The study integrates both the geological and geophysical mapping techniques for groundwater potential studies at Ekwegbe-Agu and the environs, Enugu state, Nigeria for optimal citing of borehole. Located in the Anambr...The study integrates both the geological and geophysical mapping techniques for groundwater potential studies at Ekwegbe-Agu and the environs, Enugu state, Nigeria for optimal citing of borehole. Located in the Anambra Basin between latitudes 6˚43'N and 6˚47'N and longitudes 7˚28'E and 7˚32'E, it is stratigraphycally underlain by, from bottom to top, the Enugu/Nkporo, Mamu and Ajali Formation respectively, a complex geology that make citing of productive borehole in the area problematic leading to borehole failure and dry holes due to inadequate sampling. The study adopted a field and analytic sampling approach, integrating field geological, electrical resistivity and self-potential methods. The software, SedLog v3.1, InterpexIx1Dv.3, and Surfer v10 were employed for the data integration and interpretation. The result of the geological field and borehole data shows 11 sedimentary facies consisting of sandstone, shales and heterolith of sandstone/shale, with the aquifer zone mostly prevalent in the more porous sand-dominated horizons. Mostly the AK and HK were the dominant curve types. An average of 6 geo-electric layers were delineated across all transects with resistivity values ranging from 25.42 - 105.85 Ωm, 186.38 - 3383.3 Ωm, and 2992 - 6286.4 Ωm in the Enugu, Mamu and Ajali Formations respectively. The resistivity of the main aquifer layer ranges from 1 to 500 Ωm. The aquifer thickness within the study area varies between 95 and 140 m. The western and northwestern part of the study area which is underlain mainly by the Ajali Formation showed the highest groundwater potential in the area and suitable for citing productive boreholes.展开更多
In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ...In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.展开更多
Volcanic oil and gas reservoirs are generally buried deep,which leads to a high whole-well coring cost,and the degree of development and size of reservoirs are controlled by volcanic facies.Therefore,accurately identi...Volcanic oil and gas reservoirs are generally buried deep,which leads to a high whole-well coring cost,and the degree of development and size of reservoirs are controlled by volcanic facies.Therefore,accurately identifying volcanic facies by logging curves not only provides the basis of volcanic reservoir prediction but also saves costs during exploration.The Songliao Basin is a‘fault-depression superimposed’composite basin with a typical binary filling structure.Abundant types of volcanic lithologies and facies are present in the Lishu fault depression.Volcanic activity is frequent during the sedimentary period of the Huoshiling Formation.Through systematic petrographic identification of the key exploratory well(SN165C)of the Lishu fault-depression,which is a whole-well core,it is found that the Huoshiling Formation in SN165C contains four facies and six subfacies,including the volcanic conduit facies(crypto explosive breccia subfacies),explosive facies(pyroclastic flow and thermal wave base subfacies),effusive facies(upper and lower subfacies),and volcanogenic sedimentary facies(pyroclastic sedimentary subfacies).Combining core,thin section,and logging data,the authors established identification markers and petrographic chart logging phases,and also interpreted the longitudinal variation in volcanic petro-graphic response characteristics to make the charts more applicable to this area's volcanic petrographic interpretation of the Huoshiling Formation.These charts can provide a basis for the further exploration and development of volcanic oil and gas in this area.展开更多
Hydrate reservoirs are different from the host reservoirs of all other fossil energy sources because the characteristics of hydrate reservoirs are generally controlled by deep-sea fine-grained sedimentation. In such r...Hydrate reservoirs are different from the host reservoirs of all other fossil energy sources because the characteristics of hydrate reservoirs are generally controlled by deep-sea fine-grained sedimentation. In such reservoirs, the reliability of the classical logging evaluation models established for diagenetic reservoirs is questionable. This study used well W8 in the Qiongdongnan Basin to explore the clay content, porosity, saturation, and hydrate-enriched layer identification of a logging-based hydrate reservoir, and it was found that considering the effect of the clay content on the log response is necessary in the logging evaluation of hydrate reservoirs. In