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| 1 | Forecasting multiphase flowing bottom-hole pressure of vertical oil wells using three machine learning techniques显示文摘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. | Nagham Amer Sami Dhorgham Skban Ibrahim | 2021 | Petroleum Research2021，6，4： | 3 |
| 2 | Predictors of Clostridium difficile infection severity in patients hospitalised in medical intensive care显示文摘AIM:To describe and analyse factors associated with Clostridium difficile infection(CDI)severity in hospitalised medical intensive care unit patients.METHODS:We performed a retrospective cohort study of 40 patients with CDI in a medical intensive care unit(MICU)at a French university hospital.We include patients hospitalised between January 1,2007and December 31,2011.Data on demographics characteristics,past medical history,CDI description was collected.Exposure to risk factors associated with CDI within 8 wk before CDI was recorded,including previous hospitalisation,nursing home residency,antibiotics,antisecretory drugs,and surgical procedures.RESULTS:All included cases had their first episode of CDI.The mean incidence rate was 12.94 cases/1000admitted patients,and 14.93,8.52,13.24,19.70,and8.31 respectively per 1000 admitted patients annually from 2007 to 2011.Median age was 62.9[interquartile range(IQR)55.4-72.40]years,and 13(32.5%)were women.Median length of MICU stay was 14.0d(IQR 5.0-22.8).In addition to diarrhoea,the clinical symptoms of CDI were fever(>38℃)in 23 patients,abdominal pain in 15 patients,and ileus in 1 patient.The duration of diarrhoea was 13.0(8.0-19.5)d.In addition to diarrhoea,the clinical symptoms of CDI were fever(>38℃)in 23 patients,abdominal pain in 15 patients,and ileus in 1 patient.Prior to CDI,38patients(95.0%)were exposed to antibiotics,and 12(30%)received at least 4 antibiotics.Fluoroquinolones,3rdgeneration cephalosporins,coamoxiclav and tazocillin were prescribed most frequently(65%,55%,40%and 37.5%,respectively).The majority of cases were hospital-acquired(n=36,90%),with 5 cases(13.9%)being MICU-acquired.Fifteen patients had severe CDI.The crude mortality rate within 30 d after diagnosis was 40%(n=16),with 9 deaths(9 over 16;56.3%)related to CDI.Of our 40 patients,15(37.5%)had severe CDI.Multivariate logistic regression showed that male gender[odds ratio(OR):8.45;95%CI:1.06-67.16,P=0.044],rising serum C-reactive protein levels(OR=1.11;95%CI:1.02-1.21,P=0.021),and previous exposure to fluoroquinolones(OR=9.29;95%CI:1.16-74.284,P=0.036)were independently associated with severe CDI.CONCLUSION:We report predictors of severe CDI not dependent on time of assessment.Such factors could help in the development of a quantitative score in ICU’s patients. | Nagham Khanafer Abdoulaye Touré Cécile Chambrier Martin Cour Marie-Elisabeth Reverdy Laurent Argaud Philippe Vanhems | 2013 | World Journal of Gastroenterology2013，19，44： | 2 |
| 3 | Computational fluid dynamic(CFD)modelling of transient flow in the intermittent gas lift显示文摘A computational fluid dynamics model(CFD)is developed for intermittent gas lift techniques.The simulation is conducted for a test section of 18 m vertical tube with 0.076 m in diameter using air as injection gas and oil as a formation fluid.The results obtained from the CFD model are validated with the experiment results from the literature.The current study shows that computational modeling is a proven simulation program for predicting intermittent gas lift characteristics and the transient flow parameters that are changing with time and position in the coordinate system.The model can predict the slug velocity behavior for different injection pressure.The slug velocity profile shows three regions;the first region is the rapid acceleration at the initial time of injection,the second region shows the nearly constant velocity until the slug reaches the surface and the third region is again the rapid acceleration when the liquid starts to produce.Also,the results obtained from this model show that as the gas injection pressure increases,the liquid slug velocity increase,and the region of the constant velocity decrease.The effect of the injection time on the liquid production rate has been studied for two different gas injection pressures of 40 psig and 50 psig.The developed model shows that more than 50%of the liquid production is coming from after flow period. | Nagham Amer Sami Zoltan Turzo | 2020 | Petroleum Research2020，5，2： | 1 |
| 4 | From vulnerable plaque to vulnerable patient:a call for new definitions and risk assessment strategies:part1显示文摘 | NAGHAM LIBBYP FALKE | 2003 | Cireulation2003，108，20： | 1 |
