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Estimating Bottom Hole Damage Zone Parameters Based on Mathematical Model of Thermo-hydrodynamic Processes
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, ECMOR XIV - 14th European Conference on the Mathematics of Oil Recovery, Sep 2014, Volume 2014, p.1 - 9
Abstract
In order to find the optimal parameters of workovers for recovering well productivity it is important to know the properties of the bottom hole damage zone, such as its radius and permeability. The effect of the damage zone can be described by the skin factor, which can be indirectly estimated by well test analysis. Temperature and pressure are the most frequently observed physical parameters in well testing. During transient tests, both the pressure and temperature are measured at the well bottom hole. Typically, only pressure transient data is considered, and pressure transient data analysis is used to estimate the skin-factor. However, due to well bore effects, it is impossible to identify the structure of the damage zone. The reservoir pressure and temperature both recover after a well shuts. Temperature changes in the reservoir are induced by heat convection, heat conduction and the Joule-Thomson effect. The flow rate of these physical processes depends on the properties of both the reservoir and the damage zone. By considering the differences from the pressure buildup curve, temperature transient data can be used to estimate the radius of the damage zone, which can help give more accurate well test interpretation [1] . The mathematical model for well tests analysis is based on the solution of diffusivity equation for pressure and heat transfer equation for temperature. In [2] Green function technique was used to solve the pressure equation. In this study we present joint solution of the equations for the pressure and temperature dynamics. The calculations take into account the dependence of Joule-Thompson coefficient on reservoir pressure. Calculations were performed for the interpretation of well test data in a gas producing well. The results are qualitatively consistent with field examples.