Open AccessA Novel Simplification for the Prediction of Natural Gas Compressibility Factor
Author(s) -
Omobolanle Oluwasegun Cornelious,
Akinsete Oluwatoyin Olakunle,
Aromokeye Niyi
Publication year - 2021
Publication title -
journal of engineering research and reports
Language(s) - English
Resource type - Journals
ISSN - 2582-2926
DOI - 10.9734/jerr/2021/v20i817360
Subject(s) - compressibility factor , compressibility , natural gas , microsoft excel , correlation coefficient , correlation , chart , absolute deviation , mathematics , computer science , statistics , thermodynamics , chemistry , physics , geometry , organic chemistry , operating system
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.