An Efficient Method to Predict Compressibility Factor of Natural Gas Streams

Abstract

The gas compressibility factor, also known as the deviation or Z-factor, is one of the most important parameters in the petroleum and chemical industries involving natural gas, as it is directly related to the density of a gas stream, hence its flow rate and isothermal compressibility. Obtaining accurate values of the Z-factor for gas mixtures of hydrocarbons is challenging due to the fact that natural gas is a multicomponent, non-ideal system. Traditionally, the process of estimating the Z-factor involved simple empirical correlations, which often yielded weak results either due to their limited accuracy or due to calculation convergence difficulties. The purpose of this study is to apply a hybrid modeling technique that combines the kernel ridge regression method, in the form of the recently developed Truncated Regularized Kernel Ridge Regression (TR-KRR) algorithm, in conjunction with a simple linear-quadratic interpolation scheme to estimate the Z-factor. The model is developed using a dataset consisting of 5616 data points taken directly from the Standing–Katz chart and validated using the ten-fold cross-validation technique. Results demonstrate an average absolute relative prediction error of 0.04%, whereas the maximum absolute and relative error at near critical conditions are less than 0.01 and 2%, respectively. Most importantly, the obtained results indicate smooth, physically sound predictions of gas compressibility. The developed model can be utilized for the direct calculation of the Z-factor of any hydrocarbon mixture, even in the presence of impurities, such as N 2 , CO 2 , and H 2 S, at a pressure and temperature range that fully covers all upstream operations and most of the downstream ones. The model accuracy combined with the guaranteed continuity of the Z-factor derivatives with respect to pressure and temperature renders it as the perfect tool to predict gas density in all petroleum engineering applications. Such applications include, but are not limited to, hydrocarbon reserves estimation, oil and gas reservoir modeling, fluid flow in the wellbore, the pipeline system, and the surface processing equipment.

Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network

“…The correlation was limited to a pseudo-reduced temperatures higher than 0.92. The Dranchuk and Abou-Kassem (DAK) correlation for the calculations of gas density and compressibility is currently considered a standard in the petroleum industry [1]. The correlation was conducted based on Benedict's equation of state, as follows [17].…”

“…Knowledge of the physical properties of gas or oil related to pressure, volume, and temperature (PVT) is of great importance to petroleum engineers and academics. The physical properties of natural gas, which are used in the estimation of hydrocarbon reserves in reservoirs, the study of gas flow in the reservoir and wellbore and its thermodynamic behavior are essential in planning, gas metering, the design of pipelines, and surface facilities [1]. Most of these properties can be determined in a PVT laboratory.…”

“…The high expense of laboratory equipment and the lack of these laboratories are the main reason for seeking alternative techniques to determine or predict the Z factor of natural gases. The most well-known ways to predict the Z factor comprise the EOS, empirical correlations, and artificial intelligence [1,[4][5][6]. In the past, EOS was developed and adapted to determine many of the physical properties of oil and gas, especially when dealing with volumetric properties and Z factor calculations [6][7][8].…”

Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems

“…A combination of different methods can be used to improve the compressibility factor prediction. The work done by Gaganis [26] combined the truncated regularized kernel ridge regression (TR-KRR) algorithm with a simple linear-quadratic interpolation scheme for estimation of the Z-factor. The maximum absolute relative prediction error is around the critical point was obtained around 2%.…”

Application of Machine Learning to Accelerate Gas Condensate Reservoir Simulation

“…Finally, Ashraf et al utilized petrophysical, mineral composition, well-log facies and horizon attribute analyses in conjunction with VQ and sequential indicator simulation (SIS) modeling [1] to define subsurface geobodies. ML based fluids properties models have also been proposed [36][37][38].…”