the evaluation of clay content, a method based on the optimization inversion method can obtain a more reliable clay content than other methods. Fine-grained sediment reservoirs have a high clay content, and the effect of clay on log responses must be considered when calculating porosity. In addition, combining density logging and neutron porosity logging data can obtain the best porosity calculation results, and the porosity calculation method based on sonic logging predicted that the porosity of the studied reservoir was low. It was very effective to identify hydrate layers based on resistivity, but the clay distribution and pore structure will also affect the relationship between resistivity, porosity and saturation, and it was suggested that the factors effecting the resistivity of different layers should be considered in the saturation evaluation and that a suitable model should be selected. This study also considered the lack of clarity of the relationships among the lithology, physical properties, hydrate-bearing occurrence properties, and log response properties of hydrate reservoirs and the lack of specialized petrophysical models. This research can directly help to improve hydrate logging evaluation.展开更多
Log anomaly detection is an important paradigm for system troubleshooting.Existing log anomaly detection based on Long Short-Term Memory(LSTM)networks is time-consuming to handle long sequences.Transformer model is in...Log anomaly detection is an important paradigm for system troubleshooting.Existing log anomaly detection based on Long Short-Term Memory(LSTM)networks is time-consuming to handle long sequences.Transformer model is introduced to promote efficiency.However,most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing,which introduces parsing errors.They only extract simple semantic feature,which ignores other features,and are generally supervised,relying on the amount of labeled data.To overcome the limitations of existing methods,this paper proposes a novel unsupervised log anomaly detection method based on multi-feature(UMFLog).UMFLog includes two sub-models to consider two kinds of features:semantic feature and statistical feature,respectively.UMFLog applies the log original content with detailed parameters instead of templates or template IDs to avoid log parsing errors.In the first sub-model,UMFLog uses Bidirectional Encoder Representations from Transformers(BERT)instead of random initialization to extract effective semantic feature,and an unsupervised hypersphere-based Transformer model to learn compact log sequence representations and obtain anomaly candidates.In the second sub-model,UMFLog exploits a statistical feature-based Variational Autoencoder(VAE)about word occurrence times to identify the final anomaly from anomaly candidates.Extensive experiments and evaluations are conducted on three real public log datasets.The results show that UMFLog significantly improves F1-scores compared to the state-of-the-art(SOTA)methods because of the multi-feature.展开更多
How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue...How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.展开更多
In order to improve the accuracy and efficiency of Lentinula edodes logs contamination identification,an improved YOLOv5s contamination identification model for Lentinula edodes logs(YOLOv5s-CGGS)is proposed in this p...In order to improve the accuracy and efficiency of Lentinula edodes logs contamination identification,an improved YOLOv5s contamination identification model for Lentinula edodes logs(YOLOv5s-CGGS)is proposed in this paper.Firstly,a CA(coordinate attention)mechanism is introduced in the feature extraction network of YOLOv5s to improve the identifiability of Lentinula edodes logs contamination and the accuracy of target localiza-tion.Then,the CIoU(Complete-IOU)loss function is replaced by an SIoU(SCYLLA-IoU)loss function to improve the model’s convergence speed and inference accuracy.Finally,the GSConv and GhostConv modules are used to improve and optimize the feature fusion network to improve identification efficiency.The method in this paper achieves values of 97.83%,97.20%,and 98.20%in precision,recall,and mAP@0.5,which are 2.33%,3.0%,and 1.5%better than YOLOv5s,respectively.mAP@0.5 is better than YOLOv4,Ghost-YOLOv4,and Mobilenetv3-YOLOv4(improved by 4.61%,5.16%,and 6.04%,respectively),and the FPS increased by two to three times.展开更多
