Flow Assurance-Hydrates Basics

Introduction

Gas hydrates are defined as crystalline water-based structures resembling ice in which smaller molecules are trapped in cages formed by the bonded water molecules. They are favoured in environments of low-temperature high-pressure such as deepwater fields. Hydrates could lead to complete blockage of pipelines, which might lead to sudden shutdowns. In order to avoid their formation, thermodynamics of the system should be well-defined to identify regions where hydrate formation is possible. In the event hydrates were already formed, thermodynamic inhibitors (methanol - monoethylene glycol), anti-agglomerants, or kinetic inhibitors should be used to shift the curve of hydrate formation to a higher pressure for the same temperature enabling production consistency at lower temperatures.

Current Research

Some recent laboratory experiments (Lingelem et. al. 1994) have showed cases where hydrates, when formed, did not cause flow blockage. Instead, they were carried by the flow without any flow assurance issues. This was explained as crude oil includes naturally occurring components that interact with hydrates and could render their surfaces. Those components might adsorb to hydrates surface preventing agglomeration or might be embedded in them.?

(Guo et. al. 2016) assumed a two-phase annular flow to be the main pattern in deepwater gas wells and stated their work using models developed by 1) OLGA, to map the operational limits of production systems and 2) PVTsim, to simulate physical fluid properties along with OLGA models. They coupled optimum pressure and temperature models with hydrate equilibrium model, which is established using chemical potential theory, to predict the region of hydrate formation along the wellbore. They eventually stated that their introduced model achieved relative error of 0.057% and outperformed other empirical chart methods of (Polo Maleev, Stergaard, and Hammerschmid).

ML Research

(Gjelsvik et. al. 2023) reviewed the recent advancements of machine learning related to gas hydrates. They declared that most of work was related to predicting the thermodynamic conditions of hydrates forming and chemical analysis of crude oil. Hence, they referred to the widely used methodology of Fourier Transform Ion Cyclotron Mass Spectrometry (FT-ICR MS), in which the mass-to-charge (m/z) ratio of ions are determined based on the cyclotron frequency of the ions in a fixed magnetic field, could achieve high accuracy for crude oil and hydrates chemical analysis. Different ML algorithms have been coupled with FT-ICR MS to analyse the mass spectra such as SVM, PCA, Genetic algorithms. In addition, they discussed the different ML methodologies used to predict thermodynamic conditions and suitable inhibitors for gas hydrate systems such as (Artificial Neural Networks,??Support Vector Machine, Decision Trees, Random Forest, K-nearest neighbours, Na?ve Bayes, and Convolutional Neural Networks). They also mentioned that there was only one research (Gjelsvik et. al. 2022) was related to ML application that considered natural hydrates inhibitors that exist in crude oil.

References

1. Lingelem, M. N., Majeed, A. I., & Stange, E. (1994). Industrial experience in evaluation of hydrate formation, inhibition, and dissociation in pipeline design and operation.?Annals of the New York Academy of Sciences,?715(1), 75-93.
2. Guo, Y., Sun, B., Zhao, K., & Zhang, H. (2016). A prediction method of natural gas hydrate formation in deepwater gas well and its application.?Petroleum,?2(3), 296-300.
3. Gjelsvik, E. L., Fossen, M., Brunsvik, A., & T?ndel, K. (2022). Using machine learning-based variable selection to identify hydrate related components from FT-ICR MS spectra.?PloS one,?17(8), e0273084.
4. Gjelsvik, E. L., Fossen, M., & T?ndel, K. (2023). Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates.?Fuel,?334, 126696.