The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Guangming Pan - One of the best experts on this subject based on the ideXlab platform.
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Quantitative Prediction Model for the Water–Oil Relative Permeability Curve and Its Application in Reservoir Numerical Simulation. Part 1: Modeling
Energy & Fuels, 2011Co-Authors: Jian Hou, Fuquan Luo, Chuanfei Wang, Yanhui Zhang, Kang Zhou, Guangming PanAbstract:The water–Oil Relative Permeability curve has a great effect on the rules of water cut increase and production variation. It is one of the most important data in reservoir development. With regard to a reservoir with a high degree of heterogeneity, the flow properties are various in different positions of the reservoir. Therefore, neither a single average Relative Permeability curve for the whole reservoir nor different curves for different sedimentary facies can precisely describe the reservoir flow characteristics, which will cause great difficulties for the remaining Oil prediction and potential tapping. Therefore, it is of great importance to build a prediction model for the water–Oil Relative Permeability curve, which can provide a calculation theory of the Relative Permeability curve for reservoir simulation using different Relative Permeability curves in different grid cells. The existing prediction models for the Relative Permeability curve have established the correlations between petrophysical p...
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quantitative prediction model for the water Oil Relative Permeability curve and its application in reservoir numerical simulation part 1 modeling
Energy & Fuels, 2011Co-Authors: Jian Hou, Fuquan Luo, Chuanfei Wang, Yanhui Zhang, Kang Zhou, Guangming PanAbstract:The water–Oil Relative Permeability curve has a great effect on the rules of water cut increase and production variation. It is one of the most important data in reservoir development. With regard to a reservoir with a high degree of heterogeneity, the flow properties are various in different positions of the reservoir. Therefore, neither a single average Relative Permeability curve for the whole reservoir nor different curves for different sedimentary facies can precisely describe the reservoir flow characteristics, which will cause great difficulties for the remaining Oil prediction and potential tapping. Therefore, it is of great importance to build a prediction model for the water–Oil Relative Permeability curve, which can provide a calculation theory of the Relative Permeability curve for reservoir simulation using different Relative Permeability curves in different grid cells. The existing prediction models for the Relative Permeability curve have established the correlations between petrophysical p...
Jian Hou - One of the best experts on this subject based on the ideXlab platform.
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Estimation of the Water–Oil Relative Permeability Curve from Radial Displacement Experiments. Part 2: Reasonable Experimental Parameters
Energy & Fuels, 2012Co-Authors: Jian Hou, Daigang Wang, Fuquan Luo, Shaoxian BingAbstract:The capillary pressure is the key parameter to affect the inversion accuracy of the water–Oil Relative Permeability curve. The existing analytical inversion methods have neglected the influence of capillary pressure, which may cause low precision for the estimated Relative Permeability curve in some cases. On the basis of the numerical inversion method for the water–Oil Relative Permeability curve established in part 1 (10.1021/ef300018w), taking the one-dimensional radial numerical experiment for example, the rules of Relative Permeability variation and influence of different displacement conditions on Relative Permeability deviation when neglecting the capillary pressure are investigated. With regard to water-wet cases whose Oil–water viscosity ratio is greater than 1.5, it indicates that the estimated water-phase Relative Permeability curve is higher and the estimated Oil-phase Relative Permeability curve is lower compared to the true Relative Permeability curve when the capillary pressure is neglected...
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Quantitative Prediction Model for the Water–Oil Relative Permeability Curve and Its Application in Reservoir Numerical Simulation. Part 1: Modeling
Energy & Fuels, 2011Co-Authors: Jian Hou, Fuquan Luo, Chuanfei Wang, Yanhui Zhang, Kang Zhou, Guangming PanAbstract:The water–Oil Relative Permeability curve has a great effect on the rules of water cut increase and production variation. It is one of the most important data in reservoir development. With regard to a reservoir with a high degree of heterogeneity, the flow properties are various in different positions of the reservoir. Therefore, neither a single average Relative Permeability curve for the whole reservoir nor different curves for different sedimentary facies can precisely describe the reservoir flow characteristics, which will cause great difficulties for the remaining Oil prediction and potential tapping. Therefore, it is of great importance to build a prediction model for the water–Oil Relative Permeability curve, which can provide a calculation theory of the Relative Permeability curve for reservoir simulation using different Relative Permeability curves in different grid cells. The existing prediction models for the Relative Permeability curve have established the correlations between petrophysical p...