BACKGROUND Colon cancer(CC)is one of the most common cancers of the digestive tract,the third most common cancer worldwide,and the second most common cause of cancer-related deaths.Previous studies have demonstrated a...BACKGROUND Colon cancer(CC)is one of the most common cancers of the digestive tract,the third most common cancer worldwide,and the second most common cause of cancer-related deaths.Previous studies have demonstrated a higher risk of lymph node metastasis(LNM)in young patients with CC.It might be reasonable to treat patients with early-onset locally advanced CC with extended lymph node dissection.However,few studies have focused on early-onset CC(ECC)patients with LNM.At present,the methods of predicting and evaluating the prognosis of ECC patients with LNM are controversial.From the data of patients with CC obtained from the Surveillance,Epidemiology,and End Results(SEER)database,data of young patients with ECC(≤50 years old)was screened.Patients with unknown data were excluded from the study,while the remaining patients were included.The patients were randomly divided into a training group(train)and a testing group(test)in the ratio of 7:3,while building the model.The model was constructed by the training group and verified by the testing group.Using multiple Cox regression models to compare the prediction efficiency of LNM indicators,nomograms were built based on the best model selected for overall survival(OS)and cause-specific survival(CSS).In the two groups,the performance of the nomogram was evaluated by constructing a calibration plot,time-dependent area under the curve(AUC),and decision curve analysis.Finally,the patients were grouped based on the risk score predicted by the prognosis model,and the survival curve was constructed after comparing the survival status of the high and low-risk groups.RESULTS Records of 26922 ECC patients were screened from the SEER database.N classification,positive lymph nodes(PLN),lymph node ratio(LNR)and log odds of PLN(LODDS)were considered to be independent predictors of OS and CSS.In addition,independent risk factors for OS included gender,race,marital status,primary site,histology,grade,T,and M classification,while the independent prognostic factors for CSS included race,marital status,primary site,grade,T,and M classification.The prediction model including LODDS is composed of minimal Akaike information criterion,maximal concordance indexes,and AUCs.Factors including gender,race,marital status,primary site,histology,grade,T,M classification,and LODDS were integrated into the OS nomogram,while race,marital status,primary site,grade,T,M classification,and LODDS were included into the CSS nomogram.The nomogram representing both cohorts had been successfully verified in terms of prediction accuracy and clinical practicability.CONCLUSION LODDS is superior to N-stage,PLN,and LNR of ECC.The nomogram containing LODDS might be helpful in tumor evaluation and clinical decision-making,since it provides an appropriate prediction of ECC.展开更多
文摘The study integrates both the geological and geophysical mapping techniques for groundwater potential studies at Ekwegbe-Agu and the environs, Enugu state, Nigeria for optimal citing of borehole. Located in the Anambra Basin between latitudes 6˚43'N and 6˚47'N and longitudes 7˚28'E and 7˚32'E, it is stratigraphycally underlain by, from bottom to top, the Enugu/Nkporo, Mamu and Ajali Formation respectively, a complex geology that make citing of productive borehole in the area problematic leading to borehole failure and dry holes due to inadequate sampling. The study adopted a field and analytic sampling approach, integrating field geological, electrical resistivity and self-potential methods. The software, SedLog v3.1, InterpexIx1Dv.3, and Surfer v10 were employed for the data integration and interpretation. The result of the geological field and borehole data shows 11 sedimentary facies consisting of sandstone, shales and heterolith of sandstone/shale, with the aquifer zone mostly prevalent in the more porous sand-dominated horizons. Mostly the AK and HK were the dominant curve types. An average of 6 geo-electric layers were delineated across all transects with resistivity values ranging from 25.42 - 105.85 Ωm, 186.38 - 3383.3 Ωm, and 2992 - 6286.4 Ωm in the Enugu, Mamu and Ajali Formations respectively. The resistivity of the main aquifer layer ranges from 1 to 500 Ωm. The aquifer thickness within the study area varies between 95 and 140 m. The western and northwestern part of the study area which is underlain mainly by the Ajali Formation showed the highest groundwater potential in the area and suitable for citing productive boreholes.
基金supported by CNPC-CZU Innovation Alliancesupported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology (2022YFC2806403)。
文摘In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.
基金Supported by projects of the National Natural Science Foundatio n of China(Nos.41972313,41790453).
