Comparison of kriging, machine learning algorithms and classical thermodynamics for correlating the formation conditions for CO2 gas hydrates and semi-clathrates

Ahmadi, Mohammad Ali; Chen, Zhangxin; Clarke, Matthew A.; Fedutenko, Eugene

doi:10.1016/j.jngse.2020.103659

Cited by 13 publications

(10 citation statements)

References 59 publications

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“…However, the model performance is relatively poor in N 2 + THF, CH 4 + 1,4-dioxane, and H 2 + acetone clathrate hydrates systems . The use of ANN and ANFIS still outperforms some hybrid machine learning algorithms such as TOKM and HFGA . GBR gives the best prediction for predicting the methane hydrate phase boundary conditions than MLP, k-NN, SVR, and RF algorithms .…”

Section: Machine Learning In Gas Hydrate Applicationsmentioning

confidence: 99%

“…ANN with the hyperbolic tangent sigmoid function predicts hydrate phase boundary conditions in the same range as ANFIS (Gaussian MF) . However, in the CO 2 gas hydrate system ANFIS performs better than ANN . Mehrizadeh also confirms the performance of ANFIS over ANN.…”

Section: Machine Learning In Gas Hydrate Applicationsmentioning

confidence: 99%

“…The hybrid group method of data handling (GMDH), ANFIS, and ANN also outperforms these same traditional correlations. ,,, Contrarily, van der Waals–Platteeuw (vdWP) models and HFGA are in the same range of performance for CO 2 hydrate systems with promoters such as acetone, TBAB, and TBAC. However, the van der Waals–Platteeuw (vdWP) EOS model outperforms ANN and Ordinary Kriging models by 112% and 215%, respectively, but the adaptive neuro-fuzzy interference system (ANFIS) performs better than the thermodynamics-based approach of van der Waals–Platteeuw (vdWP) by 26% . The machine learning performance with conventional hydrate models suggests that the incorporation of machine learning models into commercial simulation software will reduce the calculation stress while maintaining accuracy.…”

Section: Comparison Between Conventional and Machine Learning Modelsmentioning

confidence: 99%

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Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO 2 capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.

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Section: Machine Learning In Gas Hydrate Applicationsmentioning

confidence: 99%

Section: Machine Learning In Gas Hydrate Applicationsmentioning

confidence: 99%

Section: Comparison Between Conventional and Machine Learning Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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“…To predict hydrate formation for CO 2 with thermodynamic promoters, a range of ML approaches, such as ordinary kriging, adaptive neuro-fuzzy inference system (ANFIS), ANNs, and a hybrid of fuzzy logic and genetic algorithm were compared [25]. In addition, a separate study employed ANFIS and a hybrid of fuzzy logic and genetic algorithms to establish reliable inverse-based techniques for predicting CO 2 hydrate formation using 1,4-dioxane [26].…”

Section: Existing Models For Predicting Gas Hydratesmentioning

confidence: 99%

Predicting Gas Hydrate Equilibria in Multicomponent Systems with a Machine Learning Approach

2023

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Natural gas production and transportation can be threatened by gas hydrate plugging, especially in offshore environments with low temperatures and high pressures. This can cause pipeline blockages, increased back pressure, production stoppage, and pipeline ruptures. Machine learning (ML) models were created to predict gas hydrate formation in multicomponent systems with and without inhibitors. The models utilized input parameters such as gas-mixture specific gravity, pressure, and inhibitor concentrations, which were fed into five supervised ML algorithms. The accuracy of the models' predictions was compared, and three models had accuracy greater than 90%, while two had accuracies between 80 and 90%.

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“…To link these layers, activation functions besides training and optimization algorithms are utilized, including logistic-sigmoid and tan-sigmoid functions as transfer functions in addition to the Levenberg−Marquardt, Bayesian regularization, and scaled conjugate gradient as optimization approaches. Ahmadi 60 provided a detailed comparison between machine learning algorithms in predicting the gas hydrate formation conditions. Each layer has a specific number of neurons that could be thought as embedded processing units in the ANN model.…”

Section: Conventional Drilling Fluids Modelsmentioning

confidence: 99%

Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

et al. 2021

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Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from nonbeing field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature (T), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination (R 2 ) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination (R 2 ) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of −0.67, and an absolute average relative error of 3.18.

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Comparison of kriging, machine learning algorithms and classical thermodynamics for correlating the formation conditions for CO2 gas hydrates and semi-clathrates

Cited by 13 publications

References 59 publications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Predicting Gas Hydrate Equilibria in Multicomponent Systems with a Machine Learning Approach

Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

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