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quantitative prediction model for the water Oil Relative Permeability curve and its application in reservoir numerical simulation part 1 modeling
Energy & Fuels, 2011Co-Authors: Jian Hou, Fuquan Luo, Chuanfei Wang, Yanhui Zhang, Kang Zhou, Guangming PanAbstract:The water–Oil Relative Permeability curve has a great effect on the rules of water cut increase and production variation. It is one of the most important data in reservoir development. With regard to a reservoir with a high degree of heterogeneity, the flow properties are various in different positions of the reservoir. Therefore, neither a single average Relative Permeability curve for the whole reservoir nor different curves for different sedimentary facies can precisely describe the reservoir flow characteristics, which will cause great difficulties for the remaining Oil prediction and potential tapping. Therefore, it is of great importance to build a prediction model for the water–Oil Relative Permeability curve, which can provide a calculation theory of the Relative Permeability curve for reservoir simulation using different Relative Permeability curves in different grid cells. The existing prediction models for the Relative Permeability curve have established the correlations between petrophysical p...
Fuquan Luo - One of the best experts on this subject based on the ideXlab platform.
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Estimation of the Water–Oil Relative Permeability Curve from Radial Displacement Experiments. Part 2: Reasonable Experimental Parameters
Energy & Fuels, 2012Co-Authors: Jian Hou, Daigang Wang, Fuquan Luo, Shaoxian BingAbstract:The capillary pressure is the key parameter to affect the inversion accuracy of the water–Oil Relative Permeability curve. The existing analytical inversion methods have neglected the influence of capillary pressure, which may cause low precision for the estimated Relative Permeability curve in some cases. On the basis of the numerical inversion method for the water–Oil Relative Permeability curve established in part 1 (10.1021/ef300018w), taking the one-dimensional radial numerical experiment for example, the rules of Relative Permeability variation and influence of different displacement conditions on Relative Permeability deviation when neglecting the capillary pressure are investigated. With regard to water-wet cases whose Oil–water viscosity ratio is greater than 1.5, it indicates that the estimated water-phase Relative Permeability curve is higher and the estimated Oil-phase Relative Permeability curve is lower compared to the true Relative Permeability curve when the capillary pressure is neglected...
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Quantitative Prediction Model for the Water–Oil Relative Permeability Curve and Its Application in Reservoir Numerical Simulation. Part 1: Modeling
Energy & Fuels, 2011Co-Authors: Jian Hou, Fuquan Luo, Chuanfei Wang, Yanhui Zhang, Kang Zhou, Guangming PanAbstract:The water–Oil Relative Permeability curve has a great effect on the rules of water cut increase and production variation. It is one of the most important data in reservoir development. With regard to a reservoir with a high degree of heterogeneity, the flow properties are various in different positions of the reservoir. Therefore, neither a single average Relative Permeability curve for the whole reservoir nor different curves for different sedimentary facies can precisely describe the reservoir flow characteristics, which will cause great difficulties for the remaining Oil prediction and potential tapping. Therefore, it is of great importance to build a prediction model for the water–Oil Relative Permeability curve, which can provide a calculation theory of the Relative Permeability curve for reservoir simulation using different Relative Permeability curves in different grid cells. The existing prediction models for the Relative Permeability curve have established the correlations between petrophysical p...
