Search

As a new energy source, gas hydrates have attracted worldwide attention, but their exploration and development face enormous challenges. Thus, it has become increasingly crucial to identify hydrate distribution accurately. Electrical resistivity tomography (ERT) can be used to detect the distribution of hydrate deposits. An ERT inversion network (ERTInvNet) based on a deep neural network (DNN) is proposed, with strong learning and memory capabilities to solve the ERT nonlinear inversion problem. 160,000 samples about hydrate distribution are generated by numerical simulation, of which 10% are used for testing. The impact of different deep learning parameters (such as loss function, activation function, and optimizer) on the performance of ERT inversion is investigated to obtain a more accurate hydrate distribution. When the Logcosh loss function is enabled in ERTInvNet, the average correlation coefficient (CC) and relative error (RE) of all samples in the test sets are 0.9511 and 0.1098. The results generated by Logcosh are better than MSE, MAE, and Huber. ERTInvNet with Selu activation function can better learn the nonlinear relationship between voltage and resistivity. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, the best choices for Relu, Selu, Leaky_Relu, and Softplus. Compared with Adadelta, Adagrad, and Aadmax, Adam has the best performance in ERTInvNet with the optimizer. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, respectively. By optimizing the critical parameters of deep learning, the accuracy of ERT in identifying hydrate distribution is improved. [ABSTRACT FROM AUTHOR]

As a future clean energy resource, the exploration and exploitation of natural gas hydrate are favorable for solving the energy crisis and improving environmental pollution. Detecting the spatial distribution of natural gas hydrate in the reservoir is of great importance in natural gas hydrate exploration and exploitation. Fracture-filling hydrate, one of the most common types of gas hydrate, usually appears as a massive or layered accumulation below the seafloor. This paper aims to detect the spatial distribution variation of fracture-filling hydrate in sediments using the electrical property in the laboratory. Massive hydrate and layered hydrate are formed in the electrical resistivity tomography device with a cylindrical array. Based on the electrical resistivity tomography data during the hydrate formation process, the three-dimensional resistivity images of the massive hydrate and layered hydrate are established by using finite element forward, Gauss–Newton inversion, and inverse distance weighted interpolation. Massive hydrate is easier to identify than layered hydrate because of the big difference between the massive hydrate area and surrounding sediments. The diffusion of salt ions in sediments makes the boundary of massive hydrate and layered hydrate change with hydrate formation. The average resistivity values of massive hydrate (50 Ω ⋅ m ) and layered hydrate (1.4 Ω ⋅ m ) differ by an order of magnitude due to the difference in the morphology of the fracture. Compared with the theoretical resistivity, it is found that the resistivity change of layered hydrate is in accordance with the change tendency of the theoretical value. The formation characteristic of massive hydrate is mainly affected by the pore water distribution and pore microstructure of hydrate. The hydrate formation does not necessarily cause the increase in resistivity, but the increase of resistivity must be due to the formation of hydrate. The decrease of resistivity in fine-grains is not obvious due to the cation adsorption of clay particles. These results provide a feasible approach to characterizing the resistivity and growth characteristics of fracture-filling hydrate reservoirs and provide support for the in-situ visual detection of fracture-filling hydrate. [ABSTRACT FROM AUTHOR]

CH4/CO2 replacement is of great significance for the exploitation of natural gas hydrate resources and CO2 storage. The feasibility of this method relies on our understanding of the CH4/CO2 replacement efficiency and mechanism. In this study, CH4/CO2 replacement experiments were carried out to study the distribution characteristics of CH4 and CO2 in hydrate-bearing sediments during and after replacement. Similar to previously reported data, our experiments also implied that the CH4/CO2 replacement process could be divided into two stages: fast reaction and slow reaction, representing CH4/CO2 replacement in the hydrate-gas interface and bidirectional CH4/CO2 diffusion caused replacement, respectively. After replacement, the CO2 content gradually decreased, and the methane content gradually increased with the increase of sediment depth. Higher replacement percentage can be achieved with higher replacement temperature and lower initial saturation of methane hydrate. Based on the calculation of CO2 consumption amounts, it was found that the replacement mainly took place in the fast reaction stage while the formation of CO2 hydrate by gaseous CO2 and water almost runs through the whole experimental process. Thus, the pore scale CH4/CO2 replacement process in sediments can be summarized in the following steps: CO2 injection, CO2 diffusing into sedimentary layer, occurrence of CH4/CO2 replacement and CO2 hydrate formation, wrapping of methane hydrate by mixed CH4-CO2 hydrate, continuous CO2 hydrate formation, and almost stagnant CH4/CO2 replacement. [ABSTRACT FROM AUTHOR]