Well Tests analysis is a useful tool to estimate and "visualize" properties and performance of reservoirs under production induced stress-sensitivity. Formation damage due to the irreversible nature of rock deformation reduces the permeability, and consequently alters the oil/gas production.
The objective of this study is to assess well tests analysis in stress-sensitive reservoirs to determine the mechanism of permeability reduction and the subsequent well formation damage along with production depletion. Therefore, conventional well test analysis must be modified to include the governing equations describing rock deformation and changing pore pressure.
In this work a 3D fully coupled fluid-flow/geomechanical model accounting for well testing and formation damage has been developed. The numerical model performs well test analysis such as draw-down, pressure build-up and multi-rate tests describing the permeability reduction in stress-sensitive reservoirs. The governing equations were developed in cylindrical coordinates to better simulate the flow geometry that characterize the drainage area in most well tests. Furthermore, it is assumed that the mechanical rock properties are function of the mean effective stress (i.e., nonlinear rock deformation).
For typical field conditions, the results show that a combined effect of rock deformation -caused by pore pressure depletion, and reservoir compaction -caused by the overburden, generates high formation damage approximately within 9.5 feet around the wellbore. This damage is irreversible, and the permeability can only be improved a few feet near the wellbore through well stimulation. It is observed that conventional well-test analysis techniques may lead to erroneous estimations of well performance due to formation damage in stress-sensitive reservoirs. New results illustrate that Reservoir Pressure Distribution plots can be used as a "visualization" tool to identify formation damage by deformation/compaction in well test analysis. Premature diagnosis of the above-mentioned combined effect will provide evaluation and picturing of formation damage for field development strategies.
It has been shown[1–8] that in stress sensitive reservoirs rock properties may change significantly with variation of the pore pressure and the stress state. The maximum pore pressure variation is at the wellbore and thus the largest deviations from the initial stress state are found at the borehole wall and its neighborhood. This implies that the conventional assumptions in well tests analysis models such as constant fluid-flow and geomechanical rock properties, and constant stress state cannot longer be used.
In a previous study, Osorio et al.,  shown that production decline is associated with formation damage. Pressure depletion produces variation of the stress state, causing rock deformation, overburden compaction, axial loads and loss of lateral support that might lead to formation damage even far away from the wellbore and in, or adjacent to, the producing layer.
To study the impact on well testing when the above mentioned assumptions do not longer exist, and to determine the mechanism of permeability reduction with the subsequent well formation damage, it will be necessary to solve the governing equations describing the rock solid-part deformation coupled with the governing equations describing the changes in the pore pressure. Due to their strong nonlinear behavior, the solution of this set of differential equations must be performed numerically.
This paper presents a 3D, point-distributed, finite-difference model accounting for well testing and formation damage caused by rock deformation in stress-sensitive reservoirs. The physical system is represented in cylindrical coordinates and discretized by means of a point-distributed grid. The governing equations are based mainly on the following assumptions:
isothermal, oil-phase fluid flow,
rock solid-part deformation behaves as a nonlinear elastic medium with small strains, and
mechanical and fluid-flow properties are assumed to be function of the mean effective stress.