Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates

Gjelsvik, Elise Lunde; Fossen, Martin; Tøndel, Kristin

doi:10.1016/j.fuel.2022.126696

Cited by 11 publications

(4 citation statements)

References 96 publications

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“…The rapid development of science and technology has led to the diversification of energy sources, but petroleum is still one of the most important energy sources in the world, playing a pivotal role across industries. , While petroleum extraction has progressed to its middle and late stages, heavy oil extraction remains challenging . This challenge stems from the high viscosity, density, and boiling point of heavy oil, often surpassing 50 mPa/s –1 in viscosity with low fluidity.…”

Section: Introductionmentioning

confidence: 99%

Functionalized Ti₃C₂T_x MXene Nanosheets for Increased Emulsion Viscosity and Enhanced Heavy Oil Recovery

He,

Wang,

Yang

et al. 2024

ACS Appl. Nano Mater.

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Section: Introductionmentioning

confidence: 99%

Functionalized Ti₃C₂T_x MXene Nanosheets for Increased Emulsion Viscosity and Enhanced Heavy Oil Recovery

He,

Wang,

Yang

et al. 2024

ACS Appl. Nano Mater.

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“…Machine learning approach could potentially be used to improve prediction accuracy of gas hydrate [53]. In the future, it could be worth to seek additional insights into gas hydrate research through machine learning [54]. In a recent study, Least Squares Support Vector Machines (LSSVMs) and Arti cial Neural Networks (ANNs) were used to estimate gas hydrate formation for different gas mixtures contains CH 4 , CO 2 , N 2 , H 2 and H 2 O-soluble organic promoter [55].…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning on hydrate formation prediction of pure components with water and inhibitors solution

Nasir,

Suleman,

Abdul Majeed

2024

Can J Chem Eng

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The present work investigates the use of machine learning approaches for the prediction of hydrate formation pressure (HFP) in gas hydrate systems. Advanced machine learning models, including the decision tree regressor (DTR), random forest regressor (RFR), extreme gradient boosting (XGB), gradient boosting regressor (GBR), histogram gradient boosting regressor (HGBR), and CatBoost regressor (CB), are trained and evaluated on a large dataset consists of 3137 experimental data points. The models are evaluated using R‐squared (R2), mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). The study indicates that for the intent of HFP prediction, CatBoost outperformed all other machine learning models. It demonstrated high accuracy on the testing set with an R2 value of 0.9922, and with the lowest RMSE (1.61 × 10−3), MAE (7.90 × 10−4), and MSE (2.58 × 10−6), CatBoost strengthened its prediction ability.

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“…Crude oil properties such as density, viscosity, or boiling point are often used as measures for rapid assessments of crude oils, as they are determined by the chemical composition of the oil, and thereby related to the crude oil components. 3 Chemometric methods have been used for the identification of crude oil properties for decades, 4–6 and the field of petroleomics 2 arose from identification of crude oil components using high-resolution mass spectrometry (MS).…”

Section: Introductionmentioning

confidence: 99%

Crude Oil Density Prediction Improved by Multiblock Analysis of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Fourier Transform Infrared, and Near-Infrared Spectroscopy Data

et al. 2023

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Crude oils are among the world’s most complex organic mixtures containing a large number of unique components and many analytical techniques lack resolving power to characterize. Fourier transform ion cyclotron resonance mass spectrometry offers a high mass accuracy, making a detailed analysis of crude oils possible. Infrared (IR) spectroscopic methods such as Fourier transform IR spectroscopy (FT-IR) and near-IR, can also be used for crude oil characterization. The three methods measure different properties of the samples, and different data sources can often be combined to improve the prediction accuracy of models. In this study, partial least squares regression (PLSR) models for each of the three methods (single-block PLSR) were compared to multiblock PLSR and sequential and orthogonalized PLSR (SO-PLSR), with the aim of predicting the density of crude oils. Variable importance in projection was used to identify the important variables for each method, as spectroscopic data often contain irrelevant variation. The variables were interpreted to evaluate their underlying chemistry and to check whether consistency could be found between the variables selected from the spectroscopic data for the single-block and multiblock methods. Combining the different blocks of data increased the prediction abilities of the models both before and after variable selection, and SO-PLSR using a reduced data set resulted in the best-performing prediction model.

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Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates

Cited by 11 publications

References 96 publications

Functionalized Ti₃C₂T_x MXene Nanosheets for Increased Emulsion Viscosity and Enhanced Heavy Oil Recovery

Functionalized Ti₃C₂T_x MXene Nanosheets for Increased Emulsion Viscosity and Enhanced Heavy Oil Recovery

Application of machine learning on hydrate formation prediction of pure components with water and inhibitors solution

Crude Oil Density Prediction Improved by Multiblock Analysis of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Fourier Transform Infrared, and Near-Infrared Spectroscopy Data

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Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates

Cited by 11 publications

References 96 publications

Functionalized Ti3C2Tx MXene Nanosheets for Increased Emulsion Viscosity and Enhanced Heavy Oil Recovery

Functionalized Ti3C2Tx MXene Nanosheets for Increased Emulsion Viscosity and Enhanced Heavy Oil Recovery

Application of machine learning on hydrate formation prediction of pure components with water and inhibitors solution

Crude Oil Density Prediction Improved by Multiblock Analysis of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Fourier Transform Infrared, and Near-Infrared Spectroscopy Data

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Product

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Functionalized Ti₃C₂T_x MXene Nanosheets for Increased Emulsion Viscosity and Enhanced Heavy Oil Recovery

Functionalized Ti₃C₂T_x MXene Nanosheets for Increased Emulsion Viscosity and Enhanced Heavy Oil Recovery