The Experts below are selected from a list of 1215 Experts worldwide ranked by ideXlab platform
Ioannis Chatzis - One of the best experts on this subject based on the ideXlab platform.
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connectionist model to estimate performance of steam assisted Gravity Drainage in fractured and unfractured petroleum reservoirs enhanced oil recovery implications
Industrial & Engineering Chemistry Research, 2014Co-Authors: Sohrab Zendehboudi, Ioannis Chatzis, Amin Reza Rajabzadeh, Alireza Bahadori, Maurice B Dusseault, Ali Elkamel, Ali Lohi, Michael FowlerAbstract:Steam-Assisted Gravity Drainage (SAGD) is an enhanced oil recovery technology for heavy (or viscous) oil and bitumen that involves drilling two horizontal wells in underground formations. Laboratory work, pilot-plant studies, and mathematical model development, which are generally costly, difficult, and time-consuming tasks, are taken into account as important stages in finding an effective and economical method and also predicting the performance of the SAGD technique for a certain heavy-oil reservoir. Currently, smart techniques as accurate and fairly fast tools are highly recommended for these purposes. In this work, an experimental study and an artificial neural network (ANN) linked to an optimization technique, called particle swarm optimization (PSO), were employed to obtain performance parameters such as the cumulative steam-to-oil ratio (CSOR) and recovery factor (RF) for the SAGD process. The outputs of the developed connectionist modeling (i.e., ANN–PSO) were compared with actual data, showing a...
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Connectionist Model to Estimate Performance of Steam-Assisted Gravity Drainage in Fractured and Unfractured Petroleum Reservoirs: Enhanced Oil Recovery Implications
2014Co-Authors: Sohrab Zendehboudi, Ioannis Chatzis, Amin Reza Rajabzadeh, Alireza Bahadori, Maurice B Dusseault, Ali Elkamel, Ali Lohi, Michael FowlerAbstract:Steam-Assisted Gravity Drainage (SAGD) is an enhanced oil recovery technology for heavy (or viscous) oil and bitumen that involves drilling two horizontal wells in underground formations. Laboratory work, pilot-plant studies, and mathematical model development, which are generally costly, difficult, and time-consuming tasks, are taken into account as important stages in finding an effective and economical method and also predicting the performance of the SAGD technique for a certain heavy-oil reservoir. Currently, smart techniques as accurate and fairly fast tools are highly recommended for these purposes. In this work, an experimental study and an artificial neural network (ANN) linked to an optimization technique, called particle swarm optimization (PSO), were employed to obtain performance parameters such as the cumulative steam-to-oil ratio (CSOR) and recovery factor (RF) for the SAGD process. The outputs of the developed connectionist modeling (i.e., ANN–PSO) were compared with actual data, showing an average error lower than 7%, mostly because of the supremacy of the ANN–PSO method compared to the conventional ANN method and the correlations developed in this study. Furthermore, it is concluded that, among the contributing parameters, reservoir thickness and oil saturation have the most significant impacts on RF and CSOR during SAGD operations. The current study confirms the potential of hybrid connectionist modeling to screen heavy-oil fractured reservoirs for the SAGD process
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production characteristics of the steam assisted Gravity Drainage sagd and solvent aided sagd sa sagd processes using a 2 d macroscale physical model
Energy & Fuels, 2012Co-Authors: Omid Mohammadzadeh, Nima Rezaei, Ioannis ChatzisAbstract:There are extensive bitumen deposits in Canada that, with the development of sustainable exploitation technologies, can satisfy the energy requirements of the nation for more than a century. The currently available surface mining and in situ recovery techniques are applicable to only ∼15% of the known in-place resources. There is an obvious need for improved technology to make more of the in-place resources exploitable. This paper is aimed at studying the macroscale performance of the Steam-Assisted Gravity Drainage (SAGD) and the solvent-aided SAGD (SA-SAGD) processes. A two-dimensional (2-D) physical model of porous media was designed and fabricated for the purpose of implementing these two processes. The physical model was packed with different sizes of glass beads to create porous media with different permeability values. Athabasca bitumen was used as the oil phase. All the SAGD and SA-SAGD experiments were performed in an isothermal jacket as the controlled-temperature environment to reduce the amoun...
