RESUMEN
Accurate evaluation of coalbed methane (CBM) content is crucial for effective exploration and development. Traditional gas content measurement methods based on laboratory analysis of drill core samples are costly, whereas geophysical logging methods offer a cost-effective alternative by providing continuous high-resolution profiles of rock layer physical properties. However, the relationship between CBM content and geophysical logging data is complex and nonlinear, necessitating an advanced prediction method. This study focuses on the No. 3 coal seam in the Shizhuang South Block of the Qinshui Basin, utilizing geophysical logging data and 148 sets of laboratory core samples. We employed the Random Forest (RF) method optimized with a simulated annealing-genetic algorithm (SA-GA) to develop the SA-GA-RF model for evaluating CBM content. The model's performance was validated using test data and new CBM well data, and it was applied to calculate the vertical gas content profiles of No. 3 coal seam across 128 wells. The SA-GA-RF model demonstrated an average relative error of 13.13% in the test data set, outperforming Backpropagation Neural Network (BPNN), Least Squares Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), and multivariate regression (MR) methods. The model also exhibited strong generalizability in new wells and improved model-building efficiency compared to traditional cross-validation grid search methods. The construction of a three-dimensional CBM content model, incorporating well coordinates and elevation data, allowed for detailed identification of high gas content areas and layers. This three-dimensional model offers a more precise characterization than traditional two-dimensional isopleth maps, providing valuable insights for CBM exploration, reserve evaluation, and production optimization.
RESUMEN
AIM: To observe the prevalence of hepatitis B virus (HBV) infection in maintenance hemodialysis patients. METHODS: Eighty-eight hemodialysis patients who had been receiving hemodialysis regularly for an average of 39.45 +/- 7.57 (range from 36 to 49) mo were enrolled in this study. HBV markers were measured in these patients before hemodialysis and in 100 healthy controls by the chemiluminescent microparticle immunoassay (CMI) method in order to compare the incidence of HBV infection in hemodialysis patients versus normal healthy people. All patients were then divided into two groups: patients positive for HBV markers (i.e. those positive for HBsAg, anti-HBc, HBeAg, anti-HBe, with or without positive anti-HBs) (n = 33), and patients negative for HBV markers (including those only positive anti-HBs) (n = 55). The following information was obtained for all patients: socio-demographic data, number of blood transfusions and some laboratory investigations. After 39.45 +/- 7.57 mo follow-up, HBV markers were measured in these patients by CMI. RESULTS: The incidence of HBV infection in maintenance hemodialysis patients was 37.5%, which was higher than in controls (9%). In the patients positive for HBV markers, there were 13 patients (39.4%) who had a history of blood transfusion, which was more than the number [12 (21.8%), P = 0.04] of patients negative for HBV markers. Eight of the 88 patients negative for HBV markers turned out to be positive, while three of the 33 patients positive for HBV markers turned out to be negative. There was no cirrhosis of the liver or hepatoma occurring in these patients. CONCLUSION: Maintenance hemodialysis patients have a higher risk of HBV infection than the average population. The number of blood transfusions is associated with an increased prevalence of HBV. While it is hard for hemodialysis patients to eliminate HBV, the prognosis of patients with positive HBV markers is good.
