A machine learning model for predicting the minimum miscibility pressure of CO2 and crude oil system based on a support vector machine algorithm approach

Chen, Hao; Zhang, Chao; Jia, Ning; Duncan, Ian; Yang, Shenglai; Yang, Yongzhi

doi:10.1016/j.fuel.2020.120048

Cited by 62 publications

(53 citation statements)

References 34 publications

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“…The support vector machine model, or SVM, is a key approach in ML for many disciplines for different data sizes. The SVM provides rapid and robust answers to regression tasks [ 26 , 27 , 28 ]. SVM-based learning algorithms are especially appropriate to problems requiring previously unknown data, and they can be used to simply refine the solution.…”

Section: Introductionmentioning

confidence: 99%

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

et al. 2022

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Industrial-based application of supercritical CO2 (SCCO2) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C15H18O3) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO2, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10−4, 7.814 × 10−5, and 1.664 × 10−4 for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10−4, 6.197 × 10−5, and 8.777 × 10−5errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x1 = 40.0, x2 = 338.0, Y = 1.27 × 10−3) vector.

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Section: Introductionmentioning

confidence: 99%

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

et al. 2022

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“…Reducing MMP is a common method to realize CO 2 miscible flooding [10,11]. The purity of injected CO 2 , the viscosity of formation crude oil, formation temperature, the composition of crude oil, and pore size are all influencing factors of MMP [12][13][14][15]. The main direction of reducing MMP between crude oil and CO 2 is to change the properties of carbon dioxide and crude oil [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

How Is Ultrasonic-Assisted CO2 EOR to Unlock Oils from Unconventional Reservoirs?

et al. 2021

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CO2 enhanced oil recovery (EOR) has proven its capability to explore unconventional tight oil reservoirs and the potential for geological carbon storage. Meanwhile, the extremely low permeability pores increase the difficulty of CO2 EOR and geological storage processing in the actual field. This paper initiates the ultrasonic-assisted approach to facilitate oil–gas miscibility development and finally contributes to excavating more tight oils. Firstly, the physical properties of crude oil with and without ultrasonic treatments were experimentally analyzed through gas chromatography (GC), Fourier-transform infrared spectroscopy (FTIR) and viscometer. Secondly, the oil–gas minimum miscibility pressures (MMPs) were measured from the slim-tube test and the miscibility developments with and without ultrasonic treatments were interpreted from the mixing-cell method. Thirdly, the nuclear-magnetic resonance (NMR) assisted coreflood tests were conducted to physically model the recovery process in porous media and directly obtain the recovery factor. Basically, the ultrasonic treatment (40 KHz and 200 W for 8 h) was found to substantially change the oil properties, with viscosity (at 60 °C) reduced from 4.1 to 2.8 mPa·s, contents of resin and asphaltene decreased from 27.94% and 6.03% to 14.2% and 3.79%, respectively. The FTIR spectrum showed that the unsaturated C-H bond, C-O bond and C≡C bond in macromolecules were broken from the ultrasonic, which caused the macromolecules (e.g., resin and asphaltenes) to be decomposed into smaller carbon-number molecules. Accordingly, the MMP was determined to be reduced from 15.8 to 14.9 MPa from the slim-tube test and the oil recovery factor increased by an additional 11.7%. This study reveals the mechanisms of ultrasonic-assisted CO2 miscible EOR in producing tight oils.

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“…Based on the principle, back propagation (BP) [ 23 ] and radial basis function (RBF) [ 24 ] are proposed. Beyond that, a series of optimization methods such as genetic algorithm (GA) [ 25 ], particle swarm optimization (PSO) [ 26 ], support vector machine (SVM) [ 27 ], and hybrid-ANFIS [ 28 ] have also been developed for MMP determination. 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.…”

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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A machine learning model for predicting the minimum miscibility pressure of CO2 and crude oil system based on a support vector machine algorithm approach

Cited by 62 publications

References 34 publications

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

How Is Ultrasonic-Assisted CO2 EOR to Unlock Oils from Unconventional Reservoirs?

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

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