Research progress and scientific challenges in the depressurization exploitation mechanism of clayey-silt natural gas hydrates in the northern South China Sea

Natural gas hydrate reservoirs in the northern South China Sea primarily comprise clayey silt, making exploitation more challenging relative to sandy reservoirs in other countries and regions. This paper provides an overview of the latest research developments in the exploitation mechanism covering the past five years, focusing on hydrate phase transition, multiphase flow in the decomposition zone, the seepage regulation of reservoir stimulation zone, and production capacity simulation, all of which are relevant to the previously conducted two rounds of hydrate trial production in offshore areas of China. The results indicate that the phase transition of clayey-silt hydrate remains in a dynamic equilibrium, with the decomposition efficiency mainly controlled by the coupling of heat and flow and high heat consumption during decomposition. The decomposition zone exhibits strong hydrophilicity, easy adsorption, and sudden permeability changes. A temperature drop is present that is concentrated near the wellbore, and once a water lock has formed, the gas-phase flow capacity significantly decreases, leading to potential secondary hydrate formation. To enhance permeability and increase production, it is imperative to implement reservoir and temperature field reconstruction based on initial formation alterations, which will further optimize and improve the transport capacity of the reservoir.Document Type: Current minireviewCited as: Lu, C., Qin, X., Sun, J., Wang, R., Cai, J. Research progress and scientific challenges in the depressurization exploitation mechanism of clayey-silt natural gas hydrates in the northern South China Sea. Advances in Geo-Energy Research, 2023, 10(1): 14-20. https://doi.org/10.46690/ager.2023.10.0

AU-PD: An Arbitrary-size and Uniform Downsampling Framework for Point Clouds

Point cloud downsampling is a crucial pre-processing operation to downsample the points in the point cloud in order to reduce computational cost, and communication load, to name a few. Recent research on point cloud downsampling has achieved great success which concentrates on learning to sample in a task-aware way. However, existing learnable samplers can not perform arbitrary-size sampling directly. Moreover, their sampled results always comprise many overlapping points. In this paper, we introduce the AU-PD, a novel task-aware sampling framework that directly downsamples point cloud to any smaller size based on a sample-to-refine strategy. Given a specified arbitrary size, we first perform task-agnostic pre-sampling to sample the input point cloud. Then, we refine the pre-sampled set to make it task-aware, driven by downstream task losses. The refinement is realized by adding each pre-sampled point with a small offset predicted by point-wise multi-layer perceptrons (MLPs). In this way, the sampled set remains almost unchanged from the original in distribution, and therefore contains fewer overlapping cases. With the attention mechanism and proper training scheme, the framework learns to adaptively refine the pre-sampled set of different sizes. We evaluate sampled results for classification and registration tasks, respectively. The proposed AU-PD gets competitive downstream performance with the state-of-the-art method while being more flexible and containing fewer overlapping points in the sampled set. The source code will be publicly available at https://zhiyongsu.github.io/Project/AUPD.html

Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation

Existing interactive point cloud segmentation approaches primarily focus on the object segmentation, which aim to determine which points belong to the object of interest guided by user interactions. This paper concentrates on an unexplored yet meaningful task, i.e., interactive point cloud semantic segmentation, which assigns high-quality semantic labels to all points in a scene with user corrective clicks. Concretely, we presents the first interactive framework for point cloud semantic segmentation, named InterPCSeg, which seamlessly integrates with off-the-shelf semantic segmentation networks without offline re-training, enabling it to run in an on-the-fly manner. To achieve online refinement, we treat user interactions as sparse training examples during the test-time. To address the instability caused by the sparse supervision, we design a stabilization energy to regulate the test-time training process. For objective and reproducible evaluation, we develop an interaction simulation scheme tailored for the interactive point cloud semantic segmentation task. We evaluate our framework on the S3DIS and ScanNet datasets with off-the-shelf segmentation networks, incorporating interactions from both the proposed interaction simulator and real users. Quantitative and qualitative experimental results demonstrate the efficacy of our framework in refining the semantic segmentation results with user interactions. The source code will be publicly available

Preparation and investigation of self-healing gel for mitigating circulation loss

Lost circulation is a common and complex downhole accident in the process of oil and gas drilling. Traditional bridge plugging material has the limitation of poor adaptability to lost formations. Therefore, this study synthesized a new self-healing plugging material to improve the plugging success rate; specifically, the hydrophobic association polymer lauryl methlacrylate-acrylamide-acrylic acid containing Fe3+ was modified via curdlan to form a composite gel with high strength and self-healing properties. The self-healing time, mechanicalness and rheological properties of the self-healing gel were systematically evaluated. The results showed that the modification of curdlan could significantly improve the mechanical properties and rheological strength of self-healing gel, and the chelating structure formed by Fe3+ and carboxyl groups could further enhance the mechanical properties of the self-healing gel. Toughness and storage modulus of the LF0.15C2 selfhealing gel with the introduction of curdlan and Fe3+ could reach 30.2 kJ/m3 and 3,458 Pa, respectively. Compared with conventional gel materials, composite gels with self-healing properties exhibited better pressure-bearing capacity of 2.5 MPa, and could effectively avoid causing plugging at the entrance of the fractures by high-concentration inert material and improve the pressure-bearing capacity. In addition, the plugging mechanism of the self-healing gel modified via curdlan in formation fractures was analysed in detail. The self-healing gel modified via curdlan prepared in this work has application potential as a lost circulation material in the field of oil and gas drilling.Cited as: Wang, R., Wang, C., Long, Y., Sun, J., Liu, L., Wang, J. Preparation and investigation of self-healing gel for mitigating circulation loss. Advances in Geo-Energy Research, 2023, 8(2): 112-125. https://doi.org/10.46690/ager.2023.05.0

FAHP and TOPSIS Prediction of Diamond Segments Wear When Using Frame Saw to Cut Granites

Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approaches were employed to predict the sawability of a diamond frame saw to cut granites. FAHP is used to determine the weights of the criteria of decision-makers and TOPSIS is used to rank sawability. The sawability was evaluated by diamond segment wear. The prediction of segment wear is important to determine the segments service life and sawing cost and may determine cutting parameter selection for a given stone. Sawing experiments were conducted to verify the analysis result of the applied method in this study. The experimental results are in good agreement with the theoretical analysis. The ranking method can be used to evaluate segment wear. Stone properties, such as uniaxial compressive strength, shore hardness, quartz content, and bending strength, must be determined for the best segment wear ranking

Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics

This paper studies the problem of modeling interacting dynamical systems, which is critical for understanding physical dynamics and biological processes. Recent research predominantly uses geometric graphs to represent these interactions, which are then captured by powerful graph neural networks (GNNs). However, predicting interacting dynamics in challenging scenarios such as out-of-distribution shift and complicated underlying rules remains unsolved. In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem. The core of GOAT is to incorporate factorized prototypes from contextual knowledge into a continuous graph ODE framework. Specifically, GOAT employs representation disentanglement and system parameters to extract both object-level and system-level contexts from historical trajectories, which allows us to explicitly model their independent influence and thus enhances the generalization capability under system changes. Then, we integrate these disentangled latent representations into a graph ODE model, which determines a combination of various interacting prototypes for enhanced model expressivity. The entire model is optimized using an end-to-end variational inference framework to maximize the likelihood. Extensive experiments in both in-distribution and out-of-distribution settings validate the superiority of GOAT