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 (R 2) is as high as 0.9987, and the prediction accuracy is much higher than that of other models.
Reliability analysis provides a basis for the anticorrosion design and maintenance strategy of pipelines. This paper introduces a calculation method for corroded pipeline life reliability by step-down-stress testing (SDST) and Weibull distribution. SDST is used to obtain the corrosion rate of N80 steel under the action of an H2S and CO2 environment at four different temperatures. The Arrhenius model is used to establish the conversion model of failure time and then obtain quasi-samples with parameters. The quasi-samples are used to estimate the parameters of the Weibull distribution, and finally, we can obtain the reliability function of the corroded pipeline. The life reliability curve shows that the pipeline life decreases with the increase of temperature, and when the operation temperature is 363 K, the average life is 10.09 years, which is far less than the designed life. The life reliability of the pipeline decreases with increasing time, when the service life of a pipe with 90% reliability is approximately 7.4 years, and with 50% reliability, it will increase to approximately 10.2 years. With increasing temperature, the average life of the pipeline declines, and at the same temperature, the higher the reliability is, the lower the average life of the pipeline, which provides a reference for the rational use and maintenance decisions of N80 steel pipes. The life reliability of a pipeline for which the operating time is shorter has a smaller temperature effect than that the operating time is longer. This proves that the effect of temperature on life reliability accumulates. This paper strives to provide a scientific basis for the safety management of oil and gas pipelines.
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.
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