In the reservoir simulations, the geomechanical effects are usually taken into account to describe the porosity and the permeability variations. In this paper, we present a new method, patented by authors, which allows to model the geomechanical effects also on the well productivity index. The Steam Assisted Gravity Drainage (SAGD) method is widely used for the heavy oil production. A very high variation in pressure and temperature play a significant role on the petrophysical properties and may impact the productivity estimation. In this paper we develop a new simplified geomechanical model in order to account for the thermal and pressure effects on the porosity, permeability and the productivity index during the reservoir simulation. At the current state, these dependencies are defined using semi-analytical relationships. The model is applied to a meandering fluvial reservoir based on 3D outcrop observations. The productivity is found underestimated if the pressure and temperature effects on the petrophysical properties are ignored in the reservoir simulation. Moreover, this study shows an important impact of thermal effects on the productivity estimation. The results of this work show that it is essential to properly take into account the geomechanical effects on the petrophysical properties and also on the productivity index for a better productivity estimation.
{"title":"Impact of geomechanical effects during SAGD process in a meander belt","authors":"I. Malinouskaya, C. Preux, N. Guy, G. Etienne","doi":"10.2516/OGST/2018011","DOIUrl":"https://doi.org/10.2516/OGST/2018011","url":null,"abstract":"In the reservoir simulations, the geomechanical effects are usually taken into account to describe the porosity and the permeability variations. In this paper, we present a new method, patented by authors, which allows to model the geomechanical effects also on the well productivity index. The Steam Assisted Gravity Drainage (SAGD) method is widely used for the heavy oil production. A very high variation in pressure and temperature play a significant role on the petrophysical properties and may impact the productivity estimation. In this paper we develop a new simplified geomechanical model in order to account for the thermal and pressure effects on the porosity, permeability and the productivity index during the reservoir simulation. At the current state, these dependencies are defined using semi-analytical relationships. The model is applied to a meandering fluvial reservoir based on 3D outcrop observations. The productivity is found underestimated if the pressure and temperature effects on the petrophysical properties are ignored in the reservoir simulation. Moreover, this study shows an important impact of thermal effects on the productivity estimation. The results of this work show that it is essential to properly take into account the geomechanical effects on the petrophysical properties and also on the productivity index for a better productivity estimation.","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75694946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Rostami, Mahdi Kalantari-Meybodi, M. Karimi, A. Tatar, A. Mohammadi
Polymers applications have been progressively increased in sciences and engineering including chemistry, pharmacology science, and chemical and petroleum engineering due to their attractive properties. Amongst the all types of polymers, partially Hydrolyzed Polyacrylamide (HPAM) is one of the widely used polymers especially in chemistry, and chemical and petroleum engineering. Capability of solution viscosity increment of HPAM is the key parameter in its successful applications; thus, the viscosity of HPAM solution must be determined in any study. Experimental measurement of HPAM solution viscosity is time-consuming and can be expensive for elevated conditions of temperatures and pressures, which is not desirable for engineering computations. In this communication, Multilayer Perceptron neural network (MLP), Least Squares Support Vector Machine approach optimized with Coupled Simulated Annealing (CSA-LSSVM), Radial Basis Function neural network optimized with Genetic Algorithm (GA-RBF), Adaptive Neuro Fuzzy Inference System coupled with Conjugate Hybrid Particle Swarm Optimization (CHPSO-ANFIS) approach, and Committee Machine Intelligent System (CMIS) were used to model the viscosity of HPAM solutions. Then, the accuracy and reliability of the developed models in this study were investigated through graphical and statistical analyses, trend prediction capability, outlier detection, and sensitivity analysis. As a result, it has been found that the MLP and CMIS models give the most reliable results with determination coefficients (R 2 ) more than 0.98 and Average Absolute Relative Deviations (AARD) less than 4.0%. Finally, the suggested models in this study can be applied for efficient estimation of aqueous solutions of HPAM polymer in simulation of polymer flooding into oil reservoirs.
