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

Abstract

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.

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

“…GBR gives the best prediction for predicting the methane hydrate phase boundary conditions than MLP, k-NN, SVR, and RF algorithms . A study by Suresh also confirmed that GPR has good hydrate phase behavior prediction accuracy than ANN and LSSVM. RF and ET have similar prediction accuracy, but XGBoost outperforms both RF and ET in hydrate phase behavior prediction .…”

“…This indicates that the LSSVM model efficiently predicts natural gas hydrate formation conditions without sour constituents and the presence of inhibitors. Contrarily, Suresh showed that GPR provides better predictions than LSSVM and ANN models for pure hydrates of CH 4 and CO 2 systems. The performance of the LSSVM model in predicting natural gas hydrate formation conditions decreases 3–15 times in the presence of inhibitors, CO 2 , and H 2 S based on AARD evaluation. , For the same inhibitors and natural gas systems, the use of the extra trees model works better with about 50% error reduction compared with the LSSVM model .…”

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