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pore level investigation of heavy oil and bitumen recovery using solvent aided steam assisted Gravity Drainage sa sagd process
Energy & Fuels, 2010Co-Authors: Omid Mohammadzadeh, Nima Rezaei, Ioannis ChatzisAbstract:Attempts have been made to reduce the energy requirements of steam assisted Gravity Drainage (SAGD) projects through coupled thermal and solvent processes (i.e., hybrid SAGD). The augmented process brings superior features to the SAGD process in terms of reduced energy requirement, enhanced produced oil quality, and also improved oil recoveries. The pore-level recovery mechanisms of the hybrid SAGD process have not been investigated yet. The main objective of this paper is to visually investigate and to document the pore-scale events during the hybrid SAGD process using glass micromodel type of porous media. Different additives (n-pentane and n-hexane) were added to steam prior to injecting into the models. Experiments were conducted in an inverted-bell vacuum chamber to reduce the excessive heat loss to the surroundings. The results indicate that the Gravity Drainage process takes place through a layer of pores composed of 1−5 pores in thickness, in the direction perpendicular to the nominal oil−gaseous ...
David Pernitsky - One of the best experts on this subject based on the ideXlab platform.
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oil sands steam assisted Gravity Drainage process water sample aging during long term storage
Energy & Fuels, 2015Co-Authors: Matthew Alan Petersen, Claire Susan Henderson, Annie Q Sun, David PernitskyAbstract:Technology development activities are routinely performed using process water samples collected and stored for several months while tests are being conducted. The results of the technology development activities are highly correlated to the water composition and properties. Processes such as atmospheric oxygen contamination, microbiological activity, and ultraviolet (UV) oxidation have the potential to act on a sample during long-term storage and modify the properties that may be relevant to technology development testing. These changes are referred to as “aging”. Process water samples collected from a Steam-Assisted Gravity Drainage (SAGD) bitumen production plant were subjected to different storage conditions and monitored for nearly 5 months. The sample organic composition and physical characteristics of the water were found to be highly dependent upon storage conditions, particularly atmospheric oxygen exposure. Oxygen exposure appeared to drive abiotic polymerization and precipitation of phenolic spe...
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Oil Sands Steam-Assisted Gravity Drainage Process Water Sample Aging during Long-Term Storage
2015Co-Authors: Matthew Alan Petersen, Annie Q Sun, Claire S. Henderson, David PernitskyAbstract:Technology development activities are routinely performed using process water samples collected and stored for several months while tests are being conducted. The results of the technology development activities are highly correlated to the water composition and properties. Processes such as atmospheric oxygen contamination, microbiological activity, and ultraviolet (UV) oxidation have the potential to act on a sample during long-term storage and modify the properties that may be relevant to technology development testing. These changes are referred to as “aging”. Process water samples collected from a Steam-Assisted Gravity Drainage (SAGD) bitumen production plant were subjected to different storage conditions and monitored for nearly 5 months. The sample organic composition and physical characteristics of the water were found to be highly dependent upon storage conditions, particularly atmospheric oxygen exposure. Oxygen exposure appeared to drive abiotic polymerization and precipitation of phenolic species and promote aerobic microbiological activity. These results highlight the importance of excluding oxygen from the sample during collection and storage activities. Sample aging must be accounted for in technology development testing activities
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Dissolved Organic Matter in Steam Assisted Gravity Drainage Boiler Blow-Down Water
2015Co-Authors: Subhayan Guha Thakurta, David Pernitsky, Abhijit Maiti, Subir BhattacharjeeAbstract:Steam assisted Gravity Drainage (SAGD) boiler blow-down (BBD) water contains high concentrations of dissolved organic matter (DOM) and total dissolved solids (TDS). A detailed understanding of the BBD chemistry, particularly the DOM composition, is important for better management and recycle of this water. In this study, we fractionated the dissolved organic matter in the BBD using DAX-8, Dowex, and Duolite resins into hydrophobic and hydrophilic fractions of acid, base, and neutral compounds. Additionally, the DOM was fractionated on the basis of size by filtering the BBD through a series of membranes with progressively tighter molecular weight cutoffs of 10, 3, and 