{"title":"Efficient estimation of hydrolyzed polyacrylamide (HPAM) solution viscosity for enhanced oil recovery process by polymer flooding","authors":"A. Rostami, Mahdi Kalantari-Meybodi, M. Karimi, A. Tatar, A. Mohammadi","doi":"10.2516/OGST/2018006","DOIUrl":"https://doi.org/10.2516/OGST/2018006","url":null,"abstract":"Polymers applications have been progressively increased in sciences and engineering including chemistry, pharmacology science, and chemical and petroleum engineering due to their attractive properties. Amongst the all types of polymers, partially Hydrolyzed Polyacrylamide (HPAM) is one of the widely used polymers especially in chemistry, and chemical and petroleum engineering. Capability of solution viscosity increment of HPAM is the key parameter in its successful applications; thus, the viscosity of HPAM solution must be determined in any study. Experimental measurement of HPAM solution viscosity is time-consuming and can be expensive for elevated conditions of temperatures and pressures, which is not desirable for engineering computations. In this communication, Multilayer Perceptron neural network (MLP), Least Squares Support Vector Machine approach optimized with Coupled Simulated Annealing (CSA-LSSVM), Radial Basis Function neural network optimized with Genetic Algorithm (GA-RBF), Adaptive Neuro Fuzzy Inference System coupled with Conjugate Hybrid Particle Swarm Optimization (CHPSO-ANFIS) approach, and Committee Machine Intelligent System (CMIS) were used to model the viscosity of HPAM solutions. Then, the accuracy and reliability of the developed models in this study were investigated through graphical and statistical analyses, trend prediction capability, outlier detection, and sensitivity analysis. As a result, it has been found that the MLP and CMIS models give the most reliable results with determination coefficients (R 2 ) more than 0.98 and Average Absolute Relative Deviations (AARD) less than 4.0%. Finally, the suggested models in this study can be applied for efficient estimation of aqueous solutions of HPAM polymer in simulation of polymer flooding into oil reservoirs.","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82206406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Super-giant carbonate fields, such as Ghawar, in Saudi Arabia, and Lula, at the Brazilian pre-salt, show highly heterogeneous behavior that is linked to high permeability intervals in thin layers. This article applies Local Grid Refinements (LGR) integrated with upscaling procedures to improve the representation of highly laminated reservoirs in flow simulation by preserving the static properties and dynamic trends from geological model. This work was developed in five main steps: (1) define a conventional coarse grid, (2) define LGR in the conventional coarse grid according to super-k and well locations, (3) apply an upscaling procedure for all scenarios, (4) define LGR directly in the simulation model, without integrate geological trends in LGR and (5) compare the dynamic response for all cases. To check results and compare upscaling matches, was used the benchmark model UNISIM-II-R, a refined model based on a combination of Brazilian Pre-salt and Ghawar field information. The main results show that the upscaling of geological models for coarse grid with LGR in highly permeable thin layers provides a close dynamic representation of geological characterization compared to conventional coarse grid and LGR only near-wells. Pseudo-relative permeability curves should be considered for (a) conventional coarse grid or (b) LGR scenarios under dual-medium flow simulations as the upscaling of discrete fracture networks and dual-medium flow models presents several limitations. The conventional approach of LGR directly in simulation model, presents worse results than LGR integrated with upscaling procedures as the extrapolation of dynamic properties to the coarse block mismatch the dynamic behavior from geological characterization. This work suggests further improvements for results for upscaling procedures that mask the flow behavior in highly laminated reservoirs.
{"title":"Flow Simulation Using Local Grid Refinements to Model Laminated Reservoirs","authors":"M. Correia, C. Maschio, D. Schiozer","doi":"10.2516/OGST/2017043","DOIUrl":"https://doi.org/10.2516/OGST/2017043","url":null,"abstract":"Super-giant carbonate fields, such as Ghawar, in Saudi Arabia, and Lula, at the Brazilian pre-salt, show highly heterogeneous behavior that is linked to high permeability intervals in thin layers. This article applies Local Grid Refinements (LGR) integrated with upscaling procedures to improve the representation of highly laminated reservoirs in flow simulation by preserving the static properties and dynamic trends from geological model. This work was developed in five main steps: (1) define a conventional coarse grid, (2) define LGR in the conventional coarse grid according to super-k and well locations, (3) apply an upscaling procedure for all scenarios, (4) define LGR directly in the simulation model, without integrate geological trends in LGR and (5) compare the dynamic response for all cases. To check results and compare upscaling matches, was used the benchmark model UNISIM-II-R, a refined model based on a combination of Brazilian Pre-salt and Ghawar field information. The main results show that the upscaling of geological models for coarse grid with LGR in highly permeable thin layers provides a close dynamic representation of geological characterization compared to conventional coarse grid and LGR only near-wells. Pseudo-relative permeability curves