A new empirical correlation for estimating bubble point pressure using the genetic algorithm

Heidarian, Mohamad; Karimnezhad, Masoud; Schaffie, Mahin; Ranjbar, Mohammad

doi:10.7494/geol.2017.43.1.33

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Due to regional changes in crude oil compositions and properties, none of the correlations can be applied as an exact universal correlation. In recent years, genetic algorithms was used to increase the accuracy of PVT correlations ( uses the genetic algorithm, which is one the most powerful techniques of artificial intelligence in optimization) [12,13]. In 2016 researchers from King Said University of Egypt [12] prepared correlation to predict bubble point oil formation volume factor of universal volatile crude oils samples by using genetic programming.…”

Section: Empirical Correlations For Forecasting Of Pvt Properties Of Crude Oilmentioning

confidence: 99%

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Current Approaches in Prediction of PVT Properties of Reservoir Oils

Hajirahimova

Aybeniz

2018

Review of Information Engineering and Applications

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Bubble point pressure Bubble point oil formation factor Crude oil.PVT (pressure-volume-temperature) properties of reservoir fluids in the oil and gas industry constitute an integral part of the required data for a thorough study of the reservoir, optimally compilation of oil production and operation schemes. In the absence of PVT data that measured in laboratory conditions, empirical correlation is used to evaluate these properties. These correlations cannot be applied universally due to the differences of crude oil composition, the working condition of geographical and oil environment. In the article widespread correlations and models was investigated in the field of prediction of PVT properties of reservoir oil from different regions. Their accuracy and productivity was thoroughly analyzed too.

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Section: Empirical Correlations For Forecasting Of Pvt Properties Of Crude Oilmentioning

confidence: 99%

“…In 2017, researchers from Kerman Shahid Bahonar University of Iran M.Heidarian and colleagues [13]. by applying the genetic algorithm develop empirical corellation that expressed following formula for evaluating of bubble point pressure ( b P ) in the Middle East crude oil systems.…”

Section: Empirical Correlations For Forecasting Of Pvt Properties Of Crude Oilmentioning

confidence: 99%

Current Approaches in Prediction of PVT Properties of Reservoir Oils

Hajirahimova

Aybeniz

2018

Review of Information Engineering and Applications

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Application of intelligent models in reservoir and production engineering

Hemmati‐Sarapardeh¹,

Larestani²,

Amar³

et al. 2020

Applications of Artificial Intelligence Techniques in the Petroleum Industry

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Committee Machine-Ensemble as a General Paradigm for Accurate Prediction of Bubble Point Pressure of Crude Oil

Rashidi

Khajehesfandeari

2020

Journal of Energy Resources Technology

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Bubble point pressure (BPP) not only is a basic pressure-volume-temperature (PVT) parameter for calculation nearly all of crude oil characteristics, but it also determines phase type of oil reservoirs, gas to oil ratio, oil formation volume factor, inflow performance relationship, and so on. Since measurement of BPP of crude oil is an expensive and time-consuming experiment, this study develops a committee machine-ensemble (CME) paradigm for accurate estimation of this parameter from solution gas oil ratio, reservoir temperature, gas specific gravity, and stock-tank oil gravity. Our CME approach is designed using linear combination of predictions of four different expert systems. Unknown coefficients of this combination are adjusted through minimizing deviation between actual BPPs and their associated predictions using differential evolution and genetic algorithm. Our proposed CME paradigm is developed using 380 PVT datasets for crude oils from different geological regions. This novel intelligent paradigm estimates available experimental databank with an excellent accuracy i.e. absolute average relative deviation (AARD) of 6.06% and regression coefficient (R2) of 0.98777. Accurate prediction of BPP using our CME paradigm decreases the risk of producing from two-phase region of oil reservoirs.

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A new empirical correlation for estimating bubble point pressure using the genetic algorithm

Cited by 4 publications

References 21 publications

Current Approaches in Prediction of PVT Properties of Reservoir Oils

Current Approaches in Prediction of PVT Properties of Reservoir Oils

Application of intelligent models in reservoir and production engineering

Committee Machine-Ensemble as a General Paradigm for Accurate Prediction of Bubble Point Pressure of Crude Oil

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