scholarly journals Prediction of Gas Hydrate Formation at Blake Ridge using Machine Learning and Probabilistic Reservoir Simulation

Author(s):  
William K. Eymold ◽  
Jennifer M. Frederick ◽  
Michael Nole ◽  
Benjamin J. Phrampus ◽  
Warren T. Wood
2021 ◽  
Author(s):  
Celestine Udim Monday ◽  
Toyin Olabisi Odutola

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.


Author(s):  
Sachin Dev Suresh ◽  
Ali Qasim ◽  
Bhajan Lal ◽  
Syed Muhammad Imran ◽  
Khor Siak Foo

The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country’s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important. It affects the normal operation of the pipeline by plugging it. Under high pressure and low temperature conditions, gas hydrate is a crystalline structure consisting of a network of hydrogen bonds between host molecules of water and guest molecules of the incoming gases. Industry uses different types of chemical inhibitors in pipeline to suppress hydrate formation. To overcome this problem, machine learning algorithm has been introduced as part of risk management strategies. The objective of this paper is to utilize Machine Learning (ML) model which is Gaussian Process Regression (GPR). GPR is a new approach being applied to mitigate the growth of gas hydrate. The input parameters used are concentration and pressure of Carbon Dioxide (CO2) and Methane (CH4) gas hydrates whereas the output parameter is the Average Depression Temperature (ADT). The values for the parameter are taken from available data sets that enable GPR to predict the results accurately in terms of Coefficient of Determination, R2 and Mean Squared Error, MSE. The outcome from the research showed that GPR model provided with highest R2 value for training and testing data of 97.25% and 96.71%, respectively. MSE value for GPR was also found to be lowest for training and testing data of 0.019 and 0.023, respectively.


2020 ◽  
Author(s):  
William Eymold ◽  
Jennifer Frederick ◽  
Michael Nole ◽  
Benjamin Phrampus ◽  
Warren Wood

2014 ◽  
Vol 14 (1) ◽  
pp. 45
Author(s):  
Peyman Sabzi ◽  
Saheb Noroozi

Gas hydrates formation is considered as one the greatest obstacles in gas transportation systems. Problems related to gas hydrate formation is more severe when dealing with transportation at low temperatures of deep water. In order to avoid formation of Gas hydrates, different inhibitors are used. Methanol is one of the most common and economically efficient inhibitor. Adding methanol to the flow lines, changes the thermodynamic equilibrium situation of the system. In order to predict these changes in thermodynamic behavior of the system, a series of modelings are performed using Matlab software in this paper. The main approach in this modeling is on the basis of Van der Waals and Plateau's thermodynamic approach. The obtained results of a system containing water, Methane and Methanol showed that hydrate formation pressure increases due to the increase of inhibitor amount in constant temperature and this increase is more in higher temperatures. Furthermore, these results were in harmony with the available empirical data.Keywords: Gas hydrates, thermodynamic inhibitor, modelling, pipeline blockage


Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3615
Author(s):  
Florian Filarsky ◽  
Julian Wieser ◽  
Heyko Juergen Schultz

Gas hydrates show great potential with regard to various technical applications, such as gas conditioning, separation and storage. Hence, there has been an increased interest in applied gas hydrate research worldwide in recent years. This paper describes the development of an energetically promising, highly attractive rapid gas hydrate production process that enables the instantaneous conditioning and storage of gases in the form of solid hydrates, as an alternative to costly established processes, such as, for example, cryogenic demethanization. In the first step of the investigations, three different reactor concepts for rapid hydrate formation were evaluated. It could be shown that coupled spraying with stirring provided the fastest hydrate formation and highest gas uptakes in the hydrate phase. In the second step, extensive experimental series were executed, using various different gas compositions on the example of synthetic natural gas mixtures containing methane, ethane and propane. Methane is eliminated from the gas phase and stored in gas hydrates. The experiments were conducted under moderate conditions (8 bar(g), 9–14 °C), using tetrahydrofuran as a thermodynamic promoter in a stoichiometric concentration of 5.56 mole%. High storage capacities, formation rates and separation efficiencies were achieved at moderate operation conditions supported by rough economic considerations, successfully showing the feasibility of this innovative concept. An adapted McCabe-Thiele diagram was created to approximately determine the necessary theoretical separation stage numbers for high purity gas separation requirements.


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