Flowing bottom-hole pressure(FBHP)is a key metric parameter in the evaluation of performances of oil and gas production wells.An accurate prediction of FBHP is highly required in the petroleum industry for many applic...Flowing bottom-hole pressure(FBHP)is a key metric parameter in the evaluation of performances of oil and gas production wells.An accurate prediction of FBHP is highly required in the petroleum industry for many applications,such the hydrocarbon production optimization,oil lifting cost,and assessment of workover operations.Production and reservoir engineers rely on empirical correlations and mechanistic models exist in open resources to estimate the FBHP.Several empirical models have been developed based on simulation and laboratory results that involved many assumptions that reduce the model's accuracy when they are applied for the field applications.The technologies of machine learning(ML)are one discipline of Artificial Intelligence(AI)techniques provide promising tools that help solving human's complex problems.This study develops machine-learning based models to predict the multiphase FBHP using three machine learning techniques that are Random forest,K-Nearest Neighbors(KNN),and artificial neural network(ANN).Results showed that using an artificial neural network model give error of 2.5%to estimate the FBHP which is less than the random forest and K-nearest neighbor models with error of 3.6%and 4%respectively.The ML models were developed based on a surface production data,which makes the FBHP is predicted using actual field data.The accuracy of the proposed models from ML was evaluated by comparing the results with the actual dataset values to ensure the effectiveness of the work.The results of this study show the potential of artificial intelligence in predicting the most complex parameter in the multiphase petroleum production process.展开更多
This paper introduces a systems theory-driven framework to integration artificial intelligence(AI)into traditional Chinese medicine(TCM)research,enhancing the understanding of TCM’s holistic material basis while adhe...This paper introduces a systems theory-driven framework to integration artificial intelligence(AI)into traditional Chinese medicine(TCM)research,enhancing the understanding of TCM’s holistic material basis while adhering to evidence-based principles.Utilizing the System Function Decoding Model(SFDM),the research progresses through define,quantify,infer,and validate phases to systematically explore TCM’s material basis.It employs a dual analytical approach that combines top-down,systems theory-guided perspectives with bottom-up,elements-structure-function methodologies,provides comprehensive insights into TCM’s holistic material basis.Moreover,the research examines AI’s role in quantitative assessment and predictive analysis of TCM’s material components,proposing two specific AIdriven technical applications.This interdisciplinary effort underscores AI’s potential to enhance our understanding of TCM’s holistic material basis and establishes a foundation for future research at the intersection of traditional wisdom and modern technology.展开更多
The functional concept of using synthetic entities to supplement or replace certain functions or structures of biological cells is realized by the development of atypical artificial cells using a bottom-up approach.Tr...The functional concept of using synthetic entities to supplement or replace certain functions or structures of biological cells is realized by the development of atypical artificial cells using a bottom-up approach.Tremendous progress has been achieved over the past 5 years that focuses on the therapeutic applications of atypical artificial cells,especially in the anticancer arena.Artificial cell-based anticancer strategies have demon-strated eminent advantages over conventional anticancer tactics,with excellent biocompatibility and targeting capability.The present review commences with introducing the constructing principles and classification of artificial cells.Artificial cell-based applications in cancer prophylaxis,diagnosis,and treatment are subsequently highlighted.These stimulating outcomes may inspire the development of next-generation anticancer ther-apeutic strategies.展开更多
This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement wh...This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling.For the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill strings.Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field data.For modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new fields.The gathered data encompasses a variety of well data,general information/data,depths,hole size,and depths.The developed model was compared with data obtained from two new fields based on its capability,stability and accuracy.The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9.The high values of R^(2) for the two models suggest the relative reliability of the modelling techniques.The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for Feed Forward model respectively;the least errors were recorded for the Extra Tree model.Also,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the Feed Forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the Feed Forward model.Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on the relationship between the bottomhole press展开更多
A new method using high water content material to mechanically fill cross roadways to form artificial bottom for coal faces was introduced. The reasonable determination of filling range, the optimization of the compou...A new method using high water content material to mechanically fill cross roadways to form artificial bottom for coal faces was introduced. The reasonable determination of filling range, the optimization of the compounding ratio of high water content material, and the filling technique were discussed in detail. This new method has been spread after industrial testing in Baodian Colliery. Compared with the traditional method, the manual wooden chock method, the new one decreases about 40% of the filling range and cost in dealing every one set of cross roadway in the testing condition.展开更多
