Gas Hydrate Formation Probability Distributions: The Effect of Shear and Comparisons with Nucleation Theory

Abstract

Gas hydrate formation is a stochastic phenomenon of considerable significance for any risk-based approach to flow assurance in the oil and gas industry. In principle, well-established results from nucleation theory offer the prospect of predictive models for hydrate formation probability in industrial production systems. In practice, however, heuristics are relied on when estimating formation risk for a given flowline subcooling or when quantifying kinetic hydrate inhibitor (KHI) performance. Here, we present statistically significant measurements of formation probability distributions for natural gas hydrate systems under shear, which are quantitatively compared with theoretical predictions. Distributions with over 100 points were generated using low-mass, Peltier-cooled pressure cells, cycled in temperature between 40 and -5 °C at up to 2 K·min and analyzed with robust algorithms that automatically identify hydrate formation and initial growth rates from dynamic pressure data. The application of shear had a significant influence on the measured distributions: at 700 rpm mass-transfer limitations were minimal, as demonstrated by the kinetic growth rates observed. The formation probability distributions measured at this shear rate had mean subcoolings consistent with theoretical predictions and steel-hydrate-water contact angles of 14-26°. However, the experimental distributions were substantially wider than predicted, suggesting that phenomena acting on macroscopic length scales are responsible for much of the observed stochastic formation. Performance tests of a KHI provided new insights into how such chemicals can reduce the risk of hydrate blockage in flowlines. Our data demonstrate that the KHI not only reduces the probability of formation (by both shifting and sharpening the distribution) but also reduces hydrate growth rates by a factor of 2.

Experimental study of growth kinetics of CO2 hydrates and multiphase flow properties of slurries in high pressure flow systems

“…Different researchers have proposed different denitions of hydrate induction time based on different experimental systems. [30][31][32][33][34][35] In this work, induction time is described from the macroscopic aspect, including critical nuclei stage and visible crystal stage. In other words, the induction time is determined by a start point and an end point that is easy to observe and characterized by the large amount of hydrate formation.…”

“…Therefore, through sensitivity analysis, the system water content has the most signicant inuence on the induction period of CO 2 hydrate formation, liquid loading take the second place, mass ow rate next, and initial pressure on induction time the weakest. Moreover, establishing an induction time model for oil-water systems based on classic nucleation theories 34,35 will be the focus of next step work. According to the similarity criterion in uid mechanics, it is supposed that the ow and plugging phenomenon as well as mechanisms in the ow loop will have similarities with the industrial pipelines.…”

Hydrate nucleation and growth on water droplets acoustically-levitated in high-pressure natural gas

“…Recently, May and co-workers (16,17) reported robust measurements of gas hydrate formation probability distributions using an improved technique based on a highpressure stirred automated lag time apparatus (HPS-ALTA), and compared their results with other experimental studies and the predictions of classical nucleation theory (CNT). (11) They found that careful nucleation rate measurements reported in the literature were broadly consistent, particularly those determined in well-mixed systems where the agreement was within a factor of three.…”

Tecnologia de Hidrato de Gás: Modelagem Computacional para a Etapa de Crescimento do Hidrato

“…( 16), em que valores menores no denominador 𝑉 tendem a aumentar o resultado final de %𝐸𝑅. Ainda, o perfil de formação naturalmente estocástico dos hidratos de gás acaba sendo uma problemática recorrente, especialmente em trabalhos experimentais, onde a necessidade de grande número de amostras é mandatória, pois a distribuição dos dados de formação tende a ser mais ampla que a predita [24]. Tabela 4: Erro relativo percentual para os dados experimentais de Vlasov [14] Outra vantagem de se utilizar um método computacional consiste na possibilidade de avaliação dos dados experimentais, sem a necessidade de preparação de novas amostras.…”