0.5 kDa. Fluorescence excitation–emission matrix spectroscopy (EEMs), specific UV absorbance (SUVA), and FTIR were used to characterize the water samples and the different fractions. The ion exchange fractionation revealed that the DOM contained a high percentage of hydrophobic acids (39%) and hydrophilic neutrals (28.5%). The different ion exchange fractions had distinct fluorescence excitation–emission signatures. The permeate samples from the membrane fractionation, on the other hand, did not reveal any significant difference in the fluorescence EEM spectra, indicating that the hydrophilic and hydrophobic constituents of the DOM could not be separated on the basis of pore size by these membranes. The SAGD boiler blow-down water was found to be significantly concentrated in DOM compared to oil sands mining process affected water
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Characterization of Boiler Blowdown Water from Steam-Assisted Gravity Drainage and Silica–Organic Coprecipitation during Acidification and Ultrafiltration
2015Co-Authors: Abhijit Maiti, David Pernitsky, Subhayan Guha Thakurta, Mohtada Sadrezadeh, Subir BhattacharjeeAbstract:In thermally enhanced oil recovery operations, particularly in Steam-Assisted Gravity Drainage (SAGD), boiler blowdown (BBD) containing high concentrations of dissolved organic matter (DOM), dissolved silica, and total dissolved solids (TDS) is generated. To develop efficient tools for managing this blowdown, a detailed understanding of its chemistry is required. In this study, BBD was evaporated to yield ∼66% condensate and ∼33% concentrate blowdown (CBD). Detailed characterization of the BBD and CBD water was conducted. The effect of acidification was also studied. The acidification coprecipitates the silica and DOM, with over 90% of the silica and over 40% of the DOM precipitating at pH 4. Ultrafiltration treatment was also examined, and a major fraction of the silica and DOM in the CBD was found to foul a 100 kDa ultrafiltration membrane in the pH range of 7.5 to 9. The analysis revealed that the dominant fouling mechanism was cake filtration, indicating the formation of a silica–DOM precipitate layer on the membrane surface. These studies can provide insight regarding management options for SAGD disposal water
Peter John Dzurman - One of the best experts on this subject based on the ideXlab platform.
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practical implementation of knowledge based approaches for steam assisted Gravity Drainage production analysis
Expert Systems With Applications, 2015Co-Authors: Juliana Y Leung, Stefan Zanon, Peter John DzurmanAbstract:Input attributes descriptive of SAGD reservoir heterogeneities are formulated.Neural network models are trained using a comprehensive field dataset.Uncertainty analysis is performed involving Monte Carlo and bootstrapping methods.Sensitivity of model architecture is explored.Results demonstrate important potential in facilitating SAGD production analysis. Quantitative appraisal of different operating areas and assessment of uncertainty due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Although detailed compositional simulators are available for recovery performance evaluation for Steam-Assisted Gravity Drainage (SAGD), the simulation process is usually deterministic and computationally demanding, and it is not quite practical for real-time decision-making and forecasting. Data mining and machine learning algorithms provide efficient modeling alternatives, particularly when the underlying physical relationships between system variables are highly complex, non-linear, and possibly uncertain.In this study, a comprehensive training set encompassing SAGD field data compiled from numerous publicly available sources is analyzed. Exploratory data analysis (EDA) is carried out to interpret and extract relevant attributes describing characteristics associated with reservoir heterogeneities and operating constraints. An extensive dataset consisting of over 70 records is assembled. Because of their ease of implementation and computational efficiency, knowledge-based techniques including artificial neural network (ANN) are employed to facilitate SAGD production performance prediction. The principal components analysis (PCA) technique is implemented to reduce the dimensionality of the input vector, alleviate the effects of over-fitting, and improve forecast quality. Statistical analysis is performed to analyze the uncertainties related to ANN model parameters and dataset. Predictions from the proposed approaches are both successful and reliable. It is demonstrated that model predictability is highly influenced by model parameter uncertainty. This work illustrates that data-driven models are capable of predicting SAGD recovery performance from log-derived and operational variables. The modeling approach can be updated when new information becomes available. The analysis presents an important potential to be integrated directly into existing reservoir management and decision-making routines.
