Wangquan Ye

Fracture networks are the primary fluid migration conduits in geological reservoirs. Their spatial connectivity, geometric morphology, and dynamic evolution patterns directly influence the concentration of resources such as natural gas hydrates. Traditional core analysis techniques are unable to accurately characterize the three-dimensional topological structure of fractures, leading to significant deviations in the predictions of fundamental physical reservoir parameters. This study addresses the challenge of identifying fractures and reconstructing their connections in X-ray computed tomography (CT) images of natural gas hydrate reservoir cores. Based on interactive processing modules and morphological operation mechanisms, the top-hat transformation method is introduced to extract microfractures in the core, thereby avoiding local detail masking by global thresholds. A multi-constraint dynamic fusion method for characterizing fracture connections is proposed. Principal component analysis (PCA) is used to extract fracture direction features, which are then combined with geometric collinearity verification and spatial proximity screening to effectively identify connections in damaged fracture networks. Based on this fracture network optimization, numerical simulation was used to obtain the absolute permeability parameters of the rock core. This method can effectively address the difficulty caused by CT image noise and insufficient resolution in identifying microfractures, making the extracted core fracture network’s geometric topology characteristics more closely resemble the actual reservoir. This provides microscopic data support for natural gas hydrate reservoir flow and stability assessments. The research findings are important for promoting the exploration and development of natural gas hydrates.

In this work, we proposed a simple way to enhance underwater LIBS signals with a solid substrate-assisted method, promoting the development of compact marine in situ LIBS sensors with low power consumption, while ensuring high detection sensitivity.

The pore structure of marine natural gas-hydrate-bearing sediments is a key factor related to the physical properties of reservoirs. However, the resolution of micro-computed tomography (micro-CT) images is unsuitable for the analysis of pore structures in fine-grained sediments. In this regard, super-resolution (SR) reconstruction technology is expected to improve the spatial resolution of micro-CT images. We present a self-supervised learning method that does not require high-resolution datasets as input images to complete the training and reconstruction processes. This method is an end-to-end network consisting of two subnetworks: an SR network and a downscaling network. We trained on a self-built dataset of hydrate samples from three different particle sizes. Compared with typical methods, the SR results indicate that our method provides high resolution while improving clarity. In addition, it has the highest consistency with the liquid saturation method with the subsequent calculation of porosity parameters. This study contributes to the investigation of seepage and energy transfer in sediments containing natural gas hydrates, which is particularly important for the exploration and development of marine natural gas hydrate resources.

The pore structure of marine sediments varies with the distribution of gas-hydrate, hence affecting the gas-water permeability. CT image is a conventional approach to view the internal structure, while for hydrate-bearing sediment investigation, rather poor resolution of obtained image has limited the accuracy of the analysis. Recently, super-resolution (SR) reconstruction techniques have been used to enhance the spatial resolution of CT images with varying degrees of improvement. Typical Image Pairs-Based SR (PSR) methods require higher resolution matching images for training, which is challenging for hydrate samples in dynamic temperature and pressure conditions. Here, we introduced a self-supervised learning (SLSR) method that only relies on a single input image to complete the process of training and reconstruction. We conducted a complete training to establish an end-to-end network consisting of two sub-networks, an SR network and a downscaling network. Self-built datasets from three hydrate samples with different sediment grains were trained and tested. Compared with the typical method, the SR results show that our method provides higher resolution while improving clarity. Moreover, in the subsequent calculation of porosity parameters, it has the highest consistency with the liquid saturation method. This study contributes to investigating the water seepage and energy transfer in the gas hydrate bearing sediments, which is particularly important for the exploration and development of marine natural gas hydrate resources. The image super-resolution method established by us has also a broad application prospect in the field of CT imaging.

As a powerful in situ detection technique, Raman spectroscopy is becoming a popular underwater investigation method, especially in deep-sea research. In this paper, an easy-to-operate underwater Raman system with a compact design and competitive sensitivity is introduced. All the components, including the optical module and the electronic module, were packaged in an L362 × Φ172 mm titanium capsule with a weight of 20 kg in the air (about 12 kg in water). By optimising the laser coupling mode and focusing lens parameters, a competitive sensitivity was achieved with the detection limit of SO₄²⁻ being 0.7 mmol/L. The first sea trial was carried out with the aid of a 3000 m grade remotely operated vehicle (ROV) “FCV3000” in October 2018. Over 20,000 spectra were captured from the targets interested, including methane hydrate, clamshell in the area of cold seep, and bacterial mats around a hydrothermal vent, with a maximum depth of 1038 m. A Raman peak at 2592 cm⁻¹ was found in the methane hydrate spectra, which revealed the presence of hydrogen sulfide in the seeping gas. In addition, we also found sulfur in the bacterial mats, confirming the involvement of micro-organisms in the sulfur cycle in the hydrothermal field. It is expected that the system can be developed as a universal deep-sea survey and detection equipment in the near future.

In recent years, the investigation and exploitation of hydrothermal region and polymetallic mineral areas has become a hot topic. The emergence of underwater vehicle platforms has made it possible for new chemical sensors to be applied in marine in-situ detection. Laser-induced breakdown spectroscopy (LIBS), with its advantages of rapid real-time analysis, sampling without pretreatment, simultaneous multi-element detection and stand-off detection, has great potential in marine applications. In this paper, a newly more compact and lighter underwater LIBS system based on the LIBSea system named LIBSea II was developed and tested both in the laboratory and sea trials. The system consists of a Nd:YAG single-pulse laser at 1064 nm, a fiber spectrometer, optical layout, a power supply module and an internal environment sensor. The system is encapsulated in a pressure vessel (Φ 190 mm × L 588 mm) with an optical window on the end cap. Experimental parameters of the system including laser energy and delay time were firstly optimized in the laboratory. Then, field test of the system in nearshore was performed with various samples, including pure metal and alloy samples as well as a manganese nodule sample from deep sea, to verify the detection performance of the LIBSea II system. In 2019, the system was deployed on a remotely operated vehicle (ROV) of Haima for deep sea trial, and atomic lines of K, Na, Ca and strong molecular bands of CaOH from a carbonate rock sample were obtained for the first time at depths of 1400 m. These results show that the LIBSea II system has great potential to be used in deep-sea geological exploration.