservoir Properties Using Integrated Data Analysis

The ambition of modern reservoir modeling is to make integrated use of dynamic data from multiple sources to infer the reservoir properties. The process of inferring the reservoir properties from indirect measurement is an inverse or parameter estimation problem. The parameters of interest in this work are porosity and absolute permeability. These parameters have important influence in determining the performance of the reservoir and in reservoir optimization. This work represents a way of estimating such parameters from a variety of indirect measurements such as well test data, long-term pressure and water-oil ratio history, and 4-D seismic information. The work also considers the effect of the data on the uncertainty and resolution of reservoir parameters. In particular, since earlier work has addressed two-dimensional problems, this study focuses on the estimation of parameters in three dimensions where properties vary as a function of depth. The study determined that depth-dependent properties can be estimated to varying degrees of precision, depending on the resolution of the data used to constrain the model. Seismic data generally has poor depth resolution, hence it was found that parameter estimates are better determined areally.