This study focuses on a safety evaluation method for underground gas storage. Gas storage is usually constructed underground in complex environments, and the service life of such facilities is limited. To ensure the secure and long-term operation of gas storage facilities, safety evaluation has become the focus of management. The present paper provides an effective method for safety evaluation. An index system was established as the foundation of the analysis for this evaluation, and the matter-element extension method was applied to obtain a quantitative evaluation result. For the weight values of each index in the matter-element extension method, this paper presents a comprehensive weight computation method based on vague sets and entropy. By application of this method, the safety level of a gas storage facility in the Jintan salt mines (in Jiangsu, China) was calculated, and the evaluation result was 4.6433, which meant the safety level was V and the underground gas storage was slightly at risk. It indicated that the influence on the overall safety and tightness of this gas storage could be ignored in the operation process, but the frequency of regular monitoring should be increased. The defective indexes were also obtained, such as salt rock cohesion, the roof thickness, the volume contraction ratio, the interlayer content, the height of the casing shoe and the adjacent cavity pressure difference, which need to be monitored and modified. This paper evaluated the safety of the underground gas storage from a unique perspective. It is expected that the results of this research will contribute to the maintenance and operational decisions, and provide a reference for management in the energy industry.

The study of sulfur solubility is of great significance to the safe development of sulfur-containing gas reservoirs. However, due to measurement difficulties, experimental research data on sulfur solubility thus far are limited. Under the research background of small samples and poor information, a weighted least-squares support vector machine (WLSSVM)-based machine learning model suitable for a wide temperature and pressure range is proposed to improve the prediction accuracy of sulfur solubility in sour gas. First, we use the comprehensive gray relational analysis method to extract important factors affecting sulfur solubility as the model input parameters. Then, we use the whale optimization algorithm (WOA) and gray wolf optimizer (GWO) intelligence algorithms to find the optimal solution of the penalty factor and kernel coefficient and bring them into three common kernel functions. The optimal kernel function is calculated, and the final WOA-WLSSVM and GWO-WLSSVM models are established. Finally, four evaluation indicators and an outlier diagnostic method are introduced to test the proposed model’s performance. The empirical results show that the WOA-WLSSVM model has better performance and reliability; the average absolute relative deviation is as low as 3.45%, determination coefficient (R²) is as high as 0.9987, and the prediction accuracy is much higher than that of other models.

A coal-rock dynamic disaster is a rapid instability and failure process with dynamic effects and huge disastrous consequences that occurs in coal-rock mass under mining disturbance. Disasters lead to catastrophic consequences, such as mine collapse, equipment damage, and casualties. Early detection can prevent the occurrence of disasters. However, due to the low accuracy of anomaly detection, disasters still occur frequently. In order to improve the accuracy of anomaly detection for coal-rock dynamic disasters, this paper proposes an anomaly detection method based on a dynamic threshold and a deep self-encoded Gaussian mixture model. First, pre-mining data were used as the initial threshold, and the subsequent continuously arriving flow data were used to dynamically update the threshold to solve the impact of artificially setting the threshold on the detection accuracy. Second, feature dimensionality reduction and reorganization of the data were carried out, and low-dimensional feature representation and feature reconstruction error modeling were used to solve the difficulty of extracting features from high-dimensional data in real time. Finally, through the end-to-end optimization calculation of the energy probability distribution between different categories for anomaly detection, the problem that key abnormal information may be lost due to dimensionality reduction was solved and accurate detection of monitoring data was realized. Experimental results showed that this method has better performance than other methods.

Some natural gases are toxic because they contain hydrogen sulfide (H₂S). The solubility pattern of elemental sulfur (S) in toxic natural gas needs to be studied for environmental protection and life safety. Some methods (e.g., experiments) may pose safety risks. Measuring sulfur solubility using a machine learning (ML) method is fast and accurate. Considering the limited experimental data on sulfur solubility, this study used consensus nested cross-validation (cnCV) to obtain more information. The global search capability and learning efficiency of random forest (RF) and weighted least squares support vector machine (WLSSVM) models were enhanced via a whale optimization–genetic algorithm (WOA-GA). Hence, the WOA-GA-RF and WOA-GA-WLSSVM models were developed to accurately predict the solubility of sulfur and reveal its variation pattern. WOA-GA-RF outperformed six other similar models (e.g., RF model) and six other published studies (e.g., the model designed by Roberts et al.). Using the generic positional oligomer importance matrix (gPOIM), this study visualized the contribution of variables affecting sulfur solubility. The results show that temperature, pressure, and H₂S content all have positive effects on sulfur solubility. Sulfur solubility significantly increases when the H₂S content exceeds 10%, and other conditions (temperature, pressure) remain the same.

Under big data, a large number of features, as well as their complex data types, make traditional feature extraction and knowledge reasoning unable to adapt to new conditions. To solve these problems, this study proposes a museum big data feature extraction method based on a similarity mapping algorithm. Under the museum big data analysis, the museum big data text information is collected through web crawler technology. The web crawler is used to index the content of websites all across the Internet so that the museum websites can appear in search engine results and the collected text information is denoised and smoothed by a Gaussian filter to construct the processed text information set mapping matrix. The semantic similarity is computed according to the text word concept. Based on the calculation results, through word frequency and document probability inverse document frequency weight, the museum big data text information features are extracted. Simulation results show that the proposed method has high accuracy and short extraction time. Through the comparative analysis, it can be realized that this method not only solves the problems existing in traditional methods but also lays a foundation for the analysis of museum massive data.