Inferring Depth-Dependent Reservoir Properties From Integrated Analysis Using Dynamic Data


Vinh Quang Phan







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To be able to predict reservoir performance or to optimize reservoir production, the determination of reservoir properties is required. The reservoir properties are spatially dependent and deterministic but are sampled at only a very small number of points. It is impossible to determine most of them by direct measurement.

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 in uence 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 and also considers the effect of the data on the uncertainty and resolution of reservoir parameters.

In particular, since earlier work (Landa, 1997) 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 objective is to find sets of distributions of permeability and/or porosity such that the model response closely matches the reservoir response. In addition, besides physical constraints, the sets of permeability and porosity must also satisfy constraints given by other information known about the reservoir.

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