should be considered for (a) conventional coarse grid or (b) LGR scenarios under dual-medium flow simulations as the upscaling of discrete fracture networks and dual-medium flow models presents several limitations. The conventional approach of LGR directly in simulation model, presents worse results than LGR integrated with upscaling procedures as the extrapolation of dynamic properties to the coarse block mismatch the dynamic behavior from geological characterization. This work suggests further improvements for results for upscaling procedures that mask the flow behavior in highly laminated reservoirs.","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79872498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a control-oriented thermodynamic model capable of predicting nitrogen oxides (NOx ) emissions in diesel engines. It is derived from zero-dimensional combustion model using in-cylinder pressure as the input. The methodology is based on a two-zone thermodynamic model which divides the combustion chamber into a burned and unburned gas zone. The original contribution of proposed method arises from: (1) application of a detailed two-zone modeling framework, developed in a way that the thermodynamic equations could be solved in a closed form without iterative procedure, which provides the basis for achieving high level of predictiveness, on the level of real-time capable models and (2) introduction of relative air-fuel ratio during combustion as a main and physically motivated calibration parameter of the NOx model. The model was calibrated and validated using data sets recorded in two different direct injection diesel engines, i.e . a light and a heavy-duty engine. The model is suitable for real-time applications since it takes less than a cycle to complete the entire closed cycle thermodynamic calculation including NOx prediction, which opens the possibility of integration in the engine control unit for closed-loop or feed-forward control.
{"title":"Real-time capable virtual NOx sensor for diesel engines based on a two-Zone thermodynamic model","authors":"R. Vihar, Urban Žvar Baškovič, T. Katrašnik","doi":"10.2516/OGST/2018005","DOIUrl":"https://doi.org/10.2516/OGST/2018005","url":null,"abstract":"This paper presents a control-oriented thermodynamic model capable of predicting nitrogen oxides (NOx ) emissions in diesel engines. It is derived from zero-dimensional combustion model using in-cylinder pressure as the input. The methodology is based on a two-zone thermodynamic model which divides the combustion chamber into a burned and unburned gas zone. The original contribution of proposed method arises from: (1) application of a detailed two-zone modeling framework, developed in a way that the thermodynamic equations could be solved in a closed form without iterative procedure, which provides the basis for achieving high level of predictiveness, on the level of real-time capable models and (2) introduction of relative air-fuel ratio during combustion as a main and physically motivated calibration parameter of the NOx model. The model was calibrated and validated using data sets recorded in two different direct injection diesel engines, i.e . a light and a heavy-duty engine. The model is suitable for real-time applications since it takes less than a cycle to complete the entire closed cycle thermodynamic calculation including NOx prediction, which opens the possibility of integration in the engine control unit for closed-loop or feed-forward control.","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85623088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Application of big data analytics in reservoir engineering has gained wide attention in recent years. However, designing practical data-driven models for correlating petrophysical measurements and Steam-Assisted Gravity Drainage (SAGD) production profiles using actual field data remains difficult. Parameterization of the complex reservoir heterogeneities in these reservoirs is not trivial. In this study, a set of attributes pertinent to characterizing stochastic distributions of shales and lean zones is formulated and used for correlating against a number of production performance measures. A comprehensive investigation of the heterogeneous distribution (continuity, size, proportions, permeability, location, orientation and saturation) of shale barriers and lean zones is presented. First, a series of two-dimensional SAGD models based on typical Athabasca oil reservoir properties and operating conditions are constructed. Geostatistical techniques are applied to stochastically model shale barriers, which are imbedded in a region of degraded rock properties referred to as Low-Quality Sand or LQS, among a background of clean sand. Parameters including correlation lengths, orientation, proportions and permeability anisotropy of the different rock facies are varied. Within each facies, spatial variations in water saturation are modeled probabilistically. In contrast to many previous simulation studies, representative multiphase flow functions and capillarity models are assigned in accordance to individual facies. A set of input attributes based on facies proportions and dimensionless correlation lengths are formulated. Next, to facilitate the assessment of different scenarios, production performance is quantified by numerous dimensionless output attributes defined from recovery factor and steam-to-oil ratio profiles. An additional dimensionless indicator is implemented to capture the production time during which the instantaneous steam-to-oil ratio has exceeded a particular economic