Injection of flue gas or CO-N mixtures into gas hydrate reservoirs has been considered as a promising option for geological storage of CO. However, the thermodynamic process in which the CO present in flue gas or a CO-N mixture is captured as hydrate has not been well understood. In this work, a series of experiments were conducted to investigate the dependence of CO capture efficiency on reservoir conditions. The CO capture efficiency was investigated at different injection pressures from 2.6 to 23.8 MPa and hydrate reservoir temperatures from 273.2 to 283.2 K in the presence of two different saturations of methane hydrate. The results showed that more than 60% of the CO in the flue gas was captured and stored as CO hydrate or CO-mixed hydrates, while methane-rich gas was produced. The efficiency of CO capture depends on the reservoir conditions including temperature, pressure, and hydrate saturation. For a certain reservoir temperature, there is an optimum reservoir pressure at which the maximum amount of CO can be captured from the injected flue gas or CO-N mixtures. This finding suggests that it is essential to control the injection pressure to enhance CO capture efficiency by flue gas or CO-N mixtures injection.

Capturing CO2 from power plant flue gas through hydrate formation is starting to be applied on an industrial scale. Several methods have been developed, and a large number of experiments have been conducted in order to investigate ways of increasing their efficiency. However, most of them suffer from a lack of detailed kinetic studies. In this Letter, we present a highly accurate method to investigate the kinetics of flue gas hydrate formation. Preliminary results are detailed at three different temperatures. It has been found that more than 40% of CO2 capture in the form of hydrates occurs after reaching the final pressure. Therefore, statistically constant pressure cannot be used as a sign of thermodynamic equilibrium. The results obtained from this study are important for optimizing CO2 separation operations thus maximizing efficiency and reducing economic barriers. In addition, they are also useful in studying the kinetics of hydrate formation in other gas mixture systems.

Effective production of oil from carbonate reservoirs often requires the application of improved oil recovery technologies such as waterflooding. However, conventional waterflooding in carbonates usually results in low hydrocarbon recovery as most of these formations exhibit a complex pore throats structure and are mostly oil-wet. Therefore, improved insight into the causes of hydrophobic wetting behavior of such reservoirs is important for understanding the fluid distribution, displacement and enhancing recovery processes. The characterization of fluid-rock interactions is, however, challenging with existing laboratory methods, which are typically based on macroscale (mm) observations. In this experimental study, an advanced imaging technique, namely environmental scanning electron microscope, was applied for the comprehensive investigation of microscale (µm) wettability variations in carbonate rocks covered with organic layers. For the first time, the presence of organic layers on the sample was proved using energy dispersive X-ray mapping. Furthermore, the chemical bond of this layer and carbonate rock surfaces was determined using the transmission electron microscopy and electron energy-loss spectroscopy. The thickness of layer was estimated by using image processing software. These findings show that the application of combined microscopic techniques reveals important details about the reason of hydrophobic wetting properties of real carbonate rocks.

Methane is a powerful greenhouse gas, and the abrupt degassing events that recently have formed large craters on the Russian Arctic Yamal and Gydan Peninsulas have caused major concern. Here we present field data on cover sediments and evolution of a gas-emission crater discovered in the Erkuta–Yakha River valley in the southern Yamal Peninsula in June 2017. The crater is located south of other similar craters discovered over the past decade in northern West Siberia. Data were collected during a field trip to the Erkuta crater in December 2017 which included field observations and sampling of permafrost soil and ground ice from the rim of the crater. All soil and ice samples were measured for contents of methane and its homologs (ethane and propane) and carbon dioxide. The contents of carbon dioxide in some samples are notably higher than methane. The strongly negative δ13С of methane from ground ice samples (−72‰) is typical of biogenic hydrocarbons. The ratio of methane to the total amount of its homologs indicate a component of gases that have migrated from a deeper, thermogenic source. Based on obtained results, a potential formation model for Erkuta gas-emission crater is proposed, which considers the combined effect of deep-seated (deep gas migration) and shallow (oxbow lake evolution and closed talik freezing) causes. This model includes several stages from geological prerequisites to the lake formation.

The geomechanical stability of the permafrost formations containing gas hydrates in the Arctic is extremely vulnerable to global warming and the drilling of wells for oil and gas exploration purposes. In this work the effect of gas hydrate and ice on the geomechanical properties of sediments was compared by triaxial compression tests for typical sediment conditions: unfrozen hydrate‐free sediments at 0.3 °C, hydrate‐free sediments frozen at −10 °C, unfrozen sediments containing about 22 vol% methane hydrate at 0.3 °C, and hydrate‐bearing sediments frozen at −10 °C. The effect of hydrate saturation on the geomechanical properties of simulated permafrost sediments was also investigated at predefined temperatures and confining pressures. Results show that ice and gas hydrates distinctively influence the shearing characteristics and deformation behavior. The presence of around 22 vol% methane hydrate in the unfrozen sediments led to a shear strength as strong as those of the frozen hydrate‐free specimens with 85 vol% of ice in the pores. The frozen hydrate‐free sediments experienced brittle‐like failure, while the hydrate‐bearing sediments showed large dilatation without rapid failure. Hydrate formation in the sediments resulted in a measurable reduction in the internal friction, while freezing did not. In contrast to ice, gas hydrate plays a dominant role in reinforcement of the simulated permafrost sediments. Finally, a new physical model was developed, based on formation of hydrate networks or frame structures to interpret the observed strengthening in the shear strength and the ductile deformation.

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

No abstract

No abstract