| 1 | Application of artificial intelligence to forecast hydrocarbon production from shales显示文摘Artificial intelligence(AI)methods and applications have recently gained a great deal of attention in many areas,including fields of mathematics,neuroscience,economics,engineering,linguistics,gaming,and many others.This is due to the surge of innovative and sophisticated AI techniques applications to highly complex problems as well as the powerful new developments in high speed computing.Various applications of AI in everyday life include machine learning,pattern recognition,robotics,data processing and analysis,etc.The oil and gas industry is not behind either,in fact,AI techniques have recently been applied to estimate PVT properties,optimize production,predict recoverable hydrocarbons,optimize well placement using pattern recognition,optimize hydraulic fracture design,and to aid in reservoir characterization efforts.In this study,three different AI models are trained and used to forecast hydrocarbon production from hydraulically fractured wells.Two vastly used artificial intelligence methods,namely the Least Square Support Vector Machine(LSSVM)and the Artificial Neural Networks(ANN),are compared to a traditional curve fitting method known as Response Surface Model(RSM)using second order polynomial equations to determine production from shales.The objective of this work is to further explore the potential of AI in the oil and gas industry.Eight parameters are considered as input factors to build the model:reservoir permeability,initial dissolved gas-oil ratio,rock compressibility,gas relative permeability,slope of gas oil ratio,initial reservoir pressure,flowing bottom hole pressure,and hydraulic fracture spacing.The range of values used for these parameters resemble real field scenarios from prolific shale plays such as the Eagle Ford,Bakken,and the Niobrara in the United States.Production data consists of oil recovery factor and produced gas-oil ratio(GOR)generated from a generic hydraulically fractured reservoir model using a commercial simulator.The Box-Behnken experiment design was used to minimize the number of simulations for this study.Five time-based models(for production periods of 90 days,1 year,5 years,10 years,and 15 years)and one rate-based model(when oil rate drops to 5 bbl/day/fracture)were considered.Particle Swarm Optimization(PSO)routine is used in all three surrogate models to obtain the associated model parameters.Models were trained using 80%of all data generated through simulation while 20%was used for testing of the models.All models were evaluated by measuring the goodness of fit through the coefficient of determination(R2)and the Normalized Root Mean Square Error(NRMSE).Results show that RSM and LSSVM have very accurate oil recovery forecasting capabilities while LSSVM shows the best performance for complex GOR behavior.Furthermore,all surrogate models are shown to serve as reliable proxy reservoir models useful for fast fluid recovery forecasts and sensitivity analyses. | Palash Panja Raul Velasco Manas Pathak Milind Deo | 2018 | Petroleum2018，4，1： | 4 |
| 2 | Effect of thrombocytopenia and platelet transfusion on outcomes of acute variceal bleeding in patients with chronic liver disease显示文摘BACKGROUND Platelet transfusion in acute variceal bleeding(AVB)is recommended by few guidelines and is common in routine clinical practice,even though the effect of thrombocytopenia and platelet transfusion on the outcomes of AVB is unclear.AIM To determine how platelet counts,platelets transfusions,and fresh frozen plasma transfusions affect the outcomes of AVB in cirrhosis patients in terms of bleeding control,rebleeding,and mortality.METHODS Prospectively maintained database was used to analyze the outcomes of cirrhosis patients who presented with AVB.The outcomes were assessed as the risk of rebleeding at days 5 and 42,and risk of death at day 42,considering the platelet counts and platelet transfusion.Propensity score matching(PSM)was used to compare the outcomes in those who received platelet transfusion.Statistical comparisons were done using Kaplan-Meier curves with log-rank tests and Coxproportional hazard model for rebleeding and for 42-d mortality.RESULTS The study included 913 patients,with 83.5%men,median age 45 years,and Model for End-stage Liver Disease score 14.7.Platelet count<20×10^(9)/L,20-50×10^(9)/L,and>50×10^(9)/L were found in 23(2.5%),168(18.4%),and 722(79.1%)patients,respectively.Rebleeding rates were similar between the three platelet groups on days 5 and 42(13%,6.5%,and 4.7%,respectively,on days 5,P=0.150;and 21.7%,17.3%,and 14.4%,respectively,on days 42,P=0.433).At day 42,the mortality rates for the three platelet groups were also similar(13.0%,23.2%,and 17.2%,respectively,P=0.153).On PSM analysis patients receiving platelets transfusions(n=89)had significantly higher rebleeding rates on day 5(14.6%vs 4.5%;P=0.039)and day 42(32.6%vs 15.7%;P=0.014),compared to those who didn't.The mortality rates were also higher among patients receiving platelets(25.8%vs 23.6%;P=0.862),although the difference was not significant.On multivariate analysis,platelet transfusion and not platelet count,was independently associated with 42-d rebleeding.Hepatic encephalopathy was independently associated with 42-d mortality.CONCLUSION Thrombocytopenia had no effect on rebleeding rates or mortality in cirrhosis patients with AVB;however,platelet transfusion increased rebleeding on days 5 and 42,with a higher but nonsignificant effect on mortality. | Sagnik Biswas Manas Vaishnav Piyush Pathak Deepak Gunjan Soumya Jagannath Mahapatra Saurabh Kedia Gyanranjan Rout Bhaskar Thakur Baibaswata Nayak Ramesh Kumar Shalimar | 2022 | World Journal of Hepatology2022，14，7： | 0 |
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