| 5 | Computational fluid dynamic(CFD)simulation of pilot operated intermittent gas lift valve显示文摘To design an efficient intermittent gas-lift installation,reliable information is needed in the performance of all process components,from the outer boundary of the reservoir to the surface separators.The gas lift valve is the one critical component that affects the design of the whole system.In intermittent producing system,the pilot gas-lift valve is extremely used to control the point of compressed gas entry into the production tubing and acts as a pressure regulator.A novel approach using computational fluid dynamics simulation was performed in this study to develop a dynamic model for the gas passage performance of a 1-in.,Nitrogen-charged,pilot gas-lift valve.Dynamic performance curves were obtained by using Methane as an injection gas with flow rates reaching up to 4.5 MMscf/day.This study investigates the effect of internal pressure,velocity and temperature distribution within the pilot valve that cannot be predicted in the experiments and mathematical models during the flow-performance studies.A general equation of the nonconstant discharge coefficient has been developed for 1-inch pilot valve to be used for further calculation in the industry without using CFD model.The developed model significantly reduces the complexity of the data required to calculate the discharge coefficient. | Nagham Amer Sami Zoltan Turzo | 2020 | Petroleum Research2020，5，3： | 1 |
| 6 | Trust evaluation of a system for an activity with subjective logic 显示文摘 | NAGHAM Alhadad YANN Busnel | 2014 | Trust Privacy and Security in Digital Business2014，8647，： | 1 |
| 7 | Overlapping Shadow Rendering for Outdoor Augmented Reality显示文摘Realism rendering methods of outdoor augmented reality(AR)is an interesting topic.Realism items in outdoor AR need advanced impacts like shadows,sunshine,and relations between unreal items.A few realistic rendering approaches were built to overcome this issue.Several of these approaches are not dealt with real-time rendering.However,the issue remains an active research topic,especially in outdoor rendering.This paper introduces a new approach to accomplish reality real-time outdoor rendering by considering the relation between items in AR regarding shadows in any place during daylight.The proposed method includes three principal stages that cover various outdoor AR rendering challenges.First,real shadow recognition was generated considering the sun’s location and the intensity of the shadow.The second step involves real shadow protection.Finally,we introduced a shadow production algorithm technique and shades through its impacts on unreal items in the AR.The selected approach’s target is providing a fast shadow recognition technique without affecting the system’s accuracy.It achieved an average accuracy of 95.1%and an area under the curve(AUC)of 92.5%.The outputs demonstrated that the proposed approach had enhanced the reality of outside AR rendering.The results of the proposed method outperformed other state-of-the-art rendering shadow techniques’outcomes. | Naira Elazab Shaker El-Sappagh Ahmed Atwan Hassan Soliman Mohammed Elmogy Louai Alarabi Nagham Mekky | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 8 | Investigating tight oil reservoir production performance:Influence of geomechanical parameters and their distribution显示文摘Geomechanical properties have a prominent influence on reservoir stresses,which consequently reduce permeability and porosity with pressure depletion.These properties significantly affect the accuracy of reservoir modeling and recovery calculation,but have not been fully studied;therefore,more work is needed.Full field data and laboratory measurements are included in the study.The work involves deriving an equation by combining experimental data for permeability and porosity reduction during a change in stress with the poroelastic stress equation to investigate the impact of Poisson's ratio and Young's modulus on the reduction of permeability and porosity with pressure depletion.Most simulation studies assume constant geomechanical properties across the entire reservoir or for each individual reservoir layer.In this study,three approaches were considered for the Poisson's ratio and Young's modulus in the reservoir model:1)constant average values assigned to the entire reservoir,2)constant average values assigned to each layer,and 3)constant values assigned to each grid block.The validity of the model results was checked by history matching with production and pressure data.For the studied tight reservoir,the Poisson's ratio and Young's modulus significantly affected the permeability and porosity reduction with pressure depletion.The impact of Young's modulus was more pronounced than Poisson's ratio.The simulation results for oil rate,cumulative oil production,and water cut for the reservoir and a selected well showed that applying the three suggested geomechanical approaches resulted in a substantial discrepancy in the model outcome.In general,the coupled model with the mapped geomechanical properties resulted in lower oil and water production.This is attributed to the large values of mapped Young's modulus in parts of the reservoir which resulted in large permeability reduction and subsequently lower oil and water production is expected.In contrast lower Young's modulus per layer was obtained due to averaging process.Poisson's ratio effect on fluid production is much less significant due to its small effect on permeability reduction with depletion.Similarly,the adoption of different geomechanical property values for each layer yielded a relatively lower production outcome than when using a constant value for the entire reservoir.The study indicates the importance of considering the detailed description of the reservoir geomechanical properties to obtain reliable simu-lation results. | Sameera M.Hamd-Allah Nagham Jasim Al-Ameri | 2023 | Petroleum Research2023，8，4： | 0 |
