scholarly journals On liquid phase hydrates formation in pipelines in the course of gas non-stationary flow

2021 ◽  
Vol 230 ◽  
pp. 01006
Author(s):  
Teimuraz Davitashvili
Keyword(s):  
Natural Gas ◽  
Hydrate Formation ◽  
Stationary Flow ◽  
Problem Solution ◽  
Adiabatic Flow ◽  
Urgent Task ◽  
Gas Hydrate Formation ◽  
Alternative Means ◽  
Hydrodynamic Method ◽  
Formation Of Hydrates

Nowadays, when the emphasis is on alternative means of energy, natural gas is still used as an efficient and convenient fuel both in the home (for heating buildings and water, cooking, drying and lighting) and in industry together with electricity. In industrial terms, gas is one of the main sources of electricity generation in both developed and developing countries. Pipelines are the most popular means of transporting natural gas domestically and internationally. The main reasons for the constipation of gas pipelines are the formation of hydrates, freezing of water plugs, pollution, etc. It is an urgent task to take timely measures against the formation of hydrates in the pipeline. To stop gas hydrate formation in gas transporting pipelines, from existing methods the mathematical modelling with hydrodynamic method is more acceptable. In this paper the problem of prediction of possible points of hydrates origin in the main pipelines taking into consideration gas non-stationary flow and heat exchange with medium is studied. For solving the problem the system of partial differential equations governing gas non-stationary flow in main gas pipeline is investigated. The problem solution for gas adiabatic flow is presented.

Application of Machine Learning in Gas-Hydrate Formation and Trendline Prediction

2021 ◽  
Author(s):  
Celestine Udim Monday ◽  
Toyin Olabisi Odutola
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Gas ◽  
Gas Hydrate ◽  
Gas Flow ◽  
Flow Line ◽  
Learning Algorithms ◽  
Hydrate Formation ◽  
Machine Learning Algorithms ◽  
Gas Hydrate Formation

Abstract Natural Gas production and transportation are at risk of Gas hydrate plugging especially when in offshore environments where temperature is low and pressure is high. These plugs can eventually block the pipeline, increase back pressure, stop production and ultimately rupture gas pipelines. This study seeks to develops machine learning models after a kinetic inhibitor to predict the gas hydrate formation and pressure changes within the natural gas flow line. Green hydrate inhibitor A, B and C were obtained as plant extracts and applied in low dosages (0.01 wt.% to 0.1 wt.%) on a 12meter skid-mounted hydrate closed flow loop. From the data generated, the optimal dosages of inhibitor A, B and C were observed to be 0.02 wt.%, 0.06 wt.% and 0.1 wt.% respectively. The data associated with these optimal dosages were fed to a set of supervised machine learning algorithms (Extreme gradient boost, Gradient boost regressor and Linear regressor) and a deep learning algorithm (Artificial Neural Network). The output results from the set of supervised learning algorithms and Deep Learning algorithms were compared in terms of their accuracies in predicting the hydrate formation and the pressure within the natural gas flow line. All models had accuracies greater than 90%. This result show that the application Machine learning to solving flow assurance problems is viable. The results show that it is viable to apply machine learning algorithms to solve flow assurance problems, analyzing data and getting reports which can improve accuracy and speed of on-site decision making process.

Coupled Heat and Mass Transfer CFD Model for Methane Hydrate

2015 ◽  
Author(s):  
Eugenio Turco Neto ◽  
M. A. Rahman ◽  
Syed Imtiaz ◽  
Thiago dos Santos Pereira ◽  
Fernanda Soares de Sousa
Keyword(s):  
Natural Gas ◽  
Multiphase Flow ◽  
Gas Hydrate ◽  
Gas Flow ◽  
Three Dimensional ◽  
Hydrate Formation ◽  
Cfd Model ◽  
Flow Metering ◽  
Ansys Cfx ◽  
Gas Hydrate Formation

The gas hydrates problem has been growing in offshore deep water condition where due to low temperature and high pressure hydrate formation becomes more favorable. Several studies have been done to predict the influence of gas hydrate formation in natural gas flow pipeline. However, the effects of multiphase hydrodynamic properties on hydrate formation are missing in these studies. The use of CFD to simulate gas hydrate formation can overcome this gap. In this study a computational fluid dynamics (CFD) model has been developed for mass, heat and momentum transfer for better understanding natural gas hydrate formation and its migration into the pipelines using ANSYS CFX-14. The problem considered in this study is a three-dimensional multiphase-flow model based on Simon Lo (2003) study, which considered the oil-dominant flow in a pipeline with hydrate formation around water droplets dispersed into the oil phase. The results obtained in this study will be useful in designing a multiphase flow metering and a pump to overcome the pressure drop caused by hydrate formation in multiphase petroleum production.

Progress in use of surfactant in nearly static conditions in natural gas hydrate formation

2020 ◽  
Vol 14 (3) ◽  
pp. 463-481
Author(s):  
Zhen Pan ◽  
Yi Wu ◽  
Liyan Shang ◽  
Li Zhou ◽  
Zhien Zhang
Keyword(s):  
Natural Gas ◽  
Gas Hydrate ◽  
Hydrate Formation ◽  
Natural Gas Hydrate ◽  
Gas Hydrate Formation ◽  
Static Conditions

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

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 519
Author(s):  
Jie Cao ◽  
Shijie Zhu ◽  
Chao Li ◽  
Bing Han
Keyword(s):  
Genetic Algorithm ◽  
Natural Gas ◽  
Gas Hydrate ◽  
Hydrate Formation ◽  
Support Vector ◽  
Gas Hydrate Formation ◽  
Svm Model ◽  
Data Points ◽  
Formation Conditions ◽  
Relative Molecular Weight

To predict the natural gas hydrate formation conditions quickly and accurately, a novel hybrid genetic algorithm–support vector machine (GA-SVM) model was developed. The input variables of the model are the relative molecular weight of the natural gas (M) and the hydrate formation pressure (P). The output variable is the hydrate formation temperature (T). Among 10 gas samples, 457 of 688 data points were used for training to identify the optimal support vector machine (SVM) model structure. The remaining 231 data points were used to evaluate the generalisation capability of the best trained SVM model. Comparisons with nine other models and analysis of the outlier detection revealed that the GA-SVM model had the smallest average absolute relative deviation (0.04%). Additionally, the proposed GA-SVM model had the smallest amount of outlier data and the best stability in predicting the gas hydrate formation conditions in the gas relative molecular weight range of 15.64–28.97 g/mol and the natural gas pressure range of 367.65–33,948.90 kPa. The present study provides a new approach for accurately predicting the gas hydrate formation conditions.

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