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integrated cluster analysis and artificial neural network modeling for steam assisted Gravity Drainage performance prediction in heterogeneous reservoirs
Expert Systems With Applications, 2015Co-Authors: Ehsan Amirian, Juliana Y Leung, Stefan Zanon, Peter John DzurmanAbstract:Data-driven modeling provides an attractive alternative to predict SAGD recovery.The modeling approach is applied successfully for heterogeneous reservoirs.Arps parameters are proposed to parameterize production time-series data.A normalized shale indicator is used as a pertinent input attribute.Accuracy of the prediction is greatly enhanced when cluster analyses are performed. Evaluation of Steam-Assisted Gravity Drainage (SAGD) performance that involves detailed compositional simulations is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for practical decision making and forecasting, particularly when dealing with high-dimensional data space consisting of large number of operational and geological parameters. Data-driven modeling techniques, which entail comprehensive data analysis and implementation of machine learning methods for system forecast, provide an attractive alternative.In this paper, artificial neural network (ANN) is employed to predict SAGD production in heterogeneous reservoirs, an important application that is lacking in existing literature. Numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and other relevant operating parameters. Empirical Arps decline parameters are tested successfully for parameterization of cumulative production profile and considered as outputs of the ANN models. Sensitivity studies on network configurations are also investigated. Principal components analysis (PCA) is performed to reduce the dimensionality of the input vector, improve prediction quality, and limit over-fitting. In a case study, reservoirs with distinct heterogeneity distributions are fed to the model. It is shown that robustness and accuracy of the prediction capability are greatly enhanced when cluster analysis are performed to identify internal data structures and groupings prior to ANN modeling. Both deterministic and fuzzy-based clustering techniques are compared, and separate ANN model is constructed for each cluster. The model is then tested using a validation data set (cases that have not been used during the training stage).The proposed approach can be integrated directly into most existing reservoir management routines. In addition, incorporating techniques for dimensionality reduction and clustering with ANN demonstrates the viability of this approach for analyzing large field data set. Given that quantitative ranking of operating areas, robust forecasting, and optimization of heavy oil recovery processes are major challenges faced by the industry, the proposed research highlights the significant potential of applying effective data-driven modeling approaches in analyzing other solvent-additive steam injection projects.
Omid Mohammadzadeh - One of the best experts on this subject based on the ideXlab platform.
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production characteristics of the steam assisted Gravity Drainage sagd and solvent aided sagd sa sagd processes using a 2 d macroscale physical model
Energy & Fuels, 2012Co-Authors: Omid Mohammadzadeh, Nima Rezaei, Ioannis ChatzisAbstract:There are extensive bitumen deposits in Canada that, with the development of sustainable exploitation technologies, can satisfy the energy requirements of the nation for more than a century. The currently available surface mining and in situ recovery techniques are applicable to only ∼15% of the known in-place resources. There is an obvious need for improved technology to make more of the in-place resources exploitable. This paper is aimed at studying the macroscale performance of the Steam-Assisted Gravity Drainage (SAGD) and the solvent-aided SAGD (SA-SAGD) processes. A two-dimensional (2-D) physical model of porous media was designed and fabricated for the purpose of implementing these two processes. The physical model was packed with different sizes of glass beads to create porous media with different permeability values. Athabasca bitumen was used as the oil phase. All the SAGD and SA-SAGD experiments were performed in an isothermal jacket as the controlled-temperature environment to reduce the amoun...
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pore level investigation of heavy oil and bitumen recovery using solvent aided steam assisted Gravity Drainage sa sagd process
Energy & Fuels, 2010Co-Authors: Omid Mohammadzadeh, Nima Rezaei, Ioannis ChatzisAbstract:Attempts have been made to reduce the energy requirements of steam assisted Gravity Drainage (SAGD) projects through coupled thermal and solvent processes (i.e., hybrid SAGD). The augmented process brings superior features to the SAGD process in terms of reduced energy requirement, enhanced produced oil quality, and also improved oil recoveries. The pore-level recovery mechanisms of the hybrid SAGD process have not been investigated yet. The main objective of this paper is to visually investigate and to document the pore-scale events during the hybrid SAGD process using glass micromodel type of porous media. Different additives (n-pentane and n-hexane) were added to steam prior to injecting into the models. Experiments were conducted in an inverted-bell vacuum chamber to reduce the excessive heat loss to the surroundings. The results indicate that the Gravity Drainage process takes place through a layer of pores composed of 1−5 pores in thickness, in the direction perpendicular to the nominal oil−gaseous ...
Zhangxin Chen - One of the best experts on this subject based on the ideXlab platform.