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quantitative prediction model for the water Oil Relative Permeability curve and its application in reservoir numerical simulation part 1 modeling
Energy & Fuels, 2011Co-Authors: Jian Hou, Fuquan Luo, Chuanfei Wang, Yanhui Zhang, Kang Zhou, Guangming PanAbstract:The water–Oil Relative Permeability curve has a great effect on the rules of water cut increase and production variation. It is one of the most important data in reservoir development. With regard to a reservoir with a high degree of heterogeneity, the flow properties are various in different positions of the reservoir. Therefore, neither a single average Relative Permeability curve for the whole reservoir nor different curves for different sedimentary facies can precisely describe the reservoir flow characteristics, which will cause great difficulties for the remaining Oil prediction and potential tapping. Therefore, it is of great importance to build a prediction model for the water–Oil Relative Permeability curve, which can provide a calculation theory of the Relative Permeability curve for reservoir simulation using different Relative Permeability curves in different grid cells. The existing prediction models for the Relative Permeability curve have established the correlations between petrophysical p...
Shaoxian Bing - One of the best experts on this subject based on the ideXlab platform.
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estimation of the water Oil Relative Permeability curve from radial displacement experiments part 1 numerical inversion method
Energy & Fuels, 2012Co-Authors: Daigang Wang, Zhenquan Li, Shaoxian BingAbstract:The water–Oil Relative Permeability curve is mainly obtained from linear displacement experiments. Few radial displacement experiments have been carried out. In the process of linear displacement experiments, the flow properties of the water–Oil two phase are linear. Nevertheless, it is radial near the bottom holes of an actual reservoir. With regard to both kinds of displacement experiments, the flow characteristics are various, which may result in great deviation to apply the linear calculation theory of the Relative Permeability curve to an actual reservoir. As a result of the above-mentioned problems, on the basis of radial displacement experiments, using the Levenberg–Marquardt algorithm for automatic history matching, this paper performs optimization of production performance and Relative Permeability representation models. Finally, a novel numerical inversion method for the radial water–Oil Relative Permeability curve is established. A test based on the basic data of a radial laboratory displacemen...
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Estimation of the Water–Oil Relative Permeability Curve from Radial Displacement Experiments. Part 1: Numerical Inversion Method
Energy & Fuels, 2012Co-Authors: Daigang Wang, Zhenquan Li, Shaoxian BingAbstract:The water–Oil Relative Permeability curve is mainly obtained from linear displacement experiments. Few radial displacement experiments have been carried out. In the process of linear displacement experiments, the flow properties of the water–Oil two phase are linear. Nevertheless, it is radial near the bottom holes of an actual reservoir. With regard to both kinds of displacement experiments, the flow characteristics are various, which may result in great deviation to apply the linear calculation theory of the Relative Permeability curve to an actual reservoir. As a result of the above-mentioned problems, on the basis of radial displacement experiments, using the Levenberg–Marquardt algorithm for automatic history matching, this paper performs optimization of production performance and Relative Permeability representation models. Finally, a novel numerical inversion method for the radial water–Oil Relative Permeability curve is established. A test based on the basic data of a radial laboratory displacemen...
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estimation of the water Oil Relative Permeability curve from radial displacement experiments part 2 reasonable experimental parameters
Energy & Fuels, 2012Co-Authors: Daigang Wang, Zhenquan Li, Shaoxian BingAbstract:The capillary pressure is the key parameter to affect the inversion accuracy of the water–Oil Relative Permeability curve. The existing analytical inversion methods have neglected the influence of capillary pressure, which may cause low precision for the estimated Relative Permeability curve in some cases. On the basis of the numerical inversion method for the water–Oil Relative Permeability curve established in part 1 (10.1021/ef300018w), taking the one-dimensional radial numerical experiment for example, the rules of Relative Permeability variation and influence of different displacement conditions on Relative Permeability deviation when neglecting the capillary pressure are investigated. With regard to water-wet cases whose Oil–water viscosity ratio is greater than 1.5, it indicates that the estimated water-phase Relative Permeability curve is higher and the estimated Oil-phase Relative Permeability curve is lower compared to the true Relative Permeability curve when the capillary pressure is neglected...