threshold. Finally, results of the sensitivity analysis are employed as training and testing datasets in a series of neural network models to correlate the pertinent system attributes and the production performance measures. These models are also used to assess the consequences of ignoring lateral variation of heterogeneities when extracting petrophysical (log) data from vertical delineation wells alone. An important contribution of this work is that it proposes a set of input attributes for correlating reservoir heterogeneity introduced by shale barriers and lean zones to SAGD production performance. It demonstrates that these input attributes, which can be extracted from petrophysical logs, are highly correlated with the ensuing recovery response and heat loss. This work also exemplifies the feasibility and utility of data-driven models in correlating SAGD performance. Furthermore, the proposed set of system variables and modeling approach can be applied directly in field
{"title":"Correlating Stochastically Distributed Reservoir Heterogeneities with Steam-Assisted Gravity Drainage Production","authors":"Cui Wang, Zhiwei Ma, J. Leung, S. Zanon","doi":"10.2516/OGST/2017042","DOIUrl":"https://doi.org/10.2516/OGST/2017042","url":null,"abstract":"Application of big data analytics in reservoir engineering has gained wide attention in recent years. However, designing practical data-driven models for correlating petrophysical measurements and Steam-Assisted Gravity Drainage (SAGD) production profiles using actual field data remains difficult. Parameterization of the complex reservoir heterogeneities in these reservoirs is not trivial. In this study, a set of attributes pertinent to characterizing stochastic distributions of shales and lean zones is formulated and used for correlating against a number of production performance measures. A comprehensive investigation of the heterogeneous distribution (continuity, size, proportions, permeability, location, orientation and saturation) of shale barriers and lean zones is presented. First, a series of two-dimensional SAGD models based on typical Athabasca oil reservoir properties and operating conditions are constructed. Geostatistical techniques are applied to stochastically model shale barriers, which are imbedded in a region of degraded rock properties referred to as Low-Quality Sand or LQS, among a background of clean sand. Parameters including correlation lengths, orientation, proportions and permeability anisotropy of the different rock facies are varied. Within each facies, spatial variations in water saturation are modeled probabilistically. In contrast to many previous simulation studies, representative multiphase flow functions and capillarity models are assigned in accordance to individual facies. A set of input attributes based on facies proportions and dimensionless correlation lengths are formulated. Next, to facilitate the assessment of different scenarios, production performance is quantified by numerous dimensionless output attributes defined from recovery factor and steam-to-oil ratio profiles. An additional dimensionless indicator is implemented to capture the production time during which the instantaneous steam-to-oil ratio has exceeded a particular economic threshold. Finally, results of the sensitivity analysis are employed as training and testing datasets in a series of neural network models to correlate the pertinent system attributes and the production performance measures. These models are also used to assess the consequences of ignoring lateral variation of heterogeneities when extracting petrophysical (log) data from vertical delineation wells alone. An important contribution of this work is that it proposes a set of input attributes for correlating reservoir heterogeneity introduced by shale barriers and lean zones to SAGD production performance. It demonstrates that these input attributes, which can be extracted from petrophysical logs, are highly correlated with the ensuing recovery response and heat loss. This work also exemplifies the feasibility and utility of data-driven models in correlating SAGD performance. Furthermore, the proposed set of system variables and modeling approach can be applied directly in field","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80240487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zeolite beta nanoparticles were used as a new asphaltene adsorbent for reducing asphaltene deposition during fluid injection into a silica sand pack. At first, the asphaltene adsorption efficiency and capacity of zeolite beta nanoparticles were determined by UV-Vis spectrophotometer. It was found that the proper concentration of nanoparticles for asphaltene adsorption was 10 g/L and the maximum asphaltene adsorption onto zeolite beta was 1.98 mg/m2 . Second, two dynamic experiments including co-injection of crude oil and n-heptane (as an asphaltene precipitant) with and without use of zeolite beta nanoparticles in the sand pack was carried out. The results showed that the use of zeolite beta nanoparticles increased the permeability ratio and outlet fluid's asphaltene content about 22% and 40% compared to without use of nanoparticles, respectively. Moreover, a model based on monolayer asphaltene adsorption onto nanoparticles and asphaltene deposition mechanisms including surface deposition, entrainment and pore throat plugging was developed to determine formation damage during co-injection of crude oil and n-heptane into the sand pack. The proposed model presented good prediction of permeability and porosity ratios with AAD% of 1.07 and 0.07, respectively.
