New Correlations for Dew-Point Pressure for Gas Condensate
- Mohammad Al-Dhamen (Saudi Aramco) | Muhammad Al-Marhoun (King Fahd University of Petroleum and Minerals)
- Document ID
- Society of Petroleum Engineers
- SPE Saudi Arabia section Young Professionals Technical Symposium, 14-16 March, Dhahran, Saudi Arabia
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 5.2 Reservoir Fluid Dynamics, 4.6 Natural Gas, 5.8.8 Gas-condensate reservoirs, 4.1.2 Separation and Treating, 4.1.5 Processing Equipment, 5.2.1 Phase Behavior and PVT Measurements, 4.5 Offshore Facilities and Subsea Systems, 6.1.5 Human Resources, Competence and Training, 1.8 Formation Damage, 5.2 Fluid Characterization, 5.2.2 Fluid Modeling, Equations of State
- 5 in the last 30 days
- 686 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
New Models with three different techniques have been developed to predict the dew-point pressure for gas condensate reservoirs. Traditional correlations, non-parametric approaches and artificial neural networks have been utilized in this study. The new models are function of easily obtained parameters (reservoir temperature, gas specific gravity, condensate specific gravity and gas-oil ratio). A total number of 113 data sets obtained from Constant Mass Expansion experiment (CME) were collected from Middle East fields; has been used in developing the models. The data used for developing the models covers a reservoir temperature from 100 to 309 oF, gas oil ratios from 3,321 to 103,536 SCF/STB, gas specific gravity from 0.64 to 0.82 and condensate specific gravity from 0.73 to 0.81. The artificial neural network developed in this study has the best results among all other models with an average absolute error of 6.5%. Graphical and statistical tools have been utilized for the sake of comparing the performance of the new models and empirical models available in literature.
In reservoir engineering a variety of data is needed to accurately estimate reserves and forecast production. Field characterization consists of reservoir rock analysis and fluid analysis. The determination of gas condensate dew-point pressure is essential for fluid characterization, gas reservoir performance calculations, and for the design of production systems.
The phase diagram of a condensate gas is somewhat smaller than that for oils, and critical point is further down the left side of the envelope. These changes are a result of condensate gases containing fewer of the heavy hydrocarbons than do the oils. The phase diagram of a gas condensate has a critical temperature less than the reservoir temperature and a cricondentherm greater than the reservoir temperature (Figure-1). Initially, the gas condensate is totally gas in the reservoir, point 1. As reservoir pressure decreases, the gas condensate exhibits a dew-point, point 2. The dew-point of a gas condensate fluid occurs when a gas mixture containing heavy hydrocarbons is depressurized until liquid is formed, that is, a substantial amount of gas phase exists in equilibrium with an infinitesimal amount of liquid phase. As pressure is reduced, liquid condenses from the gas to form a free liquid in the reservoir. Normally, there is no effective permeability to the liquid phase and it is not produced.
Traditionally, the dew-point pressure of gas condensate is experimentally determined in a laboratory in a process called constant mass expansion (CME) test using a visual window-type PVT cell. Another study is constant volume depletion test (CVD) which verifies the thermodynamic equilibrium at each pressure depletion level, and describes the change of composition of the reservoir gas with every decreasing pressure step. The present study focuses on prediction of the dew-point pressure for gas condensate reservoir. Three different approaches will be used to predict the dew-point pressure; traditional correlations, non-parametric approach and artificial neural networks.
|File Size||385 KB||Number of Pages||18|