Artificial lift system optimization using machine learning applications

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,etc.The 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 times.Issues such as unplanned shutdown time and failure of equipment cause a huge impact on many corporations especially with the current fluctuations in hydrocarbon prices.Similarly,advanced modern technologies such as real-time analysis and predictive maintenance are designed to drive ALO systems.This 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 system.In a nutshell,the applications of AI and ML for the artificial lift selection,their predictive maintenance,equipment malfunctioning detection,etc.using a self-trained system are the main topics of this paper.While reviewing each of these techniques,the workflow is also explained along with the effectiveness of utilizing each application to the current operations.

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

Extensive reviews and cross-comparison studies are essential to analyze the emerging developments in a specific field of research.In 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 US.On 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 operations.The 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 systems.For 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 expensive.AI 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 accordingly.This article provides a comprehensive literature review in the area of AI and ML applications to model Petrophysical and Geomechanical properties using different approaches and algorithms.Also,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 subject.Finally,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.

Smart shale gas production performance analysis using machine learning applications

With the advancement of technology and innovation in the oil and gas industry,the production of liquid and gaseous hydrocarbon from conventional and unconventional resources has seen exponential growth.Recently,the USA and other oil giants have shifted their paradigm from conventional to unconventional resources of exploration and production of hydrocarbon.However,there is still a perpetual force that exists to develop and devise new innovative approaches and methodologies for the exploration,extraction efficiencies,and production performance of hydrocarbons.To better evaluate the impact of well attributes,reservoir characteristics and production behavior of well machine learning and artificial intelligence-based models have been developed by researchers that with the help of simulation and modeling provide us the true picture of reservoir performance without exploring and investing billions of dollars.This review paper encompasses the literature published in the recent years and narrated the recent development made by researchers especially in the field of production performance estimation of shale gas by developing machine learning-based models.More specifically,this paper deals with the major shale gas reservoir of North America including Marcellus shale,Eagle Ford shale,and Bakken Shale.Additionally,equations,input parameters,and formations that are considered key parameters for the development of the smart shale gas models are also discussed in this manuscript.In addition,the methodology comparison of different machine learning algorithms including their limitations and advantages are also presented.

Numerical Validation of Asphaltene Precipitation and Deposition during CO2 miscible flooding

Asphaltene precipitation,flocculation,and deposition can significantly reduce oil production by impacting wellbores,flowlines,and more importantly,formations’pore space around the well.Any alteration in the temperature,pressure and fluid composition can trigger asphaltene deposition.The ability to predict the occurrence and magnitude of the asphaltene deposition is a major step for flow assurance.An accurate prediction of the deposition envelope enables the operator to systematically categorize different cases based on their impact on the production.This 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 needed.Predicting 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 conditions.Although 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 asphaltenes.These properties may have a detrimental role in obtaining a realistic representation of the asphaltene deposition behavior.In 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 reservoir.To 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 oil.Maximum of 50 mol%CO2 concentration mixture was tested to measure the AOP and Psat.The amount of asphaltene precipitation was found between 0.25 and 4 wt%for the mixtures of 10e50 mol%CO2 concentration.Furthermore,detailed recommendations are presented in this paper to tune an EOS for running compositional simulations when unstable asphaltene is reported based on the lab experimental measurements.