{"title":"Investigation of Asphaltene Adsorption onto Zeolite Beta Nanoparticles to Reduce Asphaltene Deposition in a Silica Sand Pack","authors":"Sepideh Kashefi, M. Lotfollahi, A. Shahrabadi","doi":"10.2516/OGST/2017038","DOIUrl":"https://doi.org/10.2516/OGST/2017038","url":null,"abstract":"Zeolite beta nanoparticles were used as a new asphaltene adsorbent for reducing asphaltene deposition during fluid injection into a silica sand pack. At first, the asphaltene adsorption efficiency and capacity of zeolite beta nanoparticles were determined by UV-Vis spectrophotometer. It was found that the proper concentration of nanoparticles for asphaltene adsorption was 10 g/L and the maximum asphaltene adsorption onto zeolite beta was 1.98 mg/m2 . Second, two dynamic experiments including co-injection of crude oil and n-heptane (as an asphaltene precipitant) with and without use of zeolite beta nanoparticles in the sand pack was carried out. The results showed that the use of zeolite beta nanoparticles increased the permeability ratio and outlet fluid's asphaltene content about 22% and 40% compared to without use of nanoparticles, respectively. Moreover, a model based on monolayer asphaltene adsorption onto nanoparticles and asphaltene deposition mechanisms including surface deposition, entrainment and pore throat plugging was developed to determine formation damage during co-injection of crude oil and n-heptane into the sand pack. The proposed model presented good prediction of permeability and porosity ratios with AAD% of 1.07 and 0.07, respectively.","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83364483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a promising solution to the reduction of fuel consumption and CO2 emissions in road transport sector, hybrid electric powertrains are confronted with complex control techniques for the evaluation of the minimal fuel consumption, particularly the excessively long computation time of the design-parameter optimization in the powertrain's early design stage. In this work, a novel and simple GRaphical-Analysis-Based method of fuel Energy Consumption Optimization (GRAB-ECO) is developed to estimate the minimal fuel consumption for parallel hybrid electric powertrains in light- and heavy-duty application. Based on the power ratio between powertrain's power demand and the most efficient engine power, GRAB-ECO maximizes the average operating efficiency of the internal combustion engine by shifting operating points to the most efficient conditions, or by eliminating the engine operation from poorly efficient operating points to pure electric vehicle operation. A turning point is found to meet the requirement of the final state of energy of the battery, which is charge-sustaining mode in this study. The GRAB-ECO was tested with both light- and heavy-duty parallel hybrid electric vehicles, and validated in terms of the minimal fuel consumption and the computation time. Results show that GRAB-ECO accurately approximates the minimal fuel consumption with less than 6% of errors for both light- and heavy-duty parallel hybrid electric powertrains. Meanwhile, GRAB-ECO reduces computation time by orders of magnitude compared with PMP-based (Pontryagin's Minimum Principle) approaches.
{"title":"GRAB-ECO for Minimal Fuel Consumption Estimation of Parallel Hybrid Electric Vehicles","authors":"Jianning Zhao, A. Sciarretta, L. Eriksson","doi":"10.2516/OGST/2017035","DOIUrl":"https://doi.org/10.2516/OGST/2017035","url":null,"abstract":"As a promising solution to the reduction of fuel consumption and CO2 emissions in road transport sector, hybrid electric powertrains are confronted with complex control techniques for the evaluation of the minimal fuel consumption, particularly the excessively long computation time of the design-parameter optimization in the powertrain's early design stage. In this work, a novel and simple GRaphical-Analysis-Based method of fuel Energy Consumption Optimization (GRAB-ECO) is developed to estimate the minimal fuel consumption for parallel hybrid electric powertrains in light- and heavy-duty application. Based on the power ratio between powertrain's power demand and the most efficient engine power, GRAB-ECO maximizes the average operating efficiency of the internal combustion engine by shifting operating points to the most efficient conditions, or by eliminating the engine operation from poorly efficient operating points to pure electric vehicle operation. A turning point is found to meet the requirement of the final state of energy of the battery, which is charge-sustaining mode in this study. The GRAB-ECO was tested with both light- and heavy-duty parallel hybrid electric vehicles, and validated in terms of the minimal fuel consumption and the computation time. Results show that GRAB-ECO accurately approximates the minimal fuel consumption with less than 6% of errors for both light- and heavy-duty parallel hybrid electric powertrains. Meanwhile, GRAB-ECO reduces computation time by orders of magnitude compared with PMP-based (Pontryagin's Minimum Principle) approaches.","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90959363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Esra Yildar, G. Kuenne, Chao He, R. Schießl, Marc-Sebastian, Benzinger, Marius Neurohr, F. Mare, A. Sadiki, Johannes, Janicka
This work applies Large Eddy Simulation (LES) to the combustion process within a CAI engine. The chemical reaction is treated with a pre-tabulation approach based on homogeneous reactor simulations. At this juncture, a five-dimensional chemistry database is employed where the thermo-chemical properties are a function of the unburnt gas temperature, the air–fuel ratio, the exhaust gas ratio, the pressure, and the reaction progress variable. Statistical quantities are gathered for 20 simulated cycles and the averaged pressure curves get compared to measurements. The simulation data are then used to provide further insight into the auto-ignition process. It will be shown how thermo-chemical states are distributed within the cylinder and how the ignition quality depends on them. A statistical analysis is conducted to identify manifolds in the multi-dimensional scalar space along which the conditions leading to ignition evolve. Furthermore the strong influence in between consecutive cycles caused by the exhaust gas is investigated to identify the mechanism of cycle-to-cycle variations.