文摘Flowing bottom-hole pressure(FBHP)is a key metric parameter in the evaluation of performances of oil and gas production wells.An accurate prediction of FBHP is highly required in the petroleum industry for many applications,such the hydrocarbon production optimization,oil lifting cost,and assessment of workover operations.Production and reservoir engineers rely on empirical correlations and mechanistic models exist in open resources to estimate the FBHP.Several empirical models have been developed based on simulation and laboratory results that involved many assumptions that reduce the model's accuracy when they are applied for the field applications.The technologies of machine learning(ML)are one discipline of Artificial Intelligence(AI)techniques provide promising tools that help solving human's complex problems.This study develops machine-learning based models to predict the multiphase FBHP using three machine learning techniques that are Random forest,K-Nearest Neighbors(KNN),and artificial neural network(ANN).Results showed that using an artificial neural network model give error of 2.5%to estimate the FBHP which is less than the random forest and K-nearest neighbor models with error of 3.6%and 4%respectively.The ML models were developed based on a surface production data,which makes the FBHP is predicted using actual field data.The accuracy of the proposed models from ML was evaluated by comparing the results with the actual dataset values to ensure the effectiveness of the work.The results of this study show the potential of artificial intelligence in predicting the most complex parameter in the multiphase petroleum production process.
基金supported by the National Natural Science Foundation of China(82230117).
文摘This paper introduces a systems theory-driven framework to integration artificial intelligence(AI)into traditional Chinese medicine(TCM)research,enhancing the understanding of TCM’s holistic material basis while adhering to evidence-based principles.Utilizing the System Function Decoding Model(SFDM),the research progresses through define,quantify,infer,and validate phases to systematically explore TCM’s material basis.It employs a dual analytical approach that combines top-down,systems theory-guided perspectives with bottom-up,elements-structure-function methodologies,provides comprehensive insights into TCM’s holistic material basis.Moreover,the research examines AI’s role in quantitative assessment and predictive analysis of TCM’s material components,proposing two specific AIdriven technical applications.This interdisciplinary effort underscores AI’s potential to enhance our understanding of TCM’s holistic material basis and establishes a foundation for future research at the intersection of traditional wisdom and modern technology.
基金National Clinical Research Center for Oral Diseases,Grant/Award Number:LCA202004National Natural Science Foundation of China,Grant/Award Numbers:82301043,82325012Shaanxi Key Scientific and Technological Innovation Team,Grant/Award Number:2020TD-033。
文摘The functional concept of using synthetic entities to supplement or replace certain functions or structures of biological cells is realized by the development of atypical artificial cells using a bottom-up approach.Tremendous progress has been achieved over the past 5 years that focuses on the therapeutic applications of atypical artificial cells,especially in the anticancer arena.Artificial cell-based anticancer strategies have demon-strated eminent advantages over conventional anticancer tactics,with excellent biocompatibility and targeting capability.The present review commences with introducing the constructing principles and classification of artificial cells.Artificial cell-based applications in cancer prophylaxis,diagnosis,and treatment are subsequently highlighted.These stimulating outcomes may inspire the development of next-generation anticancer ther-apeutic strategies.
基金The authors would like to thank Covenant University Centre for Research Innovation and Discovery(CUCRID)Ota,Nigeria for its support in making the publication of this research possible.
文摘This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling.For the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill strings.Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field data.For modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new fields.The gathered data encompasses a variety of well data,general information/data,depths,hole size,and depths.The developed model was compared with data obtained from two new fields based on its capability,stability and accuracy.The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9.The high values of R^(2) for the two models suggest the relative reliability of the modelling techniques.The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for Feed Forward model respectively;the least errors were recorded for the Extra Tree model.Also,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the Feed Forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the Feed Forward model.Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on the relationship between the bottomhole press
文摘A new method using high water content material to mechanically fill cross roadways to form artificial bottom for coal faces was introduced. The reasonable determination of filling range, the optimization of the compounding ratio of high water content material, and the filling technique were discussed in detail. This new method has been spread after industrial testing in Baodian Colliery. Compared with the traditional method, the manual wooden chock method, the new one decreases about 40% of the filling range and cost in dealing every one set of cross roadway in the testing condition.
