Improved Method for the Estimation of Minimum Miscibility Pressure for Pure and Impure CO2–Crude Oil Systems Using Gaussian Process Machine Learning Approach

Ekechukwu, Gerald Kelechi; Falode, Olugbenga; Orodu, O. D.

doi:10.1115/1.4047322

Cited by 11 publications

(5 citation statements)

References 65 publications

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“…Li et al [28] evaluated the reliability of four machine learning-based prediction models including neural network analysis (NNA), genetic function approximation (GFA), multiple linear regression (MLR), and partial least squares (PLS) using 136 sets of data. Other machine learning models have also been developed for MMP prediction, such as those developed by the authors of [18,[29][30][31][32].…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Key Parameters in the Design of CO2 Miscible Injection via the Application of Machine Learning Algorithms

Hamadi,

El Mehadji,

Laalam

et al. 2023

Eng

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The accurate determination of key parameters, including the CO2-hydrocarbon solubility ratio (Rs), interfacial tension (IFT), and minimum miscibility pressure (MMP), is vital for the success of CO2-enhanced oil recovery (CO2-EOR) projects. This study presents a robust machine learning framework that leverages deep neural networks (MLP-Adam), support vector regression (SVR-RBF) and extreme gradient boosting (XGBoost) algorithms to obtained accurate predictions of these critical parameters. The models are developed and validated using a comprehensive database compiled from previously published studies. Additionally, an in-depth analysis of various factors influencing the Rs, IFT, and MMP is conducted to enhance our understanding of their impacts. Compared to existing correlations and alternative machine learning models, our proposed framework not only exhibits lower calculation errors but also provides enhanced insights into the relationships among the influencing factors. The performance evaluation of the models using statistical indicators revealed impressive coefficients of determination of unseen data (0.9807 for dead oil solubility, 0.9835 for live oil solubility, 0.9931 for CO2-n-Alkane interfacial tension, and 0.9648 for minimum miscibility pressure). One notable advantage of our models is their ability to predict values while accommodating a wide range of inputs swiftly and accurately beyond the limitations of common correlations. The dataset employed in our study encompasses diverse data, spanning from heptane (C7) to eicosane (C20) in the IFT dataset, and MMP values ranging from 870 psi to 5500 psi, covering the entire application range of CO2-EOR. This innovative and robust approach presents a powerful tool for predicting crucial parameters in CO2-EOR projects, delivering superior accuracy, speed, and data diversity compared to those of the existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of Key Parameters in the Design of CO2 Miscible Injection via the Application of Machine Learning Algorithms

Hamadi,

El Mehadji,

Laalam

et al. 2023

Eng

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show abstract

“…With regard to classical forecasting methods, ARIMA and SARIMA models have been used successfully in plentiful applications such as oil price prediction [6][7][8][9]. The two models for our proposed experiments are considered supervised learning; hence, machine learning algorithms may be applied to create a model based on training data to make predictions or judgments without being explicitly programmed [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe

Lee

Nguyen

et al. 2022

Energies

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Crude oil price volatility impacts the global economy in general, as well as the economies of Europe and the United States in particular; it is supremely difficult to describe its tendency precisely, hence it leads to a forecasting methodology. This study aims to use the autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) approaches to cope with this problem in the United States and Europe. The data was gathered from the U.S. Energy Information Administration and federal research economic data (FRED) from January 2017 to September 2021. Simultaneously, values from January 2017 to March 2021, with 51 observations accounting for 90% of the total samples, were employed for the training phase, and the rest were used for the testing phase. The forecast result also indicated that the root mean square error (RMSE) and mean absolute percentage error (MAPE) values, applied by ARIMA models in Europe and the United States, have higher accurate indicators than SARIMA models. As a result, the ARIMA model achieved the best accuracy in both Europe and the USA, with MAPEEurope−ARIMA = 0.05, and MAPEUSA−ARIMA=0.05. Based on these accuracy parameters, the forecasting models appear incredibly reliable; similarly, the study results might assist governing bodies in making significant decisions, thereby accelerating socio-economic development in the world’s two largest economies.

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“…In a previous study [ 29 ], we compared four estimation methods and found that the machine learning intelligent algorithm had a higher precision to the MMP than pure linear model. In addition, some reports that combined multiple approaches showed better results [ 30 , 31 , 32 , 33 , 34 ].…”

Section: Introductionmentioning

confidence: 99%

Determination of Minimum Miscibility Pressure of CO2–Oil System: A Molecular Dynamics Study

Zhang

et al. 2021

Molecules

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CO2 enhanced oil recovery (CO2-EOR) has become significantly crucial to the petroleum industry, in particular, CO2 miscible flooding can greatly improve the efficiency of EOR. Minimum miscibility pressure (MMP) is a vital factor affecting CO2 flooding, which determines the yield and economic benefit of oil recovery. Therefore, it is important to predict this property for a successful field development plan. In this study, a novel model based on molecular dynamics to determine MMP was developed. The model characterized a miscible state by calculating the ratio of CO2 and crude oil atoms that pass through the initial interface. The whole process was not affected by other external objective factors. We compared our model with several famous empirical correlations, and obtained satisfactory results—the relative errors were 8.53% and 13.71% for the two equations derived from our model. Furthermore, we found the MMPs predicted by different reference materials (i.e., CO2/crude oil) were approximately linear (R2 = 0.955). We also confirmed the linear relationship between MMP and reservoir temperature (TR). The correlation coefficient was about 0.15 MPa/K in the present study.

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Improved Method for the Estimation of Minimum Miscibility Pressure for Pure and Impure CO2–Crude Oil Systems Using Gaussian Process Machine Learning Approach

Cited by 11 publications

References 65 publications

Prediction of Key Parameters in the Design of CO2 Miscible Injection via the Application of Machine Learning Algorithms

Prediction of Key Parameters in the Design of CO2 Miscible Injection via the Application of Machine Learning Algorithms

Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe

Determination of Minimum Miscibility Pressure of CO2–Oil System: A Molecular Dynamics Study

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