文摘Volcanic oil and gas reservoirs are generally buried deep,which leads to a high whole-well coring cost,and the degree of development and size of reservoirs are controlled by volcanic facies.Therefore,accurately identifying volcanic facies by logging curves not only provides the basis of volcanic reservoir prediction but also saves costs during exploration.The Songliao Basin is a‘fault-depression superimposed’composite basin with a typical binary filling structure.Abundant types of volcanic lithologies and facies are present in the Lishu fault depression.Volcanic activity is frequent during the sedimentary period of the Huoshiling Formation.Through systematic petrographic identification of the key exploratory well(SN165C)of the Lishu fault-depression,which is a whole-well core,it is found that the Huoshiling Formation in SN165C contains four facies and six subfacies,including the volcanic conduit facies(crypto explosive breccia subfacies),explosive facies(pyroclastic flow and thermal wave base subfacies),effusive facies(upper and lower subfacies),and volcanogenic sedimentary facies(pyroclastic sedimentary subfacies).Combining core,thin section,and logging data,the authors established identification markers and petrographic chart logging phases,and also interpreted the longitudinal variation in volcanic petro-graphic response characteristics to make the charts more applicable to this area's volcanic petrographic interpretation of the Huoshiling Formation.These charts can provide a basis for the further exploration and development of volcanic oil and gas in this area.
基金funded by the Laboratory for Marine Geology,Qingdao National Laboratory for Marine Science and Technology(No.MGQNLM-KF202004)Hainan Provincial Natural Science Foundation of China(Nos.422RC746 and 421QN281)+2 种基金the National Natural Science Foundation of China(No.42106213)the China Postdoctoral Science Foundation(Nos.2021M690161 and 2021T140691)the Postdoctorate Funded Project in Hainan Province.
文摘Hydrate reservoirs are different from the host reservoirs of all other fossil energy sources because the characteristics of hydrate reservoirs are generally controlled by deep-sea fine-grained sedimentation. In such reservoirs, the reliability of the classical logging evaluation models established for diagenetic reservoirs is questionable. This study used well W8 in the Qiongdongnan Basin to explore the clay content, porosity, saturation, and hydrate-enriched layer identification of a logging-based hydrate reservoir, and it was found that considering the effect of the clay content on the log response is necessary in the logging evaluation of hydrate reservoirs. In the evaluation of clay content, a method based on the optimization inversion method can obtain a more reliable clay content than other methods. Fine-grained sediment reservoirs have a high clay content, and the effect of clay on log responses must be considered when calculating porosity. In addition, combining density logging and neutron porosity logging data can obtain the best porosity calculation results, and the porosity calculation method based on sonic logging predicted that the porosity of the studied reservoir was low. It was very effective to identify hydrate layers based on resistivity, but the clay distribution and pore structure will also affect the relationship between resistivity, porosity and saturation, and it was suggested that the factors effecting the resistivity of different layers should be considered in the saturation evaluation and that a suitable model should be selected. This study also considered the lack of clarity of the relationships among the lithology, physical properties, hydrate-bearing occurrence properties, and log response properties of hydrate reservoirs and the lack of specialized petrophysical models. This research can directly help to improve hydrate logging evaluation.
基金supported in part by the National Natural Science Foundation of China under Grant 62272062the Scientific Research Fund of Hunan Provincial Transportation Department(No.202143)the Open Fund ofKey Laboratory of Safety Control of Bridge Engineering,Ministry of Education(Changsha University of Science Technology)under Grant 21KB07.
文摘Log anomaly detection is an important paradigm for system troubleshooting.Existing log anomaly detection based on Long Short-Term Memory(LSTM)networks is time-consuming to handle long sequences.Transformer model is introduced to promote efficiency.However,most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing,which introduces parsing errors.They only extract simple semantic feature,which ignores other features,and are generally supervised,relying on the amount of labeled data.To overcome the limitations of existing methods,this paper proposes a novel unsupervised log anomaly detection method based on multi-feature(UMFLog).UMFLog includes two sub-models to consider two kinds of features:semantic feature and statistical feature,respectively.UMFLog applies the log original content with detailed parameters instead of templates or template IDs to avoid log parsing errors.In the first sub-model,UMFLog uses Bidirectional Encoder Representations from Transformers(BERT)instead of random initialization to extract effective semantic feature,and an unsupervised hypersphere-based Transformer model to learn compact log sequence representations and obtain anomaly candidates.In the second sub-model,UMFLog exploits a statistical feature-based Variational Autoencoder(VAE)about word occurrence times to identify the final anomaly from anomaly candidates.Extensive experiments and evaluations are conducted on three real public log datasets.The results show that UMFLog significantly improves F1-scores compared to the state-of-the-art(SOTA)methods because of the multi-feature.
