Hydrate-based CO₂ sequestration is a novel approach that can not only realize permanent CO₂ sequestration but can also form an artificial cap to prevent its upward migration. In this work, a self-developed large-scale 3D apparatus was employed to investigate hydrate formation characteristics in hydrate-based CO₂ sequestration at a constant liquid CO₂ injection rate through a vertical well for the first time. Temperature and pressure evolutions in the sediment were analyzed in detail. Key indicators, including cumulative sequestered CO₂, CO₂ in hydrate and liquid phases, the instantaneous hydrate conversion, and liquid CO₂ retention rates, were calculated. The results show that hydrate continuously forms with increased CO₂ injection and exhibits strong heterogeneity due to the variation in hydrate formation rate and quantity. Severe liquid CO₂ heterogeneous figuring phenomena occur since hydrate deteriorates the effective pore structure and topology, resulting in relatively small cumulative sequestered CO₂ when a large amount of CO₂ is released from the outlet. Meanwhile, the instantaneous hydrate conversion and liquid CO₂ retention rates have large fluctuations owing to water consumption and variation in the effective contact area between liquid CO₂ and water. However, hydrate formation does not cause blockage of wellbore and formation nearby under given experimental conditions, which is beneficial for hydrate formation in deeper sediment. This study provides insights into hydrate formation and liquid CO₂ immigration regularity during hydrate-based CO₂ sequestration and demonstrates its feasibility at a field scale.

This paper demonstrates the applicability of machine learning algorithms in sand production problems with natural gas hydrate (NGH)-bearing sands, which have been regarded as a grave concern for commercialization. The sanding problem hinders the commercial exploration of NGH reservoirs. The common sand production prediction methods need assumptions for complicated mathematical derivations. The main contribution of this paper was to introduce machine learning into the prediction sand production by using data from laboratory experiments. Four main machine learning algorithms were selected, namely, K-Nearest Neighbor, Support Vector Regression, Boosting Tree, and Multi-Layer Perceptron. Training datasets for machine learning were collected from a sand production experiment. The experiment considered both the geological parameters and the sand control effect. The machine learning algorithms were mainly evaluated according to their mean absolute error and coefficient of determination. The evaluation results showed that the most accurate results under the given conditions were from the Boosting Tree algorithm, while the K-Nearest Neighbor had the worst prediction performance. Considering an ensemble prediction model, the Support Vector Regression and Multi-Layer Perceptron could also be applied for the prediction of sand production. The tuning process revealed that the Gaussian kernel was the proper kernel function for improving the prediction performance of SVR. In addition, the best parameters for both the Boosting Tree and Multi-Layer Perceptron were recommended for the accurate prediction of sand production. This paper also involved one case study to compare the prediction results of the machine learning models and classic numerical simulation, which showed the capability of machine learning of accurately predicting sand production, especially under stable pressure conditions.

As one of the most important future clean energy sources, natural gas hydrate (NGH) is attracting widespread attention due to the vast reserves available and high energy density. How to extract this source in a safe, efficient, and environmentally friendly manner has become the key to the commercial utilization of its resources. This paper reviews the recent advances in the study of the fundamental reservoir properties of offshore NGH, summarizing the methods and technologies for testing the sedimentary properties of reservoirs, analyzing the characteristics in reservoir mechanics, electrics, thermodynamics, and fluid dynamics, and discusses the influence of reservoir fundamental properties on NGH exploitation. The aim is to provide guidance and reference for research on the exploitation of NGH in different target exploitation areas offshore.

The aim of this study was to determine the influence of four inorganic salts, KCl, NaCl, KBr and NaBr, on the thermodynamic conditions of methane hydrate formation. In order to achieve this, the vapor–liquid water-hydrate (VL_WH) equilibrium conditions of methane (CH₄) hydrate were measured in the temperature range of 274.15 K–282.15 K by the isothermal pressure search method. The results demonstrated that, in comparison with deionized water, the four inorganic salts exhibited a significant thermodynamic inhibition on CH₄ hydrate. Furthermore, the inhibitory effect of Na⁺ on methane hydrate is more pronounced than that of K⁺, where there is no discernible difference between Cl⁻ and Br⁻. The dissociation enthalpy ($∆H_{diss}$) of CH₄ hydrate in the four inorganic salt solutions is comparable to that of deionized water, indicating that the inorganic salt does not participate in the formation of hydrate crystals. The Chen–Guo hydrate model and N–NRTL–NRF activity model were employed to forecast the equilibrium conditions of CH₄ hydrate in electrolyte solution. The absolute relative deviation (AARD) between the predicted and experimental values were 1.24%, 1.08%, 1.18% and 1.21%, respectively. The model demonstrated satisfactory universality and accuracy. This study presents a novel approach to elucidating the mechanism and model prediction of inorganic salt inhibition of hydrate.

Natural gas hydrate (NGH) is a clean resource characterized by abundant potential reserves, clean combustion, and high energy density. Although significant progress has been made in the development of NGH resources all around the world, challenges still exist that hinder commercial exploitation, such as a low daily gas production rate and short steady production periods. One significant reason lies in the complex gas–liquid–solid phase transitions occurring within the formation during production, which lead to changes in flow capacity. Understanding the phase change mechanism of NGH reservoirs will help to further reveal the production increase mechanism. To address the phase transitions’ effect on production, this paper establishes a numerical simulation model for the depressurization exploitation of natural gas hydrates in order to investigate phase transition characteristics at the field scale. First, the phase equilibrium calculation method is presented and the phase equilibrium curve is modified by considering the capillary effect, soluble salt, and surface adsorption. Then, the phase transition model is successfully characterized in a simulation and the numerical simulation model is established based on the first test project parameters in the Shenhu area. The production characteristics of different sediment types (montmorillonite, South China Sea sediments, kaolin, and silt) are analyzed under the effects of water content and salinity. It is shown that lower initial water content and higher salinity result in higher gas production. The results provide a better understanding of the effects of phase transition parameters on NGH production at the field scale.