Predict Hydrate Formation Conditions Research Articles

An investigation on hydrate prediction and inhibition: An industrial case study

AbstractThis investigation reports the first study to predict natural gas hydrate formation using both Aspen HYSYS® and HydraFlash software for various gas compositions and thermodynamic inhibitors (monoethylene glycol [MEG] concentrations at 10, 20, 30, and 40 wt.% and methanol concentrations at 10 and 20 wt.%). The simulated predictions are compared with the results of the experimental data in the literature. It has been shown that HydraFlash software can accurately predict hydrate formation conditions for a given industrial case, without having to carry out costly experimental work. This work also evaluated the effect of inhibitors and it appears that inhibitor type and concentration are determined according to condition of gas composition. MEG is consequently selected as the most ideal hydrate inhibitor for the industrial case. This also was confirmed through COSMO‐RS studies in which the sigma profile and sigma potential of the considered inhibitors were obtained and presented using density functional (DFT) calculations to verify the hydrogen bonding affinities of the inhibitors to water molecules. HydraFlash was utilized to predict the dissociation conditions of hydrates under the influence of a high concentration of MEG inhibition, reaching up to 40 wt.% at 313 K and a pressure of 311.1 bar. Finally, it is shown that both software packages are quite accurate and useful tools for the prediction of hydrate for simple systems. However, HydraFlash can simulate more complex systems, including different types of salts at higher pressures. Investigation results indicate insightful guidance for accurately predicting hydrate dissociation under simulated conditions.

Modeling on gas hydrate phase equilibrium at high concentration of alcohols

As the major interest in flow assurance of oil and natural gas production, the concentrations of injected alcohols are commonly high exceeding mass fraction of 30 %. In this work, the non-ideality of aqueous phase is represented by the activity coefficient model, in which the water and inhibitor molecules are regarded as associating compounds, and the differences in size and shape between them are properly considered. Coupled with the Chen-Guo hydrate model, the proposed model has been extended to represent the hydrate phase equilibrium of pure gas and gas mixtures in aqueous alcohol solutions with concentrations up to 50 wt%. Moreover, the hydrate lattice parameter was reoptimized to extend the Chen-Guo hydrate model to systems below 273.15 K, when high-concentration of alcohols is present. The predictions of the proposed model have been compared with the experimental data, with the average relative deviations in pressure and temperature of 3.991 % and 0.108 %, respectively, verifying the reliability of this model. Compared with other thermodynamic models or correlations, the proposed model in this work is equivalent to or outperformed those in predicting hydrate formation conditions, especially at high alcohol concentrations.

Modelling Hydrate Deposition in Gas-Dominant Subsea Pipelines in Operating and Shutdown Scenarios

This study addresses a significant research gap related to hydrate formation in subsea gas pipelines, with a specific focus on deposition rates during shutdown scenarios, which has received limited attention in previous studies. Past research has employed various methodologies, including experimental, analytical, and computational fluid dynamics (CFD) approaches, to predict hydrate formation conditions, but none have tackled the prediction of hydrate deposition during shutdowns. In this study, we employ a multiple linear regression modeling approach using the MATLAB regression learner app. Four distinct regression models were developed using data generated from 81 CFD simulations, utilising a 10 m length by 0.0204 m diameter 3D horizontal pipe model in Ansys Fluent, as previously developed Through cross-validation against experimental data, the standard linear regression model emerged as the most reliable choice for predicting hydrate deposition rates, providing predictions within ±10% uncertainty bounds of experimental results up to pressures of 8.8 MPa at hydrate-forming temperatures. The uniqueness of this new model lies in its ability to estimate the risk of hydrate deposition in subsea gas pipelines, especially with low gas flow rates and during shutdown periods, which are critical for maintenance planning. Furthermore, by estimating depositional volumes, the model predicts hydrate slurry volumes at receiving facilities, contributing to energy sustainability and benefiting gas transport pipeline operators, particularly in aging gas fields with declining production.