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Effects of Lean Zones on Steam-Assisted Gravity Drainage Performance
MDPI AG, 2017Co-Authors: Zhangxin Chen, Xiaohu Dong, Wei ZhouAbstract:A thorough understanding of the effects of lean zones and the improvement of Steam-Assisted Gravity Drainage (SAGD) operations with such heterogeneities is critically important for reducing the disadvantages of lean zones. The numerical model shows: (1) SAGD is most influenced by the single-layer lean zone with the above-injector (AI) location; with the decrease of interval distance and increase of thickness and water saturation in lean zones, the detrimental effect of single-layer lean zones on SAGD performance increases; (2) with the increase of period and decrease of connate and initial water saturations in lean zones, the detrimental effect of multiple-layer lean zones on SAGD performance increases; (3) reducing the injection pressure properly improves SAGD performance in leaky oil sands. The field-scale study indicates: (1) well pair 1 is most affected by lean zones in the studied pad due to the widest distribution of lean zones above its injector, and a hybrid cyclic steam stimulation (CSS)/SAGD method is proposed to overcome the practical problem of a low injection pressure in this area; (2) simulation results prove that the hybrid CSS/SAGD method is better than the conventional SAGD method in leaky oil sands
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a model to estimate heat efficiency in steam assisted Gravity Drainage by condensate and initial water flow in oil sands
Industrial & Engineering Chemistry Research, 2016Co-Authors: He Zhong, Mingzhe Dong, Zhangxin Chen, Xinfeng JiaAbstract:Steam-Assisted Gravity Drainage (SAGD) is one of the most popular approaches for oil sands recovery. In this study, an accurate and simple heat efficiency calculation model is presented by modeling transient heat transfer involving the flow of both hot condensate and mobile initial water in oil sands. A 2-D numerical simulation procedure is proposed to calculate temperature distribution ahead of the steam chamber edge in SAGD. A new heat efficiency model is proposed to estimate heat efficiency in SAGD by using the temperature distribution obtained from the 2-D numerical simulation. Results indicate that convection dominates the heat transfer ahead of the steam chamber edge which is induced by condensate and initial water flow in oil sands. On the basis of these studies, abundant analysis shows that the heat efficiency of SAGD can be improved by using moderate steam injection pressures for the reservoir scenarios from 1 to 10 darcy permeability.
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study of heat transfer by thermal expansion of connate water ahead of a steam chamber edge in the steam assisted Gravity Drainage process
Fuel, 2015Co-Authors: He Zhong, Mingzhe Dong, Zhangxin ChenAbstract:Abstract Steam-Assisted Gravity Drainage (SAGD) has been the preferred thermal method for bitumen recovery from reservoirs in western Canada, such as Athabasca and Cold Lake. In SAGD, near the edge of a steam chamber, the viscosity of bitumen can be reduced by several orders of magnitude by the release of latent heat from injected steam. Consequently, the heated bitumen flows downwards to a horizontal production well, under the action of Gravity. A critical control of oil production performance in SAGD is the heat transfer ahead of the steam chamber edge. It is commonly suggested that heat conduction is the only, or dominant, mechanism for heat to be transferred to the cold oil sands. Heat transfer through convection is neglected in classical models, such as in Butler’s theory. Although a few mathematical studies have recently been performed to examine the role of convective heat transfer through condensate flow perpendicular or parallel to the steam chamber edge, the role of heat transfer by cold connate water thermal expansion in SAGD has been given little attention. In this study, an analytical model is derived for heat transfer induced by thermal expansion of the connate water, and the result is reasonably consistent with the numerical simulation results obtained by running CMG STARS. The relative roles of conduction and convection ahead of the steam chamber edge are re-examined. The results show that heat convection accounts for a much higher percentage of the total heat transfer than conduction. This study also suggests that parameters that have a close relationship with the thermal expansion of connate water, such as the steam injection temperature and connate water saturation, can affect the relative roles of conductive and convective heat transfer in SAGD. Based on this study, the heat transfer efficiency can be enhanced through improving convection induced by thermal expansion of connate water.
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effects of reservoir heterogeneities thief zone and fracture systems on the fast sagd process
Energy Sources Part A-recovery Utilization and Environmental Effects, 2014Co-Authors: T B N Nguyen, T Q C Dang, Wisup Bae, Zhangxin ChenAbstract:This study presented a numerical investigation for evaluating the potential applicability of Fast-Steam-Assisted Gravity Drainage recovery process under complex reservoir conditions, such as shale barriers, thief zones with bottom and/or top water layers, and fracture systems in Clearwater formation. The simulation results indicated that the near well regions are very sensitive with shale layers and only long, continuous shale barriers (larger than 50 m or 25%), which can effect Fast-Steam-Assisted Gravity Drainage performance at the above well regions. Besides that, the thief zones have a strongly detrimental effect on Fast-Steam-Assisted Gravity Drainage. The results also proved that Fast-Steam-Assisted Gravity Drainage recovery process enhanced in the presence of vertical fractures, but horizontal fractures were harmful on the recovery. This article is a worthy guideline for Fast-Steam-Assisted Gravity Drainage operations in complex geological reservoirs.