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Estimation of the Water–Oil Relative Permeability Curve from Radial Displacement Experiments. Part 2: Reasonable Experimental Parameters
Energy & Fuels, 2012Co-Authors: Jian Hou, Daigang Wang, Fuquan Luo, Shaoxian BingAbstract:The capillary pressure is the key parameter to affect the inversion accuracy of the water–Oil Relative Permeability curve. The existing analytical inversion methods have neglected the influence of capillary pressure, which may cause low precision for the estimated Relative Permeability curve in some cases. On the basis of the numerical inversion method for the water–Oil Relative Permeability curve established in part 1 (10.1021/ef300018w), taking the one-dimensional radial numerical experiment for example, the rules of Relative Permeability variation and influence of different displacement conditions on Relative Permeability deviation when neglecting the capillary pressure are investigated. With regard to water-wet cases whose Oil–water viscosity ratio is greater than 1.5, it indicates that the estimated water-phase Relative Permeability curve is higher and the estimated Oil-phase Relative Permeability curve is lower compared to the true Relative Permeability curve when the capillary pressure is neglected...
Mohammad Ali Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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Experimental investigation the effect of nanoparticles on the Oil-water Relative Permeability
The European Physical Journal Plus, 2016Co-Authors: Hamidreza Amedi, Mohammad Ali AhmadiAbstract:This paper presents the effects of the nanosilica particles on the water and Oil Relative Permeability curves at reservoir conditions. Real reservoir crude Oil sample was employed as an Oil phase in Relative Permeability measurements. In addition, real carbonate reservoir rock samples were employed as a porous media in core displacement experiments. To determine Relative Permeability curves, the unsteady-state approach was employed in which Toth et al. method was applied to the recovery data points. By increasing the nanosilica content of the aqueous phase the Oil Relative Permeability increased while the residual Oil saturation decreased; however, by increasing the nanosilica concentration in the aqueous solution the water Relative Permeability decreased. The outcomes of this paper can provide a better understanding regarding chemically enhanced Oil recovery (EOR) by nanoparticles. Moreover, Relative Permeability curves help us in the history matching section of reservoir simulation for any further EOR scenarios.
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evolving simple to use method to determine water Oil Relative Permeability in petroleum reservoirs
Petroleum, 2016Co-Authors: Mohammad Ali Ahmadi, Maurice B. Dusseault, Sohrab Zendehboudi, Ioannis ChatzisAbstract:Abstract In the current research, a new approach constructed based on artificial intelligence concept is introduced to determine water/Oil Relative Permeability at various conditions. To attain an effective tool, various artificial intelligence approaches such as artificial neural network (ANN), hybrid of genetic algorithm and particle swarm optimization (HGAPSO) are examined. Intrinsic potential of feed-forward artificial neural network (ANN) optimized by different optimization algorithms are composed to estimate water/Oil Relative Permeability. The optimization methods such as genetic algorithm, particle swarm optimization and hybrid approach of them are implemented to obtain optimal connection weights involved in the developed smart technique. The constructed intelligent models are evaluated by utilizing extensive experimental data reported in open literature. Results obtained from the proposed intelligent tools were compared with the corresponding experimental Relative Permeability data. The average absolute deviation between the model predictions and the relevant experimental data was found to be less than 0.1% for hybrid genetic algorithm and particle swarm optimization technique. It is expected that implication of HGAPSO-ANN in Relative Permeability of water/Oil estimation leads to more reliable water/Oil Relative Permeability predictions, resulting in design of more comprehensive simulation and further plans for reservoir production and management.
