Novel mathematical correlation for accurate prediction of gas compressibility factor

Ekechukwu, Gerald Kelechi; Orodu, O. D.

doi:10.1016/j.ngib.2019.09.001

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…The compressibility factor ( Z ) is calculated by making use of the following explicit correlation , Z = D P italicPr ( 1 + Y + Y 2 − Y 3 ) ( D P Pr + E Y 2 − F Y G ) false( 1 − Y false) 3 where Y = D P italicPr a + A 2 C − A 2 B C 3 Parameters A , B , C , D , E , F , and G are functions of the pseudo-reduced temperature and pressure. Expressions for these parameters can be found in Kareem et al…”

Section: Single Gas Pipeline Dynamic Model Developmentmentioning

confidence: 99%

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Natural Gas Well Production Network State-Space Model Development and Validation for Process Control

Dzedzemane,

Wiid,

le Roux

et al. 2024

Ind. Eng. Chem. Res.

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A transient state-space nonlinear model is developed for a natural gas system fed from multiple gas wellheads. The dynamic models are developed by making use of the spectral element method for pipeline spatial discretization. Wellhead models are integrated into the pipeline models by making use of suitable boundary conditions based on the characteristic compatibility method. The models are validated against a large scale natural gas well production network. The validation shows that the model has a good prediction performance based on a low normalized root-mean-square error of at most 5.08% and a high Pearson correlation coefficient with measured plant data of at least 0.94. The good prediction response of the developed transient models makes them suitable for use in model-based optimal control of natural gas well production networks. The resulting dynamic model can be easily adapted to a gas network of any configuration due to its modular form.

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Section: Single Gas Pipeline Dynamic Model Developmentmentioning

confidence: 99%

“…Gas Compressibility Factor. The compressibility factor (Z) is calculated by making use of the following explicit correlation 34,35 where P C is the pseudo-critical pressure and T C is the pseudo-critical temperature. The pseudo-critical temperature and pressures are calculated based knowledge of the gas composition.…”

mentioning

confidence: 99%

Natural Gas Well Production Network State-Space Model Development and Validation for Process Control

Dzedzemane,

Wiid,

le Roux

et al. 2024

Ind. Eng. Chem. Res.

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“…The standard procedure of acquiring this factor in the industry is to measure it using reservoir samples. The experimental methods of assessing this property at times are very expensive, and consumes time, hence methods such as correlations and equation of state are used to predict this parameter at reservoir pressure and temperature conditions [3].…”

Section: Introductionmentioning

confidence: 99%

Gas Compressibility Factor Prediction Using Machining Learning Algorithmic Protocol for Niger – Delta Gas Reservoir

Francis,

Irene

2023

IJCSRR

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The gas compressibility factor also known as Z-factor plays an important role in obtaining thermodynamic properties of natural gas reservoir fluid property. Typically, empirical correlations and complex equation of state have been applied to determine this parameter in absence of laboratory measurements. However, high cost of running experimental measurement, poor performance and some limitations associated with these existing correlations have made the researchers to use intelligent models instead. Therefore, this study aimed at adopting support vector machine algorithm to forecast gas compressibility factor and to validate its performance with predictions from Artificial Neural network (ANN) and some existing correlations using statistical and performance plot analysis. A total of 519 data sets from Niger Delta was used in developing the model, out of it, 70 percent was used for training, 20 percent for testing and 10 percent for validation using MATLAB tool. From the statistical analysis result, it was observed that the new developed model did better than other existing methods with numerical value of 0.1997 rank, 0.0009 mean absolute error and 0.98 of coefficient of correlation using the test data. The cross plot of the support vector machine model gave the tightest cloud along 45o reference line. The residual (error) associated with the performance was impressive which was done to observe the distribution and the interval at which the error is minimal.

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“…Natural gas is recently considered as a viable alternative energy source because of its availability, environmental friendliness and higher calorific value [1]. This growing significance has prompted the need for more efficient and accurate characterization of its properties [2]. Natural gas compressibility factor (Z) also known as gas deviation factor or simply 'Z-factor' is an important thermodynamic property of natural gas [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Simplification for the Prediction of Natural Gas Compressibility Factor

Cornelious

Olakunle

Niyi³

2021

JERR

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The need for a simpler, effective and less expensive predictive tool for the estimation of natural gas compressibility factor cannot be exaggerated. An accurate prediction of gas compressibility factor is essential because it plays a definitive role in evaluating gas reservoir properties used in the estimation of gas reserves, custody transfer and design of surface equipment. In this present work, a novel explicit correlation and a highly sophisticated computer program were developed to accurately predict natural gas deviation factor. The research also aims to effectively capture the relationship between Pseudo-reduced temperature and pressure in relations to the Z-factor. In this study, 3972 digitized data points extracted from Standing and Katz’s Chart were regressed and analyzed using Microsoft Excel Spreadsheet, the extraction of this data was done using WebPlotDigitizer developed by Ankit Rohatgi of GitHub, Pacifica, CA, USA. The correlation was developed as a function of Pseudo-reduced temperature and pressure with tuned parameters distributed across 1.05 ≤ Tpr ≤ 3.0 and 0 < Ppr ≤ 8.0. Subsequently, the input (Tpr and Ppr values) of the feed data was used to validate the correlation and compare it with other known and published correlations. Statistical analysis of the results showed that a 99.8% agreement exists between the predicted and actual compressibility factors for the various test scenarios and case studies involving both sweet and sour gases. Also, the correlation was observed to outperform other models. Finally, the results were observed to perfectly mimic the Standing and Katz charts with an overall correlation coefficient of 99.76% and an adjusted R2 of 99.75%. The proposed correlation was subsequently used to develop a software using JavaScript. Undoubtedly, the proposed correlation and software are suitable for rapid and accurate simplification and prediction of natural gas compressibility factor.

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Novel mathematical correlation for accurate prediction of gas compressibility factor

Cited by 7 publications

References 9 publications

Natural Gas Well Production Network State-Space Model Development and Validation for Process Control

Natural Gas Well Production Network State-Space Model Development and Validation for Process Control

Gas Compressibility Factor Prediction Using Machining Learning Algorithmic Protocol for Niger – Delta Gas Reservoir

A Novel Simplification for the Prediction of Natural Gas Compressibility Factor

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