Experimental and modeling on hydrate phase equilibrium conditions for hydrogen-containing gas mixtures in pure water and brines

Hydrate-based technology is considered as one of potential ways for hydrogen separation and storage. Additionally, it has been reported that injecting CO2/H2 mixture is an effective approach to the enhancement of natural gas hydrate exploitation. Hence the determination of hydrate formation conditions of hydrogen containing gas mixtures is of fundamental significance. A series of hydrate formation conditions of hydrogen-containing gas mixtures were then measured under 273.8 to 285.5 K and 2.61 to 10.52 MPa, including 3 binary (CH4 + H2) gas mixtures and 5 ternary (CH4 + CO2 + H2) gas mixtures in pure water, as well as 8 ternary (CH4 + CO2 + H2) gas mixtures in aqueous solutions of NaCl and Na2SO4. Meanwhile, a thermodynamic model for predicting hydrate formation conditions of hydrogen-containing systems were developed, in which Chen–Guo hydrate model was used to calculate the fugacities of guest species in hydrate phase and Patel–Teja equation of state was used to calculate the fugacities of guest components in gas phase and those of water in aqueous solutions. Two mixing rules, van der Waals mixing rule and Wang-Sandler mixing rule, were applied in parallel to determine the values of parameters of Patel–Teja equation of state. The overall average absolute deviation on formation pressure for 273 data points is 4.61 % when using Wang-Sandler mixing rule while it is 5.70 % when using van der Waals mixing rule. The experimental data and prediction method presented might be of significance for both exploitation of natural gas hydrate via injecting CO2/H2 mixture as well as the purification of hydrogen via forming hydrate.

Experimental and simulation studies on the phase behaviour for gas hydrates in a CO2 rich gas dominant multiphase pipeline system

AbstractIn this work, an experimental and modelling study is performed to identify the phase behaviour of gas hydrates formation for a high CO2 content natural gas system and multiphase gas dominant system. Further, CSMGem, PVTSim, and Aspen HYSYS software packages are used to verify the prediction capabilities for gas hydrates formation conditions. Initially, thermodynamic equilibrium conditions are obtained using CSMGem, PVTSim, and Aspen HYSYS. Then, these results are validated and compared to the experimental work to verify the accuracy in predicting hydrate formation conditions. An experimental study is performed in the pressure range of 2–7 MPa for a pure system and multiphase system with the gas composition of carbon dioxide (70%) + methane (26%) + ethane (2%) + propane (1%) + butane (1%). The results show that the PVTSim software package shows the best accuracy with 99.9% for the pure system and 99.88% for the multiphase system.

Prediction of Gas Hydrate Formation in Industries

Natural gas and crude oil in natural underground reservoirs are in contact with water. Stability of these compounds at the presence of both components and complete dependence on the host molecules by forming holes in their guest molecules are replaced. There are many gases such as methane, ethane, propane, carbon dioxide, hydrogen sulphides that can play the role of guest molecules. The natural gas hydrate formation in different sectors of the oil and gas industry in downstream processes causes the production to stop or decrease. Therefore, the need to know the causes and conditions of hydrate formation is strongly felt. In this study, Van der Waals and Platteeuw model was used to predict hydrate formation conditions. The prediction of hydrate formation conditions needs equilibrium fugacity of gaseous components, and for the equilibrium molar component of water.

A Generalized Correlation for Predicting Ethane, Propane, and Isobutane Hydrates Equilibrium Data in Pure Water and Aqueous Salt Solutions.

Hydrate formation can cause serious problems in hydrocarbon exploration, production, and transportation, especially in deepwater environments. Hydrate‐related problems affects the integrity of the deepwater platforms, leads to equipment blockages, and also increases operational costs. In order to solve these problems, salts are used as thermodynamic inhibitors and also mixed with the drilling fluids in most drilling processes. A comprehensive understanding of hydrate formation in aqueous salt solutions is vital to overcome these problems. Statistical thermodynamic models are commonly used to predict hydrate formation conditions in different aqueous solutions. However, these models involve rigorous computations and are restricted to certain conditions. They give inaccurate predictions of hydrate equilibrium conditions for high‐temperature, high‐pressure, and high‐salinity systems. Therefore, it is paramount to develop a simple‐to‐use and reliable prediction tool. In this work, an empirical correlation is developed and successfully used to predict the equilibrium conditions of ethane, propane, and isobutane hydrates in pure water and aqueous solutions of sodium chloride, potassium chloride, calcium chloride, and magnesium chloride. Experimental data on hydrate formation conditions for these components are regressed and a generalized correlation is obtained. The predictions in this work show excellent agreement with all the experimental data in the literature.