{"title":"Understanding the Influences of Thermal and Mixture Inhomogeneities on the Auto-Ignition Process in a Controlled Auto-Ignition (CAI) Engine Using LES","authors":"Esra Yildar, G. Kuenne, Chao He, R. Schießl, Marc-Sebastian, Benzinger, Marius Neurohr, F. Mare, A. Sadiki, Johannes, Janicka","doi":"10.2516/OGST/2017025","DOIUrl":"https://doi.org/10.2516/OGST/2017025","url":null,"abstract":"This work applies Large Eddy Simulation (LES) to the combustion process within a CAI engine. The chemical reaction is treated with a pre-tabulation approach based on homogeneous reactor simulations. At this juncture, a five-dimensional chemistry database is employed where the thermo-chemical properties are a function of the unburnt gas temperature, the air–fuel ratio, the exhaust gas ratio, the pressure, and the reaction progress variable. Statistical quantities are gathered for 20 simulated cycles and the averaged pressure curves get compared to measurements. The simulation data are then used to provide further insight into the auto-ignition process. It will be shown how thermo-chemical states are distributed within the cylinder and how the ignition quality depends on them. A statistical analysis is conducted to identify manifolds in the multi-dimensional scalar space along which the conditions leading to ignition evolve. Furthermore the strong influence in between consecutive cycles caused by the exhaust gas is investigated to identify the mechanism of cycle-to-cycle variations.","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80133019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yajuvendra Shekhawat, D. Haworth, Alessandro d’Adamo, F. Berni, S. Fontanesi, Philipp Schiffmann, D. Reuss, V. Sick
An integrated experimental and Large-Eddy Simulation (LES) study is presented for homogeneous premixed combustion in a spark-ignition engine. The engine is a single-cylinder two-valve optical research engine with transparent liner and piston: the Transparent Combustion Chamber (TCC) engine. This is a relatively simple, open engine configuration that can be used for LES model development and validation by other research groups. Pressure-based combustion analysis, optical diagnostics and LES have been combined to generate new physical insight into the early stages of combustion. The emphasis has been on developing strategies for making quantitative comparisons between high-speed/high-resolution optical diagnostics and LES using common metrics for both the experiments and the simulations, and focusing on the important early flame development period. Results from two different LES turbulent combustion models are presented, using the same numerical methods and computational mesh. Both models yield Cycle-to-Cycle Variations (CCV) in combustion that are higher than what is observed in the experiments. The results reveal strengths and limitations of the experimental diagnostics and the LES models, and suggest directions for future diagnostic and simulation efforts. In particular, it has been observed that flame development between the times corresponding to the laminar-to-turbulent transition and 1% mass-burned fraction are especially important in establishing the subsequent combustion event for each cycle. This suggests a range of temporal and spatial scales over which future experimental and simulation efforts should focus.