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Evolving simple-to-use method to determine water–Oil Relative Permeability in petroleum reservoirs
Petroleum, 2016Co-Authors: Mohammad Ali Ahmadi, Maurice B. Dusseault, Sohrab Zendehboudi, Ioannis ChatzisAbstract:In the current research, a new approach constructed based on artificial intelligence concept is introduced to determine water/Oil Relative Permeability at various conditions. To attain an effective tool, various artificial intelligence approaches such as artificial neural network (ANN), hybrid of genetic algorithm and particle swarm optimization (HGAPSO) are examined. Intrinsic potential of feed-forward artificial neural network (ANN) optimized by different optimization algorithms are composed to estimate water/Oil Relative Permeability. The optimization methods such as genetic algorithm, particle swarm optimization and hybrid approach of them are implemented to obtain optimal connection weights involved in the developed smart technique. The constructed intelligent models are evaluated by utilizing extensive experimental data reported in open literature. Results obtained from the proposed intelligent tools were compared with the corresponding experimental Relative Permeability data. The average absolute deviation between the model predictions and the relevant experimental data was found to be less than 0.1% for hybrid genetic algorithm and particle swarm optimization technique. It is expected that implication of HGAPSO-ANN in Relative Permeability of water/Oil estimation leads to more reliable water/Oil Relative Permeability predictions, resulting in design of more comprehensive simulation and further plans for reservoir production and management.
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connectionist approach estimates gas Oil Relative Permeability in petroleum reservoirs application to reservoir simulation
Fuel, 2015Co-Authors: Mohammad Ali AhmadiAbstract:Relative Permeability of the petroleum reservoirs is a key parameter for various aspects of the petroleum engineering area like as reservoir simulation, history matching and etc. Due to this fact, various approaches such as experimental, theoretical and numerical approaches have been studied however; such experimental methods are time consuming, complicated and expensive. Based on the addressed disadvantages, robust, rapid, simple and accurate model is needed to represent gas/Oil Relative Permeability through petroleum reservoirs. In this research communication we utilized the concept of various intelligent approaches such as least square support vector machine (LSSVM) which is high attended branches of artificial intelligent approaches. To develop and test the proposed LSSVM approach massive experimental Relative Permeability data from literature survey was faced to the addressed model. The suggested LSSVM method has low deviation from relevant measured values and statistical factors of the addressed model solutions were calculated. According to the determined statistical factors, the results of the proposed LSSVM approach prove and certify the high performance and low uncertainty of the addressed model in prediction gas/Oil Relative Permeability in petroleum reservoirs. Finally, the suggested LSSVM model could help us to prepare more precise and accurate Relative Permeability curves without extensive experiment and furthermore, could lead to provide high performance reservoir simulation with low uncertainty.
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Connectionist approach estimates gas–Oil Relative Permeability in petroleum reservoirs: Application to reservoir simulation
Fuel, 2015Co-Authors: Mohammad Ali AhmadiAbstract:Relative Permeability of the petroleum reservoirs is a key parameter for various aspects of the petroleum engineering area like as reservoir simulation, history matching and etc. Due to this fact, various approaches such as experimental, theoretical and numerical approaches have been studied however; such experimental methods are time consuming, complicated and expensive. Based on the addressed disadvantages, robust, rapid, simple and accurate model is needed to represent gas/Oil Relative Permeability through petroleum reservoirs. In this research communication we utilized the concept of various intelligent approaches such as least square support vector machine (LSSVM) which is high attended branches of artificial intelligent approaches. To develop and test the proposed LSSVM approach massive experimental Relative Permeability data from literature survey was faced to the addressed model. The suggested LSSVM method has low deviation from relevant measured values and statistical factors of the addressed model solutions were calculated. According to the determined statistical factors, the results of the proposed LSSVM approach prove and certify the high performance and low uncertainty of the addressed model in prediction gas/Oil Relative Permeability in petroleum reservoirs. Finally, the suggested LSSVM model could help us to prepare more precise and accurate Relative Permeability curves without extensive experiment and furthermore, could lead to provide high performance reservoir simulation with low uncertainty.
