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Application of ML&AI to model petrophysical and geomechanical properties of shale reservoirs e A systematic literature review

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Petroleum 2022年第2期8卷 158-166页

作者： Fahad I.syed Abdulla Alshamsi Amirmasoud K.Dahaghi neghabhan s The University of Kansas School of EngineeringChemical and Petroleum Engineering1450 Jayhawk Blvd.Lawrence66045USA

Extensive reviews and cross-comparison studies are essential to analyze the emerging developments in a specific field of *** the past decade,hydrocarbon exploration and exploitation from the shale reservoirs have been the most discussed and researched area around the globe.A dramatic development in shale formations became the game-changer,especially in the *** the other hand,Artificial Intelligence(AI)and Machine Learning(ML)are playing an important role in the rapid development in all the industries through automating most of the routine *** oil industry is also getting equal benefits of AI and ML for the reservoir development planning and its operational accuracy through a series of automated *** the field development,computerized static and dynamic simulation models are generated based on several Petrophysical and Geomechanical properties gathered through different resources that are quite time-taking and *** and ML have made this process much easier,faster,and economical by means of learning through uncounted experiences from already explored and developed reservoirs,their rock properties,and the crossponding fluid flow behavior under different circumstances and hence,predicts *** article provides a comprehensive literature review in the area of AI and ML applications to model Petrophysical and Geomechanical properties using different approaches and ***,a systematic publication counts in each field of subject study per year in different literature databases are presented that infect reflects the trending interest in this ***,multiple AI and ML techniques are discussed in detail which have been tested in the last decade for the sake of achieving higher accuracy in Petrophysical and Geo-Mechanical simulation models.

关键词： AI ML Petrophysics GeoMechanics

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Artificial lift system optimization using machine learning applications

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Petroleum 2022年第2期8卷 219-226页

作者： Fahad I.syed Mohammed Alshamsi Amirmasoud K.Dahaghi s.neghabhan University of Kansas&ADNOC Offshore Dept.of Chemical and Petroleum E…Lawrence66045USA

Currently,in the oil industry,artificial lift optimization(ALO)systems are dealing with different applications including well monitor and control,reservoir management,production optimization,predictive maintenance,artificial lift,and flow assurance,multiphase pumping systems,*** scope of this article is to provide a detailed analysis of ALO and predictive pump maintenance applications using machine learning(ML)and artificial intelligence(AI).The oil and gas industry has experienced a lot of improvements that have impacted the businesses and economies associated with the market in recent *** such as unplanned shutdown time and failure of equipment cause a huge impact on many corporations especially with the current fluctuations in hydrocarbon ***,advanced modern technologies such as real-time analysis and predictive maintenance are designed to drive ALO *** paper covers several applications and techniques in which ML and AI have been applied to optimize hydrocarbon withdrawal from potentially depleted reservoirs that require some external supports to uplift the reservoir fluid from sub surface to surface using artificial lift *** a nutshell,the applications of AI and ML for the artificial lift selection,their predictive maintenance,equipment malfunctioning detection,*** a self-trained system are the main topics of this *** reviewing each of these techniques,the workflow is also explained along with the effectiveness of utilizing each application to the current operations.

关键词： Artificial lift optimization Predictive maintenance

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Numerical Validation of Asphaltene Precipitation and Deposition during CO2 miscible flooding

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Petroleum Research 2020年第3期5卷 235-243页

作者： Fahad I.syed shahin neghabhan Arsalan Zolfaghari Amirmasoud K.Dahaghi University of Kansas USA ADNOC Offshore UAE

Asphaltene precipitation,flocculation,and deposition can significantly reduce oil production by impacting wellbores,flowlines,and more importantly,formations’pore space around the *** alteration in the temperature,pressure and fluid composition can trigger asphaltene *** ability to predict the occurrence and magnitude of the asphaltene deposition is a major step for flow *** accurate prediction of the deposition envelope enables the operator to systematically categorize different cases based on their impact on the *** critical knowledge can be used to predict the occurrence and magnitude of asphaltene deposition,which could potentially save the expense of installing unnecessary equipment and injecting chemical inhibitors when they are not *** asphaltene-related flow assurance issues requires robust physically-based modeling capabilities for capturing the asphaltene’s deposition tendencies as a function of the prevailing field’s operating *** available simulators are found to be useful for predicting asphaltene’s phase behavior,precipitation tendency,and instability curves,they often overlook important physical characteristics of the *** properties may have a detrimental role in obtaining a realistic representation of the asphaltene deposition *** this paper,the experimental and the numerical investigations are combined to present a comprehensive methodology for studying the thermodynamics of asphaltene precipitation and deposition.A wide range of pressures and CO2 concentrations are covered that are relevant to actual CO2 flooding in a Middle East oil *** do so,a series of lab experiments including routine and special PVT analyses where the Asphaltene Onset Pressure(AOP)and saturation Pressures(Psat)were measured for different mixtures of CO2 and the reservoir *** of 50 mol%CO2 concentration mixture was tested to measure the AOP and *** amount of asphaltene

关键词： Asphaltene AOP AOC EOs Tuning Asphaltic fluid&CO2 field problems

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