Simple and Efficient Compositional Based Models to Estimate the Saturation Pressure of Oil Reservoirs

Journal of Engineering Research

Hassan Alkandari¹ 

[1] Kuwait University

关键词: Saturation pressure (Ps);    Bubble-point pressure (BPP);    Equation of state (EOS);    Correlations;    and PVT Data;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: Kuwait University * Academic Publication Council

【 摘 要 】

Saturation-pressure measurements are essential for all hydrocarbon reservoir fluids.  Gas reaches a critical saturation, below the crude oil saturation pressure; then, a two phase flow take place and this action results in decreasing oil production and recovery.  One of the reservoir engineers goals is to optimize the production of oil and to maximize oil recovery, to reach this goal, the reservoir pressure must be retained very close to the initial reservoir saturation pressure.  Saturation pressure is normally measured by using a bottom-hole samples or samples that are recombination of gas and oil at surface.  In most cases, real samples are unavailable at elevated pressures; therefore, saturation pressure needs to be estimated either by simulation or computation methods. A set of crude oil saturation pressure and compositions measurements including data from literature, and newly measured data were used to develop two practical models to predict the saturation pressure of a 214 crude oils. The first developed model uses the extended compositions of hydrocarbons up to the heptane plus fraction in addition to non-hydrocarbons. The second model utilizes the lumping criteria for compositions of light components, intermediate components, and heavy components in addition to non-hydrocarbon components as an input. The models’ performance is also compared to the Soave-Redlich-Kwong and Peng-Robinson equation-of-states in addition to published methods that uses compositions as an input.  The comparison indicates that the proposed models are easier to implement and more accurate than the other computational methods

【 授权许可】

Unknown