New Correlation for Calculating Critical Pressure of Petroleum Fractions

Gomaa, Sayed

doi:10.17148/iarjset.2016.31105

International Advanced Research Journal in Science, Engineering

2016

DOI: 10.17148/iarjset.2016.31105

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New Correlation for Calculating Critical Pressure of Petroleum Fractions

Sayed Gomaa¹

Abstract: Abstract:Critical pressure is one of the most important physical properties of the petroleum fractions, which commonly considered in compositional modeling studies and phase behavior calculations. This paper presents a comparison study among ten different correlations used to calculate the critical pressure of undefined petroleum fractions. A new correlation was developed for calculating the critical pressure of petroleum fractions as a function of the number of carbon atoms with an average error of 0.933977 %… Show more

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Cited by 3 publications

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Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

Ghorbani

Wood

Choubineh

et al. 2019

Exp. Comput. Multiph. Flow

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Pressure-Volume-Temperature (PVT) characterization of a crude oil involves establishing its bubble point pressure, which is the pressure at which the first gas bubble forms on a fluid sample while reducing pressure at a stabilized temperature. Although accurate measurement can be made experimentally, such experiments are expensive and time-consuming. Consequently, applying reliable artificial intelligence (AI)/machine learning methods to provide an accurate mathematical prediction of an oil's bubble point pressure from more easily measured characteristics can provide valuable cost and time savings. This paper develops and compares four neurocomputing models applying algorithms consisting of a Multilayer Perceptron (MLP), a Radial Basis Function trained with a Genetic Algorithm (RBF-GA), a Combined Hybrid Particle Swarm Optimization-Adaptive Neuro-Fuzzy Inference System (CHPSO-ANFIS), and Least Squared Support Vector Machine (LSSVM) tuned with a coupled simulated annealing (CSA) optimizer. Based on a comprehensive analysis, although the four proposed models yield acceptable outputs, the CHPSO-ANFIS model has the best performance with the average absolute relative deviation of 0.846, the standard deviation of 0.0126, the root mean square error of 43.21, and the correlation coefficient of 0.9902. These algorithms are deployed for the accurate estimation of the bubble point pressure from the giant Ahvaz oil field (Iran).

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Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

Ghorbani

Wood

Choubineh

et al. 2019

Exp. Comput. Multiph. Flow

View full text Add to dashboard Cite

show abstract

Deep Learning Approach for Robust Prediction of Reservoir Bubble Point Pressure

et al. 2021

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The bubble point pressure ( P b ) is a crucial pressure–volume–temperature (PVT) property and a primary input needed for performing many petroleum engineering calculations, such as reservoir simulation. The industrial practice of determining P b is by direct measurement from PVT tests or prediction using empirical correlations. The main problems encountered with the published empirical correlations are their lack of accuracy and the noncomprehensive data set used to develop the model. In addition, most of the published correlations have not proven the relationships between the inputs and outputs as part of the validation process (i.e., no trend analysis was conducted). Nowadays, deep learning techniques such as long short-term memory (LSTM) networks have begun to replace the empirical correlations as they generate high accuracy. This study, therefore, presents a robust LSTM-based model for predicting P b using a global data set of 760 collected data points from different fields worldwide to build the model. The developed model was then validated by applying trend analysis to ensure that the model follows the correct relationships between the inputs and outputs and performing statistical analysis after comparing the most published correlations. The robustness and accuracy of the model have been verified by performing various statistical analyses and using additional data that was not part of the data set used to develop the model. The trend analysis results have proven that the proposed LSTM-based model follows the correct relationships, indicating the model’s reliability. Furthermore, the statistical analysis results have shown that the lowest average absolute percent relative error (AAPRE) is 8.422% and the highest correlation coefficient is 0.99. These values are much better than those given by the most accurate models in the literature.

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An Accurate Reservoir’s Bubble Point Pressure Correlation

et al. 2022

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Bubble point pressure ( P b ) is essential for determining petroleum production, simulation, and reservoir characterization calculations. The P b can be measured from the pressure–volume–temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. Therefore, some studies used alternative methods, namely, empirical correlations and machine learning techniques, to obtain the P b . However, the previously published methods have restrictions like accuracy, and some use specific data to build their models. In addition, most of the previously published models have not shown the proper relationships between the features and targets to indicate the correct physical behavior. Therefore, this study develops an accurate and robust correlation to obtain the P b applying the Group Method of Data Handling (GMDH). The GMDH combines neural networks and statistical methods that generate relationships among the feature and target parameters. A total of 760 global datasets were used to develop the GMDH model. The GMDH model is verified using trend analysis and indicates that the GMDH model follows all input parameters’ exact physical behavior. In addition, different statistical analyses were conducted to investigate the GMDH and the published models’ robustness. The GMDH model follows the correct trend for four input parameters (gas solubility, gas specific gravity, oil specific gravity, and reservoir temperature). The GMDH correlation has the lowest average percent relative error, root mean square error, and standard deviation of 8.51%, 12.70, and 0.09, respectively, and the highest correlation coefficient of 0.9883 compared to published models. The different statistical analyses indicated that the GMDH is the first rank model to accurately and robustly predict the P b .

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New Correlation for Calculating Critical Pressure of Petroleum Fractions

Cited by 3 publications

References 6 publications

Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

Deep Learning Approach for Robust Prediction of Reservoir Bubble Point Pressure

An Accurate Reservoir’s Bubble Point Pressure Correlation

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