In this study, series of non-ionic surfactants from Span and Tween are evaluated for their ability to affect the viscosity profile of cyclopentane hydrate slurry. The surfactants; Span 20, Span 40, Span 80, Tween 20, Tween 40 and Tween 80 were selected and tested to provide different hydrophilic–hydrophobic balance values and allow evaluation their solubility impact on hydrate formation and growth time. The study was performed by using a HAAKE ViscotesterTM 500 at 2 °C and a surfactant concentration ranging from 0.1 wt%–1 wt%. The solubility characteristic of the non-ionic surfactants changed the hydrate slurry in different ways with surfactants type and varying concentration. The rheological measurement suggested that oil-soluble Span surfactants was generally inhibitive to hydrate formation by extending the hydrate induction time. However, an opposite effect was observed for the Tween surfactants. On the other hand, both Span and Tween demonstrated promoting effect to accelerate hydrate growth time of cyclopentane hydrate formation. The average hydrate crystallization growth time of the blank sample was reduced by 86% and 68% by Tween and Span surfactants at 1 wt%, respectively. The findings in this study are useful to understand the rheological behavior of surfactants in hydrate slurry.

This study evaluates the kinetic hydrate inhibition (KHI) performance of four quaternary ammonium hydroxides (QAH) on mixed CH₄ + CO₂ hydrate systems. The studied QAHs are; tetraethylammonium hydroxide (TEAOH), tetrabutylammonium hydroxide (TBAOH), tetramethylammonium hydroxide (TMAOH), and tetrapropylammonium hydroxide (TPrAOH). The test was performed in a high-pressure hydrate reactor at temperatures of 274.0 K and 277.0 K, and a concentration of 1 wt.% using the isochoric cooling method. The kinetics results suggest that all the QAHs potentially delayed mixed CH₄ + CO₂ hydrates formation due to their steric hindrance abilities. The presence of QAHs reduced hydrate formation risk than the conventional hydrate inhibitor, PVP, at higher subcooling conditions. The findings indicate that increasing QAHs alkyl chain lengths increase their kinetic hydrate inhibition efficacies due to better surface adsorption abilities. QAHs with longer chain lengths have lesser amounts of solute particles to prevent hydrate formation. The outcomes of this study contribute significantly to current efforts to control gas hydrate formation in offshore petroleum pipelines.

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.