| 9 | Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location显示文摘The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques,such as the internet of things(IoT)and mobile crowdsensing(MCS).The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively,with each mobile user completing much simpler micro-tasks.This paper discusses the task assignment problem in mobile crowdsensing,which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals.The goal is to minimize aggregate sensing time for mobile users,which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task quality.This paper introduces a two-phase task assignment framework called location time-based algorithm(LTBA).LTBA is a framework that enhances task assignment in MCS,whereas assigning tasks requires overlapping time intervals between tasks and mobile users’tasks and the location of tasks and mobile users’paths.The process of assigning the nearest task to the mobile user’s current path depends on the ant colony optimization algorithm(ACO)and Euclidean distance.LTBA combines two algorithms:(1)greedy online allocation algorithm and(2)bio-inspired traveldistance-balance-based algorithm(B-DBA).The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user.B-DBA was location-based and worked on maximizing total task quality.The results demonstrate that the average task quality is 0.8158,0.7093,and 0.7733 for LTBA,B-DBA,and greedy,respectively.The sensing time was reduced to 644,1782,and 685 time units for LTBA,B-DBA,and greedy,respectively.Combining the algorithms improves task assignment in MCS for both total task quality and sensing time.The results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time,and the greedy algorithm follows it then B-DBA. | Rasha Sleem Nagham Mekky Shaker El-Sappagh Louai Alarabi Noha AHikal Mohammed Elmogy | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 10 | Application of machine learning algorithms to predict tubing pressure in intermittent gas lift wells显示文摘Tubing pressure at gas injection depth in intermittent wells is one of the most critical parameters for production engineers to evaluate the performance of the system.However,monitoring of the tubing pressure is not usually carried out in real time.It has been realized that the generally used correlations are not effective enough due to complexity of the intermittent process which involve many parameters and assumptions to develop such equations.The focus of this study is to utilize machine learning(ML)algorithms to develop a model that can accurately predict tubing pressure in artificial intermittent gas lift wells.intelligent algorithms built on the field data provide a solution that is easy to use and universally applicable to the complex problems.Various non-linear regression ML methods are employed in this study,namely,Decision Tree-regression(DT),Random Forest-regression(RF)and K Nearest Neighbors-regression(KNN).All the tubing pressures obtained from ML models were compared with the actual values to ensure the effectiveness of the work.The developed models show that it can predict the pressure with more than 99.9%accuracy.This is an interesting result,as such outcome accuracy has not been reported usually in the open literature. | Nagham Amer Sami | 2022 | Petroleum Research2022，7，2： | 0 |
| 11 | Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm显示文摘Current traffic signals in Jordan suffer from severe congestion due to many factors,such as the considerable increase in the number of vehicles and the use of fixed timers,which still control existing traffic signals.This condition affects travel demand on the streets of Jordan.This study aims to improve an intelligent road traffic management system(IRTMS)derived from the human community-based genetic algorithm(HCBGA)to mitigate traffic signal congestion in Amman,Jordan’s capital city.The parameters considered for IRTMS are total time and waiting time,and fixed timers are still used for control.By contrast,the enhanced system,called enhanced-IRTMS(E-IRTMS),considers additional important parameters,namely,the speed performance index(SPI),speed reduction index(SRI),road congestion index(R i),and congestion period,to enhance IRTMS decision.A significant reduction in congestion period was measured using E-IRTMS,improving by 13% compared with that measured using IRTMS.Meanwhile,the IRTMS result surpasses that of the current traffic signal system by approximately 83%.This finding demonstrates that the E-IRTMS based on HCBGA and with unfixed timers achieves shorter congestion period in terms of SPI,SRI,and R_(i) compared with IRTMS. | Nagham A.Al-Madi Adnan A.Hnaif | 2022 | Computer Systems Science & Engineering2022，40，1： | 0 |
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