Determining Depth-Dependent Reservoir Properties Using Integrated Data Analysis

This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Abstract 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. Introduction Predictions of reservoir performance often require the availability of a reservoir simulation model in which rock properties such as porosity and permeability are specified at all block locations. Moreover, the reservoir model geometry and types of reservoir boundaries such as faults, closed, linear, and constant pressure must also be known in advance. For some purposes, relatively simple models such as a homogeneous, fractured or …