KPIM of Gas/Condensate Productivity: Prediction of Condensate/Gas Ratio (CGR) Using Reservoir Volumetric Balance

Abstract

Accurate forecasting of condensate well deliverability usually requires good knowledge of the gas condensate vapor – liquid properties. Condensate well deliverability is particularly important as it impacts downstream issues such as the number of wells required, surface gas handling facilities, drilling schedules and income from gas sales contracts. A new approach for forecasting viability of gas condensate wells and calculating condensate gas ratio (CGR), using simpler techniques is presented. The calculation uses a volumetric balance model for reservoir system, standardized and modified correlations, equation of state and a vapor-liquid equilibrium technique. The technique has been extended to include mass transfer and also to allow for the changes in produced fluid composition due to the formation of the condensate bank. The approach will provide a useful tool for rapid forecasts of condensate well performance, for examining the effects of condensate blockage in different well types or for studying sensitivities. It is also valuable where simple models of condensate reservoir performance are required for use in integrated studies.

Prediction of Condensate-to-Gas Ratio for Retrograde Gas Condensate Reservoirs Using Artificial Neural Network with Particle Swarm Optimization

“…Gas condensate reservoirs generally produce in the range of 30–300 STB/MMSCF (standard barrels of liquid per million standard cubic feet of gas). ,,,,− ,− The depth of majority of the reported retrograde gas condensate fields ranges from 4000–10,000 ft (∼1200–3000 m). ,,,,− ,− Also, the ranges of pressure ( P ) and temperature ( T ) for this type of gas reservoirs are usually between 3000–8500 psi (20–55 MPa) and 150–400 °F (65–200 °C), respectively. ,,,,− ,− Clearly, few gas condensate reservoirs may be found out of the ranges reported above for T , P , and depth as gas condensate reservoirs may have pressures lower than 2000 psi and temperatures under l00 °F . The pressure and temperature values along with the wide range of compositions result in gas condensate mixtures that exhibit different and complex thermodynamic behaviors. − ,, …”

“…In addition to the data generated from the experimental part, some data from the literature were also used in developing the PSO-ANN model for CGR prediction. A part of the data used in this study is presented in Appendix A, and the rest can be tracked in the open literature. ,,,,− ,− …”

“…A set of 295 experimental data was collected to develop and examine the proposed PSO-ANN model. The data series includes 45 data from South Pars gas-condensate reservoir and 250 data from the literature. ,,,,− ,− The ranges of the parameters used to develop the PSO-ANN are given in Table .…”

SVM modeling of the constant volume depletion (CVD) behavior of gas condensate reservoirs

“…The near well choking can reduce the productivity of a well by a factor of two or more (Fan et al, 1998;Roussennac, 2001). Therefore, a reliable prediction of the performance of gas condensate reservoir during pressure depletion is necessary in evaluating new fields and determining their reserve Boom et al, 1996;Morel et al, 1997;Olaberinjo et al, 2006). Lack of sufficient knowledge about gas condensate reservoirs not only leaves huge amounts of precious condensates in the reservoir but also severely damages the well productivity and lowers the gas deliverability through formation of condensate in the reservoir, especially in near well bore areas (Al-Anazi et al, 2012;Barnum et al,1995).…”

“…There are also analytical methods for characterization of gas condensate reservoir fluids which are based on PVT properties of reservoir fluids and use material balance and phase behavior relationship for this purpose. Work of Olaberinjo et al (2006) which is basically focused on viability and performance of gas condensate reservoirs (constant volume depletion calculation and estimation of condensate gas ratios close to the wellbore) using reservoir volumetric balance is an example of this type of studies. Since reservoir fluids usually consist of a large number of components, analytical methods require a significant amount of computational effort and are very cumbersome to apply Ghiasi et al, 2014).…”