Artificial Neural Network

The analysis and collection of data is an integral part of all research fields of the modern world. There is a need to perform forward mathematical modeling to improve the operations and calculations with modern technologies. Artificial neural network signifies the structure of the human brain. They can provide reasonable solutions quickly for the problems that classical programming cannot solve. An in-depth systematic study is presented in this chapter related to artificial neural network applications (ANN) for predicting the equilibrium conditions for gas hydrate formation, which can assist in designing future dissociation technology for gas hydrate so that this white gold can make world energy free for the future generation. This chapter can also help to develop a novel inhibitor for gas hydrate formation and save millions of dollars for the oil and gas industry.

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

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.

Dynamics of shock waves in bubble zones of finite size with a hydrate-forming gas

The purpose of this study is to study the dynamics of the wave field, which is realized in a channel with a liquid containing a rectangular zone with bubbles of the freon-12 hydrate-forming gas during the propagation of a pressure shock wave. In the initial state, the considered gas-liquid system is under pressure P0. After a sudden increase in pressure to the value of Pe, a pressure wave of a stepped profile propagates in the system and, as a result of the presence of a bubble curtain, its amplitude increases, which in turn has a more favorable effect on the formation of hydrate in gas bubbles. In the initial state, the hydrate formation process was not taken into account. As a result, the dynamics of the pressure wave is shown during its propagation in a semi-infinite channel containing a gas curtain with a hydrate-forming gas. The mechanism of gas hydrate formation is described in this work on the basis of the theory of nonequilibrium phase transitions in vapor-liquid systems.

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

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.

Development and Implementation of Green Inhibitors of Gas Hydrate Formation in the Fields of Western Siberia

The effect of biodegradable polysaccharides – sodium (NaCMC) and ethanolammonium salts of carboxymethylcellulose, dextran and arabinogalactan on the process of gas hydrate formation was studied in order to search for new "green" inhibitors of low-concentration gas hydrate formation. The ability of polysaccharides to inhibit gas hydrate formation was studied in a quasi-equilibrium thermodynamic experiment. A mixture of hydrocarbon gases with a composition typical of the composition of petroleum gas and containing 78% methane was used as a gas-hydrate-forming model medium. It was found that in concentrations of 0.005, 0.0065 and 0.008%, dextran, NaCMC and arabinogalactan as thermodynamic inhibitors exceed methanol by 170-270 times in inhibitory properties. Dextran is superior to NaCMC and arabinogalactan in terms of inhibition efficiency, reduction of gas hydrate formation rate and induction time. Since with an increase in the concentration of polysaccharides, the pressure drop of gas hydrate formation increases and the rate of formation of gas hydrates decreases according to the mechanism of action, the studied polysaccharides can be attributed to both thermodynamic and kinetic inhibitors. It is established that the molecular weight of water-soluble polysaccharides has a significant effect on their inhibitory properties. A polysaccharide with a molecular weight of 250,000 demonstrated the highest inhibitory activity among the studied samples of NaCMC, which is 400 times more effective than methanol. NaCMC with a mass of 700 thousand did not have any effect on the formation of hydrates. Among the ethanolammonium salts, the monoethanolammonium salt CMC showed the greatest effectiveness in inhibiting the formation of tetrahydrofuran hydrates. An increase in its concentration from 0.02 to 0.1% leads to an increase in the induction time required for the nucleation and subsequent growth of crystals by 10 times. When switching from mono - to di - and triethanolammonium salts of carboxymethylcellulose, the inhibition efficiency decreases. It is shown that sodium and ethanolammonium salts of carboxymethylcellulose, arabinogalactan and dextran are promising for creating new "green" highly effective inhibitors of gas hydrate formation on their basis. The results of laboratory and field tests of the preparative form of the "green" gas hydrate formation inhibitor at the fields of Western Siberia are presented. It was found that at dosages of 500 g/m3 or less, there is no formation of hydrate plugs in the annulus of wells.