{"title":"An Experimental and Simulation Study of Early Flame Development in a Homogeneous-charge Spark-Ignition Engine","authors":"Yajuvendra Shekhawat, D. Haworth, Alessandro d’Adamo, F. Berni, S. Fontanesi, Philipp Schiffmann, D. Reuss, V. Sick","doi":"10.2516/OGST/2017028","DOIUrl":"https://doi.org/10.2516/OGST/2017028","url":null,"abstract":"An integrated experimental and Large-Eddy Simulation (LES) study is presented for homogeneous premixed combustion in a spark-ignition engine. The engine is a single-cylinder two-valve optical research engine with transparent liner and piston: the Transparent Combustion Chamber (TCC) engine. This is a relatively simple, open engine configuration that can be used for LES model development and validation by other research groups. Pressure-based combustion analysis, optical diagnostics and LES have been combined to generate new physical insight into the early stages of combustion. The emphasis has been on developing strategies for making quantitative comparisons between high-speed/high-resolution optical diagnostics and LES using common metrics for both the experiments and the simulations, and focusing on the important early flame development period. Results from two different LES turbulent combustion models are presented, using the same numerical methods and computational mesh. Both models yield Cycle-to-Cycle Variations (CCV) in combustion that are higher than what is observed in the experiments. The results reveal strengths and limitations of the experimental diagnostics and the LES models, and suggest directions for future diagnostic and simulation efforts. In particular, it has been observed that flame development between the times corresponding to the laminar-to-turbulent transition and 1% mass-burned fraction are especially important in establishing the subsequent combustion event for each cycle. This suggests a range of temporal and spatial scales over which future experimental and simulation efforts should focus.","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76445966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oil and gas exploration and production firms have return profiles that are not easily explained by current financial theory – the variation in their market returns is non-Gaussian. In this paper, the nature and underlying reason for these significant deviations from expected behavior are considered. Understanding these differences in financial market behavior is important for a wide range of reasons, including: assessing investments, investor relations, decisions to raise capital, assessment of firm and management performance. We show that using a “thicker tailed” mixture of two normal distributions offers a significantly more accurate model than the traditionally Gaussian approach in describing the behavior of the value of oil and gas firms. This mixture of normal distribution is also more effective in bridging the gap between management theory and practice without the need to introduce complex time-sensitive GARCH and/or jump diffusion dynamics. The mixture distribution is consistent with ambidexterity theory that suggests firms operate in two distinct states driven by the primary focus of the firm: an exploration state with high uncertainty and, an exploitation (or production) state with lower uncertainty. The findings have direct implications on improving the accuracy of real option pricing techniques and futures analysis of risk management. Traditional options pricing models assume that commercial returns from these assets are described by a normal random walk. However, a normal random walk model discounts the possibility of large changes to the marketplace from events such as the discovery of important reserves or the introduction of new technology. The mixture distribution proves to be well suited to inherently describe the unusually large risks and opportunities associated with oil and gas production and exploration. A significance testing study of 554 oil and gas exploration and production firms empirically supports using a mixture distribution grounded in ambidexterity theory to describe the value fluctuations for these firms.
{"title":"Linking the Value Assessment of Oil and Gas Firms to Ambidexterity Theory Using a Mixture of Normal Distributions","authors":"S. Casault, A. Groen, J. Linton","doi":"10.2516/OGST/2015018","DOIUrl":"https://doi.org/10.2516/OGST/2015018","url":null,"abstract":"Oil and gas exploration and production firms have return profiles that are not easily explained by current financial theory – the variation in their market returns is non-Gaussian. In this paper, the nature and underlying reason for these significant deviations from expected behavior are considered. Understanding these differences in financial market behavior is important for a wide range of reasons, including: assessing investments, investor relations, decisions to raise capital, assessment of firm and management performance. We show that using a “thicker tailed” mixture of two normal distributions offers a significantly more accurate model than the traditionally Gaussian approach in describing the behavior of the value of oil and gas firms. This mixture of normal distribution is also more effective in bridging the gap between management theory and practice without the need to introduce complex time-sensitive GARCH and/or jump diffusion dynamics. The mixture distribution is consistent with ambidexterity theory that suggests firms operate in two distinct states driven by the primary focus of the firm: an exploration state with high uncertainty and, an exploitation (or production) state with lower uncertainty. The findings have direct implications on improving the accuracy of real option pricing techniques and futures analysis of risk management. Traditional options pricing models assume that commercial returns from these assets are described by a normal random walk. However, a normal random walk model discounts the possibility of large changes to the marketplace from events such as the discovery of important reserves or the introduction of new technology. The mixture distribution proves to be well suited to inherently describe the unusually large risks and opportunities associated with oil and gas production and exploration. A significance testing study of 554 oil and gas exploration and production firms empirically supports using a mixture distribution grounded in ambidexterity theory to describe the value fluctuations for these firms.","PeriodicalId":19444,"journal":{"name":"Oil & Gas Science and Technology-revue De L Institut Francais Du Petrole","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78773400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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