Xin Huang

Distributed acoustic sensing (DAS) has been widely applied to injection–production profile monitoring in horizontal wells because it provides continuous full-wellbore coverage, real-time acquisition, and straightforward long-term deployment. In practical downhole operations, however, DAS measurements are frequently compromised by optical-signal attenuation, loss of fiber–casing/formation coupling, and environmental noise. Meanwhile, the mechanisms governing flow-induced acoustic responses remain insufficiently understood, which continues to impede quantitative diagnosis and interpretation of injection–production profiles based on DAS data. To address these challenges, this study performed controlled laboratory-scale physical simulation experiments of single-phase flow in a horizontal wellbore, systematically investigating DAS acoustic responses under two wellbore diameters (25 mm and 50 mm) and a range of flow velocities. Power spectral density (PSD) was derived using the fast Fourier transform to identify flow-sensitive characteristic frequency bands, and frequency-band energy (FBE) was further used to establish an optimal quantitative relationship with flow velocity. The results show that: (1) DAS energy is dominated by low-frequency components (V³) achieves high accuracy and robustness for practical applications. The proposed workflow—“PSD peak identification–characteristic band delineation–FBE regression”—establishes a methodological foundation for quantitative DAS-based monitoring of horizontal-well injection–production profiles in both laboratory and field settings, and it provides a basis for subsequent intelligent monitoring and interpretation under multiphase-flow conditions.

Accurate measurement of liquid flow velocity is crucial for pipeline management in the petroleum industry. Traditional flowmeters, such as ultrasonic, electromagnetic, and pressure-based devices, provide only point measurements and often require intrusive installation. Distributed Acoustic Sensing (DAS) offers a non-intrusive alternative by converting optical fibers into continuous acoustic sensors with meter-scale resolution. In this study, a High-Definition DAS system was applied in a laboratory flow loop to monitor single-phase liquid flow. The recorded signals were analyzed in both time and frequency domains. Results showed that the pump operating frequency dominated the spectral energy, accompanied by dispersive features. Power spectral density (PSD) increased linearly with flow rate, while Doppler-induced frequency shifts in the dominant component enabled velocity prediction. A regression model achieved a high coefficient of determination (R² = 0.9928), confirming the strong predictive capability. These findings highlight DAS as a reliable and scalable solution for non-intrusive liquid flow monitoring in pipelines.

Distributed acoustic sensing (DAS) has proven to be a revolutionary technology for the applications in the oil and gas industry, including leak detection, pattern recognition and pipeline flow rate measurement. However, existing DAS recognition methods prioritize accuracy through computationally intensive algorithms, overlooking stringent hardware constraints in field-deployable systems. To address this gap, we propose LAMNet—a lightweight neural network for efficient flow rate measurement. This novel approach combines the Convolutional Block Attention Module (CBAM) with Depthwise Separable Convolution (DSC) to enhance feature extraction capability from DAS data while preserving a lightweight model architecture. Ablation study validates the optimal architecture, and comparative experiments with classical networks confirm state-of-the-art performance.With a compact size of 1.568 MB, LAMNet achieves real-time processing at 5.471 ms per sample on a commercial GPU (NVIDIA GeForce RTX 4060), enabling efficient edge deployment. In pipeline flow rate measurement, the model achieves a relative prediction error of 0.110, a coefficient of determination of 0.864, and an average relative uncertainty of 0.442%. Furthermore, comparative experiments with multiple existing pipeline flow rate measurement methods demonstrate that our approach achieves the state-of-the-art overall performance. The LAMNet-enabled smart DAS for pipeline flow rate measurement represents a significant breakthrough, providing fast and accurate pipeline flow rate measurement. This advancement is expected to have a substantial impact on DAS applications. Code will be available at https://github.com/OpticalLab/LAMNet.

This study simulated seabed high pressure and low temperature conditions to synthesize natural gas hydrates, multi-stage depressurization mode mining hydrates as the blank group, and then carried out experimental research on the decomposition and mining efficiency of hydrates by depressurization and injection of different alcohols, inorganic salts, and different chemical agent concentrations. According to the experimental results, the chemical agent with the best decomposition efficiency is preferred; the results show that: the depressurization and injection of a certain mass concentration of chemical agents to exploit natural gas hydrate is more effective than pure depressurization to increase the instantaneous gas production rate. This is because depressurization combined with chemical injection can destroy the hydrate phase balance while effectively reducing the energy required for hydrate decomposition, thereby greatly improving the hydrate decomposition efficiency. Among them, depressurization and injection of 30% ethylene glycol has the best performance in alcohols; the decomposition efficiency is increased by 52.0%, and the mining efficiency is increased by 68.2% within 2 h. Depressurization and injection of 15% calcium chloride has the best performance in inorganic salts; the decomposition efficiency is increased by 46.3%, and the mining efficiency is increased by 61.1% within 2 h. In the actual mining process, the appropriate concentration of chemical agents should be used to avoid polluting the environment.

In this work, a molecular dynamics simulation was conducted to study the microscopic mechanism of how nitrogen bubbles affect the formation of THF hydrates at the molecular level. The results obtained reveal that the nitrogen bubble can promote the formation of THF hydrates. In the system with a nitrogen bubble, more THF-filled cages were generated, and the crystal structure was more orderly. The promotion of nitrogen bubbles on hydrate crystallization comes from the dissolution of nitrogen molecules. Some of dissolved nitrogen molecules can be enclosed in small hydrate cages near the nitrogen bubble, which can serve as stable sites for hydrate crystal growth, resulting in the fact that THF-filled cages connected with N₂-filled cages are much more stable and have a long lifetime. The results in this work can help to understand the promotion effect of micro- and nano-air bubbles on the crystallization of THF hydrates.