Condensate Banking Phenomenon Evaluation in Heterogeneous Low Permeability Reservoirs

Abstract

In low and tight gas formations, condensate banking will form in shortly time after production start-up due to pressure drop below the saturation pressure. Mobility reduction near wellbore area will affect well productivity. The prediction of gas condensate wells production will strongly depend on oil banking evaluation and modeling. A benchmark radial fine well model has been built using constant petrophysical properties per each layer. Several coarse Cartesian grids have been considered to evaluate discrepancies in terms of production and flowing pressure with respect to the benchmark grid. For a coarser Cartesian grid, it has been deduced that Generalized Pseudo-Pressure (GPP) is a key parameter to avoid well performance over-estimation. An alternative solution consists in defining a local grid refinement (LGR) near wellbore to honour the benchmark solution without using GPP. In this case study a LGR technique has been used to incorporate future hydraulic fractures for the wells development. A real application case has been considered to extend lessons learned from benchmark to field scale. A proper geological model has been built using a sedimentological model as driver for petrophysical properties distribution. Two DST have been considered to analyze condensate banking phenomena evaluation in a low and medium permeability matrix. To this purpose, three analytical models have been considered. Thus, to validate a representative analytical model a numeric simulation has been performed. Based on the obtained results, it can be affirmed that the radial composite is the most appropriate analytical model reproducing the phenomenon of gas mobility reduction in the nearest wellbore region.

Well Placement Optimization in a Gas Condensate Reservoir to Prevent Condensate Banking

“…For a single well, it is simplest to create a tartan grid to capture the idealized fracture geometry correctly and to explicitly simulate the flow through the fracture. In addition to explicitly modeling the flow, the tartan grid method was chosen to prevent the overestimation of well performance (Giamminonni et al 2010).…”

“…Numerical reservoir simulation is the preferred tool because the reservoir flow problem depends on many pressure-dependent parameters that can affect both the fluid behavior and rock fluid interactions. Numerical solution techniques provide relatively accurate solutions given the correct input data (Aybar et al 2014;Giamminonni et al 2010;Orangi and Nagarajan 2015).…”

CO2 Injection to Mitigate Condensate Banking in a Low-Permeability Reservoir

“…Numerical reservoir simulation is the preferred tool because the reservoir flow problem depends on many pressure-dependent parameters that can affect both the fluid behavior and rock-fluid interactions (Giamminonni et al 2010;Orangi et al 2011). These interactions and changes are critical especially considering that with the injection of CO 2 the composition of the hydrocarbon mixture in place is changing with time and concentration of the injected fluid.…”