基金supported by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735)Science Foundation of China University of Petroleum,Beijing(Grant Nos.2462020XKJS02 and 2462020YXZZ004).
文摘How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.
基金funded by the Major Scientific and Technological Innovation Project of Shandong Province(Grant No.2022CXGC010609)the Talent Project of Zibo City.
文摘In order to improve the accuracy and efficiency of Lentinula edodes logs contamination identification,an improved YOLOv5s contamination identification model for Lentinula edodes logs(YOLOv5s-CGGS)is proposed in this paper.Firstly,a CA(coordinate attention)mechanism is introduced in the feature extraction network of YOLOv5s to improve the identifiability of Lentinula edodes logs contamination and the accuracy of target localiza-tion.Then,the CIoU(Complete-IOU)loss function is replaced by an SIoU(SCYLLA-IoU)loss function to improve the model’s convergence speed and inference accuracy.Finally,the GSConv and GhostConv modules are used to improve and optimize the feature fusion network to improve identification efficiency.The method in this paper achieves values of 97.83%,97.20%,and 98.20%in precision,recall,and mAP@0.5,which are 2.33%,3.0%,and 1.5%better than YOLOv5s,respectively.mAP@0.5 is better than YOLOv4,Ghost-YOLOv4,and Mobilenetv3-YOLOv4(improved by 4.61%,5.16%,and 6.04%,respectively),and the FPS increased by two to three times.
文摘BACKGROUND Colon cancer(CC)is one of the most common cancers of the digestive tract,the third most common cancer worldwide,and the second most common cause of cancer-related deaths.Previous studies have demonstrated a higher risk of lymph node metastasis(LNM)in young patients with CC.It might be reasonable to treat patients with early-onset locally advanced CC with extended lymph node dissection.However,few studies have focused on early-onset CC(ECC)patients with LNM.At present,the methods of predicting and evaluating the prognosis of ECC patients with LNM are controversial.From the data of patients with CC obtained from the Surveillance,Epidemiology,and End Results(SEER)database,data of young patients with ECC(≤50 years old)was screened.Patients with unknown data were excluded from the study,while the remaining patients were included.The patients were randomly divided into a training group(train)and a testing group(test)in the ratio of 7:3,while building the model.The model was constructed by the training group and verified by the testing group.Using multiple Cox regression models to compare the prediction efficiency of LNM indicators,nomograms were built based on the best model selected for overall survival(OS)and cause-specific survival(CSS).In the two groups,the performance of the nomogram was evaluated by constructing a calibration plot,time-dependent area under the curve(AUC),and decision curve analysis.Finally,the patients were grouped based on the risk score predicted by the prognosis model,and the survival curve was constructed after comparing the survival status of the high and low-risk groups.RESULTS Records of 26922 ECC patients were screened from the SEER database.N classification,positive lymph nodes(PLN),lymph node ratio(LNR)and log odds of PLN(LODDS)were considered to be independent predictors of OS and CSS.In addition,independent risk factors for OS included gender,race,marital status,primary site,histology,grade,T,and M classification,while the independent prognostic factors for CSS included race,marital status,primary site,grade,T,and M classification.The prediction model including LODDS is composed of minimal Akaike information criterion,maximal concordance indexes,and AUCs.Factors including gender,race,marital status,primary site,histology,grade,T,M classification,and LODDS were integrated into the OS nomogram,while race,marital status,primary site,grade,T,M classification,and LODDS were included into the CSS nomogram.The nomogram representing both cohorts had been successfully verified in terms of prediction accuracy and clinical practicability.CONCLUSION LODDS is superior to N-stage,PLN,and LNR of ECC.The nomogram containing LODDS might be helpful in tumor evaluation and clinical decision-making,since it provides an appropriate prediction of ECC.