On the determination of CO 2 –crude oil minimum miscibility pressure using genetic programming combined with constrained multivariable search methods

Fathinasab, Mohammad; Ayatollahi, Shahab

doi:10.1016/j.fuel.2016.01.009

Cited by 39 publications

(16 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Various gases are utilized for the injection purpose, including natural gas, flue gas, nitrogen, and supercritical CO 2 resulting in various levels of success in operational and economic aspects [1,2]. Among mentioned gases, high solubility of CO 2 in oil reservoir results in an extreme mass transfer between the phases [1,2], interfacial tension reduction which increase oil sweep by reducing viscosity between the phases [3] and final recovery up to 90% [4,5]. Injecting CO 2 as a greenhouse gas and removing it from atmosphere has also environmental benefits by storing this detrimental gas under the ground [4,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…To obtain MMP, different experimental and computational methods are available. Experimental methods include slim tube test, Vanishing Interfacial Tension (VIT) technique, multi-contact mixing-cell experiment and rising bubble apparatus [1]. Computation methods contain two main groups: (1) Equation of State (EoS), (2) empirical correlations.…”

Section: Introductionmentioning

confidence: 99%

“…This will result in more accurate MMP measurements. On the other hand, performing slim tube experiment is substantially time and money consuming [1,9]. Therefore, application of this in miscible injection design where a great number of MMPs should be determined would not be feasible.…”

Section: Introductionmentioning

confidence: 99%

“…Fathinasab and Ayatollahi [1] introduced a correlation for MMP prediction combining genetic programming with the multivariate search method based on reservoir temperature, C 5+ molecular weight, injected gas pseudocritical temperature, and the ratio of light to intermediate components. Using gene expression programming, Ahmadi et al…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO₂injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor

Delforouz

Movaghar

Shariaty

2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

Miscible injection of carbon dioxide (CO2) with ability to increase oil displacement as well as to reduce greenhouse effect has become one of the pioneering methods in Enhanced Oil Recovery (EOR). Minimum Miscibility Pressure (MMP) is known as a key indicator to ensure complete miscibility of two phases and maximum efficiency of injection process. There are various experimental and computational methods to calculate this key parameter. Experimental methods provide the most accurate and valid results. However, such methods are time consuming and expensive leading researchers to use mathematical methods. Among computational methods, empirical correlations are the most straight-forward and simple tools to precisely estimate MMP, especially for gases with impurities. Furthermore, in predicting the miscibility state of oil–gas system, phase behavior is a vital issue which should be taken into account to achieve reliable results. In this regard, equations of state have an indisputable role in predicting the phase behavior of reservoir fluids. Remarkable improvements have been introduced to elevate performance of equations of state, based on Pitzer’s acentric factor. Hereupon, this study aims to enumerate acentric factor of injected gas (impure CO2) as a correlating parameter alongside conventional parameters including reservoir temperature, oil constituents (molecular weight of C5+, ratio of volatiles to intermediates) and critical properties of injected gas (pseudo-critical pressure & temperature). Thus, in this study an effective empirical correlation is created, implementing the Group Method of Data Handling (GMDH) algorithm along with including the acentric factor of injected gas, which eventuated to precise predictions of MMP for impure CO2 injection. The GMDH is one of the most robust mathematical modeling methods for predicting physical parameters using linear equations. A comparison with well-known correlations, demonstrated at least 2% improvement in average absolute error with enumerating the acentric factor and the final error was equal to 12.89%.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO₂injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor

Delforouz

Movaghar

Shariaty

2019

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

View full text Add to dashboard Cite

show abstract

“…Similarly, Shokir [27] developed a new correlation to determine the MMP of pure and contaminated CO 2 by applying alternative conditional expectation (ACE) algorithm. Using genetic programming combined with constrained multivariable search methods, Fathinasab and Ayatollahi [28] developed a correlation to calculate both pure and impure CO 2 -oil MMP in live reservoir oil.…”

Section: Introductionmentioning

confidence: 99%

Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams

Bian

Han

et al. 2016

Fuel

View full text Add to dashboard Cite

Toward robust models for predicting carbon dioxide absorption by nanofluids

et al. 2022

View full text Add to dashboard Cite

The application of nanofluids has received increased attention across a number of disciplines in recent years. Carbon dioxide (CO2) absorption by using nanofluids as the solvents for the capture of CO2 is among the attractive applications, which have recently gained high popularity in various industrial aspects. In this work, two robust explicit‐based machine learning (ML) methods, namely group method of data handling (GMDH) and genetic programming (GP) were implemented for establishing accurate correlations that can estimate the absorption of CO2 by nanofluids. The correlations were developed using a comprehensive database that involved 230 experimental measurements. The obtained results revealed that the proposed ML‐based correlations can predict the absorption of CO2 by nanofluids with high accuracy. Besides, it was found that the GP‐based correlation yielded more precise predictions compared to the GMDH‐based correlation. The GP‐based correlation has an overall coefficient of determination of 0.9914 and an overall average absolute relative deviation of 3.732%. Lastly, the carried‐out trend analysis confirmed the compatibility of the proposed GP‐based correlation with the real physical tendency of CO2 absorption by nanofluids. © 2022 Society of Chemical Industry and John Wiley & Sons, Ltd.

show abstract

On the determination of CO 2 –crude oil minimum miscibility pressure using genetic programming combined with constrained multivariable search methods

Cited by 39 publications

References 52 publications

New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO₂injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor

New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO₂injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor

Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams

Toward robust models for predicting carbon dioxide absorption by nanofluids

Contact Info

Product

Resources

About

On the determination of CO 2 –crude oil minimum miscibility pressure using genetic programming combined with constrained multivariable search methods

Cited by 39 publications

References 52 publications

New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO2injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor

New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO2injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor

Integrating support vector regression with genetic algorithm for CO2-oil minimum miscibility pressure (MMP) in pure and impure CO2 streams

Toward robust models for predicting carbon dioxide absorption by nanofluids

Contact Info

Product

Resources

About

New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO₂injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor

New empirical correlations for predicting Minimum Miscibility Pressure (MMP) during CO₂injection; implementing the Group Method of Data Handling (GMDH) algorithm and Pitzer’s acentric factor