Integrating Resistivity Data with Production Data for Improved Reservoir Modelling

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 This work deals with the problem of estimating reservoir permeability and porosity distribution from multiple sources, including production data and well logging data. The work focuses on integrating long-term resistivity data into the parameter estimation problem, investigating its resolution power both in the depth direction and in area. The resistivity logging tool considered in this project is a new tool proposed for permanent installation. The tool is installed in the cement around the well when the well is completed, and is capable of recording the long-term resistivity variation around the wellbore. In this work, the Poisson equation with mixed boundary condition was used to model the infinite potential field around the resistivity logging tool. The behavior of the reservoir was modeled with a standard three-dimensional, two-phase, black-oil model. The resistivity response simulator was integrated into the flow simulator through Archie's law. The Gauss-Newton algorithm was used to solve the inverse problem. This algorithm requires the calculation of the derivatives of the observation data with respect to the unknown parameters. These derivatives are called the sensitivity coefficients. By running several simple inverse problems, it was concluded that the resistivity data has high resolution power in the depth direction and is capable of sensing the areal heterogeneity. Introduction In reservoir engineering, some of the most important tasks are to monitor reservoir conditions and design optimal production strategies. These tasks require knowledge of the spatial distributions of reservoir properties. Traditional well test