Assessing thermodynamic models and introducing novel method for prediction of methane hydrate formation

Transmission of natural gas with methane as the main constituent has been a subject of interest to industrial companies. Predicting hydrate formation conditions is important to prevent formation of methane hydrate in gas pipeline. Also, attention has been taken to account for capture and storage of pure methane. In this paper, a comprehensive comparison is performed between empirical correlations and different equation of state in van der Waals Platteeuw (VdW-P) thermodynamic model to determine the most accurate method of hydrate formation condition of methane. In addition, a novel, simple and accurate correlation is developed to predict methane hydrate formation temperature using genetic programming. Error analysis on a wide range of experimental data indicates that the new proposed correlation is superior over existing correlations and all VdW-P models with R2 = 0.999.

Application of adaptive neuro-fuzzy inference system in prediction of hydrate formation temperature

ABSTRACTA look at the number of publications in the last decades on the prediction of hydrate forming conditions for various gas mixtures obviously indicates the importance of this field from scientific and industrial viewpoints. Yet, the correlations presented in the literature are not accurate enough and also some of these correlations are presented mainly in graphical form, thus making it difficult to use them within general computer packages for simulation and design. In this study adaptive neuro-fuzzy inference systems were used to produce a nonlinear model to predict the hydrate formation temperature. The model was trained using 303 input–output patterns collected from reliable sources. The adaptive neuro-fuzzy inference system model enables the user to accurately predict hydrate formation conditions under varying system conditions (i.e., temperature, pressure, and gas composition), without having to do costly experimental measurements. Also, statistical error analysis is used to evaluate the performance and the accuracy of the adaptive neuro-fuzzy inference system for estimating natural gas hydrate formation to guide designers and operators in selecting the best system conditions for their particular applications. It is shown that the results of predictions are in acceptable agreement with experimental data indicating the capability of the adaptive neuro-fuzzy inference system for predicting hydrate formation conditions of natural gases.

Developing a simple and accurate correlation for initial estimation of hydrate formation temperature of sweet natural gases using an eclectic approach

Formation of natural-gas hydrates which are crystalline solids composed of water and gas has been a problem to the natural-gas industry in production, processing facilities and transportation and caused substantial economic risks. Therefore, in order to overcome hydrate-related issues, it is important to predict the temperature and pressure under which gas hydrates form. Since the start of hydrates studies, several correlations have been proposed to predict their formation temperature (HFT) under circumstances of different known data such as indeterminate gas composition. However; most of these models have failed to give a good prediction of HFT over a wide range of pressure and gas gravity variation; furthermore, the presented models often require complicated and long computations. The purpose of this study is to present an accurate and simple correlation with only one set of coefficients, achieved using a novel fitting method which can apply for the large range of temperatures (273–299 K), pressures (350–30,000 kPa) and molecular weights (16–29 g/mol). The new proposed correlation shows to be the most accurate one among all the available correlations. The coefficient of determination (R2) of this new formula is 0.9874 and the maximum absolute error is 1.3% for 545 experimental data. The results of the correlation are compared with seven widely used correlations along with a neural network model for predicting hydrate formation conditions using back propagation.

The Equilibrium Data and Thermodynamic Modeling of Hydrate Formation for CO2 and Mixtures of CO2 and CH4 in the Presence of Methanol

Equilibrium data for the novel formation of hydrates of carbon dioxide and mixtures of carbon dioxide and methane in 20 wt% aqueous methanol solution were measured by the constant-volume method. For CO2, these data were taken at the temperature and pressure ranges of 264.7–270.7 K and 1,470–3,160 kPa, respectively. For mixtures of carbon dioxide and methane, these data were taken at the temperature and pressure ranges of 262.9–273.7 K and 1,370–5,100 kPa, respectively. The data obtained for CO2 in 20 wt% aqueous methanol solution were in disagreement with previously published data, but there was good agreement between our data and the predictions of thermodynamic models. The Peng-Robinson equation of state (PR EOS) coupled with the Wong-Sandler (WS) mixing rule was used to obtain the fugacities of the components in the gas and aqueous liquid phases. The PR EOS was then coupled with van der Waals-Platteeuw (vdW-P) hydrate model and applied to predict hydrate-formation conditions in the system containing methanol. The model predictions demonstrated good agreement with the experimental data.

Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions

A novel high-pressure apparatus with various abilities in hydrate investigation fields has been designed, constructed and fully described in the present paper. In order to achieve an appropriate understanding of the gas hydrate behavior in formation and destabilization, series of laboratory experiments with six different gas mixtures were done and more than 130 hydrate equilibrium points in the pressure range of about 450–3000psia were recorded. Different methods of hydrate formation prediction were discussed and finally the new promising neural networks method was used. Because of the previous works defects in accurate hydrate formation prediction via neural networks, a new use of neural networks was introduced. Testing and validation of the new neural networks method indicates that it is a reliable technique for the accurate prediction of hydrate formation conditions for generalized gas systems and can be used in future automatic inhibitor dosing devices.

The Development of a New Empirical Correlation for Predicting Hydrate Formation Conditions

Hydrates are known to occur in a variety of natural-gas handling facilities and processing equipment in oil fields, refineries, and chemical plants when natural gas and water coexist at elevated pressure and reduced temperature. Prevention of hydrate formation costs large amounts of capital and results in large operating expenses. In order to avoid costly losses due to the formation of these hydrates, several methods, which include thermodynamic modeling and empirical correlations, can be employed to predict the conditions for hydrate formation. The authors employed SAS (SAS Institute, Cary, North Carolina) to develop a new correlation for predicting the hydrate-formation temperatures for both pure and mixture of hydrocarbon systems using the gravity method. The method correlates the hydrate-formation pressure against specific gravity, pressure, and water-vapor pressure.

Prediction of gas hydrate formation condition in the presence of thermodynamic inhibitors with the Elliott–Suresh–Donohue Equation of State

Elliott–Suresh–Donohue (ESD) equation of state (EOS) which is based on thermodynamic perturbation theory and uses the Wertheim association contribution to account for association interactions, is applied to predict hydrate formation conditions in the presence and absence of thermodynamic inhibitors. The ESD EOS is coupled with the van der Waals–Platteuw model. Methane, ethane, propane, nitrogen, carbon dioxide, n-butane and iso-butane are considered as gas components and methanol, ethanol, and mono-ethylene glycol (MEG) are thermodynamic inhibitors considered to be present in the aqueous phase. Different cases of single gas and gas mixtures in pure water and in a solution of water and an inhibitor are considered. To achieve more accurate results, binary interaction coefficients are adjusted as a linear function of temperature. Generally, with regard to the results obtained in various cases, it is shown that the application of the ESD EOS in predicting hydrate formation conditions leads to accurate and acceptable results.

Representation of Hydrate Phase Equilibria in Aqueous Solutions of Methanol and Electrolytes Using an Equation of State

In this work, the method for the calculation of hydrate phase equilibria proposed by Zuo and Stenby (Zuo, Y.-X.; Stenby, E. H. SEP 9816. J. Soc. Petr. Eng., submitted) has been extended to represent incipient equilibrium hydrate formation conditions in aqueous solutions containing methanol and electrolytes. To predict hydrate formation conditions, the parameters of the extended Furst-Renon electrolyte equation of state (Furst, W.; Renon, H. AIChE J. 1993, 39, 335−343) have been reevaluated. Interaction parameters between water and methanol have been determined to match experimental hydrate formation data of methane in the water−methanol solutions. The interaction parameters between methanol and ions are associated with cationic Stokes and anionic Pauling diameters. Furthermore, cationic Stokes diameters in methanol are assumed to be a function of temperature. Coefficients of the temperature dependence have been determined by two methods. In method I, the cationic Stokes diameters in methanol are equal to ...

A new correlation for predicting hydrate formation conditions for various gas mixtures and inhibitors

In the last 50 years, several studies have been performed on the measurement and prediction of hydrate forming conditions for various gas mixtures and inhibitors. Yet, the correlations presented in the literature are not accurate enough and consider most of the time, simple pure gases only and their mixtures. In addition, some of these correlations are presented mainly in graphical form, thus making it difficult to use them within general computer packages for simulation and design. The purpose of this paper is to present a comprehensive neural network model for predicting hydrate formation conditions for various pure gases, gas mixtures, and different inhibitors. The model was trained using 2387 input–output patterns collected from different reliable sources. The predictions are compared to existing correlations and also to real experimental data. The neural network model enables the user to accurately predict hydrate formation conditions for a given gas mixture, without having to do costly experimental measurements. The relative importance of the temperature and the different components in the mixture has also been investigated. Finally, the use of the new model in an integrated control dosing system for preventing hydrate formation is discussed.