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

Using machine learning-based variable selection to identify hydrate related components from FT-ICR MS spectra

(2022) Elise Lunde Gjelsvik et al.   PLoS One

Prediction of methane hydrate formation conditions in salt water using machine learning algorithms

(2021) Hongfei Xu et al.   COMPUTERS & CHEMICAL ENGINEERING

Different strategies for the use of random forest in NMR spectra

(2020) Betina P.O. Lovatti et al.   JOURNAL OF CHEMOMETRICS

Research on a Safety Assessment Method for Leakage in a Heavy Oil Gathering Pipeline

(2020) Peng Zhang et al.   Energies

A review on the role and impact of various additives as promoters/ inhibitors for gas hydrate formation

(2020) Qazi Nasir et al.   Journal of Natural Gas Science and Engineering

Integrating Support Vector Regression with Genetic Algorithm for Hydrate Formation Condition Prediction

(2020) Jie Cao et al.   Processes

Lacustrine versus Marine Oils: Fast and Accurate Molecular Discrimination via Electrospray Fourier Transform Ion Cyclotron Resonance Mass Spectrometry and Multivariate Statistics

(2020) Jose Javier Melendez-Perez et al.   ENERGY & FUELS

Saturation Modeling of Gas Hydrate Using Machine Learning with X-Ray CT Images

(2020) Sungil Kim et al.   Energies

Enhanced analysis of weathered crude oils by gas chromatography-flame ionization detection, gas chromatography-mass spectrometry diagnostic ratios, and multivariate statistics

(2020) Candice C. Chua et al.   JOURNAL OF CHROMATOGRAPHY A

Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach

(2020) Jihao Shi et al.   ENERGY

Thermodynamic features-driven machine learning-based predictions of clathrate hydrate equilibria in the presence of electrolytes

(2020) Palash V. Acharya et al.   FLUID PHASE EQUILIBRIA

Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints

(2019) Hao Li et al.   NEURAL COMPUTING & APPLICATIONS

Predicting the conditions for gas hydrate formation

(2019) Mohammad Ali Ghayyem et al.   PETROLEUM SCIENCE AND TECHNOLOGY

A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data

(2019) Guang-Hui Fu et al.   International Journal of Analytical Chemistry

Prediction of clathrate hydrate phase equilibria using gradient boosted regression trees and deep neural networks

(2019) Yongchen Song et al.   JOURNAL OF CHEMICAL THERMODYNAMICS

Investigating the effectiveness of anti-agglomerants in gas hydrates and ice formation

(2019) Jeong-Hoon Sa et al.   FUEL

Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network

(2019) Jiali Lv et al.   MOLECULES

Toward a Robust, Universal Predictor of Gas Hydrate Equilibria by Means of a Deep Learning Regression

(2019) Michael K. B. Landgrebe et al.   ACS Omega

Structure, mechanism, and performance evaluation of natural gas hydrate kinetic inhibitors

(2018) Sheida Shahnazar et al.   REVIEWS IN INORGANIC CHEMISTRY

Multivariate data analysis applied in the evaluation of crude oil blends

(2018) Cristina M.S. Sad et al.   FUEL

Rheology of natural gas hydrate slurry: Effect of hydrate agglomeration and deposition

(2018) Lin Ding et al.   FUEL

Fractionation of Asphaltenes in Understanding Their Role in Petroleum Emulsion Stability and Fouling

(2017) Peiqi Qiao et al.   ENERGY & FUELS

APPI(+)-FTICR mass spectrometry coupled to partial least squares with genetic algorithm variable selection for prediction of API gravity and CCR of crude oil and vacuum residues

(2017) Diana Catalina Palacio Lozano et al.   FUEL

Modeling of gas hydrate phase equilibria: Extremely randomized trees and LSSVM approaches

(2017) Hamidreza Yarveicy et al.   JOURNAL OF MOLECULAR LIQUIDS

Prediction of hydrate equilibrium conditions using k-nearest neighbor algorithm to CO2 capture

(2017) Javad Sayyad Amin et al.   PETROLEUM SCIENCE AND TECHNOLOGY

Modeling of stability conditions of natural gas clathrate hydrates using least squares support vector machine approach

(2016) Mohammad M. Ghiasi et al.   JOURNAL OF MOLECULAR LIQUIDS

Asphaltene Subfractions Responsible for Stabilizing Water-in-Crude Oil Emulsions. Part 2: Molecular Representations and Molecular Dynamics Simulations

(2015) Fan Yang et al.   ENERGY & FUELS

Laser desorption ionization FT-ICR mass spectrometry and CARSPLS for predicting basic nitrogen and aromatics contents in crude oils

(2015) Luciana A. Terra et al.   FUEL

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils

(2014) Luciana A. Terra et al.   ANALYST

Asphaltene Adsorption, a Literature Review

(2014) Jeramie J. Adams   ENERGY & FUELS

Developments in FT-ICR MS instrumentation, ionization techniques, and data interpretation methods for petroleomics

(2014) Yunju Cho et al.   MASS SPECTROMETRY REVIEWS

Evolving a robust approach for accurate prediction of methane hydrate formation temperature in the presence of salt inhibitor

(2014) Saeed Rashid et al.   Journal of Natural Gas Science and Engineering

Predictive Petroleomics: Measurement of the Total Acid Number by Electrospray Fourier Transform Mass Spectrometry and Chemometric Analysis

(2013) Boniek G. Vaz et al.   ENERGY & FUELS

Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions

(2012) Mohammad Ghavipour et al.   CHEMICAL ENGINEERING RESEARCH & DESIGN

Water-in-Crude Oil Emulsion Stabilization: Review and Unanswered Questions

(2012) Peter K. Kilpatrick   ENERGY & FUELS

Classification of crude oil samples through statistical analysis of APPI FTICR mass spectra

(2012) Stefano Chiaberge et al.   FUEL PROCESSING TECHNOLOGY

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

(2011) Kristin Tøndel et al.   BMC Systems Biology

Correlation of FT-ICR Mass Spectra with the Chemical and Physical Properties of Associated Crude Oils

(2010) Manhoi Hur et al.   ENERGY & FUELS

Petroleum Crude Oil Characterization by IMS-MS and FTICR MS

(2009) Francisco A. Fernandez-Lima et al.   ANALYTICAL CHEMISTRY

Combination of Statistical Methods and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for More Comprehensive, Molecular-Level Interpretations of Petroleum Samples

(2009) Manhoi Hur et al.   ANALYTICAL CHEMISTRY

Molecular analysis of petroleum derived compounds that adsorb onto gas hydrate surfaces

(2009) Anna E. Borgund et al.   APPLIED GEOCHEMISTRY

Influence of Petroleum Acids on Gas Hydrate Wettability

(2009) Kristin Erstad et al.   ENERGY & FUELS

Data Visualization for the Characterization of Naphthenic Acids within Petroleum Samples

(2009) Mark P. Barrow et al.   ENERGY & FUELS

Characterization of Athabasca Bitumen Heavy Vacuum Gas Oil Distillation Cuts by Negative/Positive Electrospray Ionization and Automated Liquid Injection Field Desorption Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

(2008) Donald F. Smith et al.   ENERGY & FUELS

Powered partial least squares discriminant analysis

(2008) Kristian Hovde Liland et al.   JOURNAL OF CHEMOMETRICS

Some aspects of hydrate formation and wetting

(2008) P. Fotland et al.   JOURNAL OF COLLOID AND INTERFACE SCIENCE

Petroleomics: Chemistry of the underworld

(2008) A. G. Marshall et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA