Accurate prediction of estimated ultimate recovery (EUR) and quantification of important engineering and geological parameters play a decisive role in production parameter optimization and investment decision of shale...
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Accurate prediction of estimated ultimate recovery (EUR) and quantification of important engineering and geological parameters play a decisive role in production parameter optimization and investment decision of shale gas wells. This study proposes an integrated learning framework for EUR prediction and production parameter quantification in shale gas wells using catboost algorithm. catboost is designed to identify complex relationships efficiently and accurately between geological and engineering parameters and target values EUR. Furthermore, the EUR prediction model based on catboost was used to identify important features. Various machine learning interpretation methods are used to visualize the marginal effects of important and interesting features in an attempt to optimize production parameters by quantifying the features. Experiments are conducted to evaluate the performance of the proposed the integrated learning framework in the deep shale gas well data set of Luzhou block in Sichuan Basin, China. Results demonstrate that compared with other mainstream ensemble learning algorithms, the proposed EUR prediction model based on catboost algorithm performs well on small data sets, with a prediction error of only 12.31%. The post-interpretation tool of the model was used to find out four important characteristics affecting EUR in Luzhou block, namely, contents of brittle minerals, fracturing length, porosity and fracturing section. The optimal combination of geological and engineering parameters for the block to maximize EUR is identified by using the established efficient and accurate EUR prediction model as input and the visualization of the marginal effects of the features of interest as output. This work can effectively provide guidance for EUR prediction and production parameter optimization of shale gas wells.
The fertility rate of China has shown an overall decline trend,so the fully understanding of the factors affecting China’s fertility intention has become the focus of the *** on CFPS2020 data,the study subjects were ...
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The fertility rate of China has shown an overall decline trend,so the fully understanding of the factors affecting China’s fertility intention has become the focus of the *** on CFPS2020 data,the study subjects were women of age between 20 and 49,who were born after the 1970s and were influenced by the family planning ***,SMOTE-catboost algorithm was used to construct a *** results show that:(1)For the women’s willingness to have multiple children,the SMOTE-catboost algorithm is more effective than the catboost algorithm,and the classification accuracy is improved by 8 percentage points.(2)The factors influencing women’s willingness to have multiple children were ranked by social status,intergenerational care,education level and *** with high social status are more willing to have multiple children;All-day intergenerational care has a positive effect on women’s willingness to have multiple *** women’s willingness to have multiple children declines with the rise of education level but increases with age.(3)Combined with the CFPS2018 data,the influencing factors changed over *** 2018,the important factors related to women’s willingness to have multiple children were mainly related to economic,within which the income ranked locally is the most important *** 2020,the most important factor changed to be social status while economic became less important.
Sea surface partial pressure of CO2 (pCO(2)) is a critical parameter in the quantification of air-sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this st...
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Sea surface partial pressure of CO2 (pCO(2)) is a critical parameter in the quantification of air-sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO(2) in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (catboost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 mu atm, the mean bias error (MAE) is 4.92 mu atm and the coefficient of determination (R-2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO(2) in the North Atlantic Ocean during 2003-2020. It can be found that the North Atlantic sea surface pCO(2) has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO(2) in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.
作者：
Han ShuwenYang XiZhou QingZhuang JingWu WeiHuzhou Univ
Huzhou Cent Hosp Affiliated Cent Hosp Dept Oncol Huzhou Peoples R China Huzhou Univ
Huzhou Cent Hosp Affiliated Cent Hosp Dept Nursing Huzhou Peoples R China Huzhou Univ
Grad Sch Nursing Huzhou Peoples R China Huzhou Univ
Huzhou Cent Hosp Affiliated Cent Hosp Dept Gastroenterol 198 Hongqi Rd Huzhou 313000 Zhejiang Peoples R China
Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown gr...
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Background Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model. Methods The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset . The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and catboost, for classification of CAD with liver metastasis samples were compared using the MCC dataset by detecting their classification test accuracy. In addition, the consortium datasets of and were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug-targeted network construction of the feature genes when applying the optimal method were conducted. Results The optimal catboost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a "steroid metabolic process" and "lipoprotein particle receptor binding" (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the "complement and coagulation cascade" pathways (eg FGA, F2, and F9). In a drug-target interaction network, F2 and F9 were predicted as targets of menadione. Conclusion The catboost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis.
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