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Schematics of Steam Assisted Gravity Drainage (SAGD) and Expanding Solvent-SAGD (ES-SAGD): (a) SAGD; and (b) ES-SAGD.
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Solvent–steam mixture is a key factor in controlling the economic efficiency of the solvent-aided thermal injection process for producing bitumen in a highly viscous oil sands reservoir. This paper depicts a strategy to quickly provide trade-off operating conditions of the Expanding Solvent–Steam Assisted Gravity Drainage (ES-SAGD) process based on...
Contexts in source publication
Context 1
... is a steam injection process that produces highly viscous oil under reservoir conditions [1]. SAGD composes a pair of a horizontal production well and an injection well (Figure 1a). The producer locates 3-5 m below the injector. ...
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... co-injects a single solvent or a mixture of solvents with steam into the reservoir from the injector [6] (Figure 1b). The solvent mixture primarily comprises of light hydrocarbons from C4 (butane) to C7 (heptane). ...
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... both figures, the initial solutions indicate the solutions in the first generation of NSGA-II adopted in the proposed hybrid multi-objective optimization approach. Compared to the non-optimal initial solutions, co-injecting steam and solvent at the maximum solvent fraction of 0.35 (Figure 9) led to the improvement for each performance indicator: the increase in RF as well as the decrease in cSOR and SR (Figure 10). The Pareto-optimal injection pressure Pinj ranges from its lower limit of 2000 kPa to its upper limit of 3800 kPa because increasing injection pressure improved RF while sacrificing cSOR at this solvent fraction. ...
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... conflict between RF and cSOR under the Pareto-optimal operating conditions is rational because the volume of steam needed for producing one barrel (approximate to 0.159 m 3 ) of oil is greater than one barrel. Figure 9a,c indicates that the shorter the operational period of the SAGD process is, the higher the total energy efficiency that can be achieved by initiating the ES-SAGD process, provided the injected solvent can be recovered sufficiently (Figure 10c). As the optimized SR ranges from 0.08 to 0.10, the expected solvent recovery is approximately 90%. ...
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... simulation results are quite optimistic, nevertheless are in reasonable agreement with observation results reported from field pilot tests [8,[50][51][52]. (a) (b) ( c) Figure 10. Projection of objective function values in two-dimensional objective space before and after optimization. ...
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... Figure 10, every final solution dominates one or more initial solutions but is non-dominated to any other final solutions. Table 7 provides the decision variables and corresponding performance indicator values of four representative solutions, which cover the lowest and greatest performance indicator values in the final non-dominated solution set obtained using the hybrid approach. ...
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... case corresponds to Solution 1 in Table 7. As mentioned in Figures 9 and 10, the non-dominated solution set obtained using NSGA-II includes two global optimum values obtained using the two GA runs. The global optimum of the first GA run minimizing Equation (12) is the same as Solution 3 presented in Table 7, whereas that of the second GA run minimizing Equation (13) is the same as Solution 1 in Table 7. ...
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In this article, the importance of colloids and interfaces in thermal heavy oil or bitumen extraction methods is reviewed, with particular relevance to oil sands. It begins with a brief introduction to the chemical composition and surface chemistry of oil sands, as well as steam-based thermal recovery methods. This is followed by the specific consi...
Citations
... Multiple objectives must be considered to identify the optimal values for these operational parameters. One option is to aggregate them into a single weighted objective; however, several previous studies have highlighted the challenges associated with properly formulating a single-objective function (Al-Gosayir et al. 2012Min et al. 2017); in many cases, the solution seeks to minimize the objective without considering the respective characteristics of each component. ...
... There are several examples of MOO application in the design of enhanced heavy oil recovery processes. Min et al. (2017) integrated the response surface modeling (RSM) in a MOO framework to optimize the expanding-solvent SAGD. MOO was also used in Ma and Leung (2020a) to design various injection parameters pertinent to the warm solvent injection process. ...
The steam alternating solvent (SAS) process involves multiple cycles of steam and solvent (e.g., propane) injected into a horizontal well pair to produce heavy oil. These solvent-based methods entail a smaller environmental footprint with reduced water usage and greenhouse gas emissions. However, the lack of understanding regarding the influences of reservoir heterogeneities, such as shale barriers, remains a significant risk for field-scale predictions. Additionally, the proper design of the process is challenging because of the uncertain heterogeneity distribution and optimization of multiple conflicting objectives. This work develops a novel hybrid multiobjective optimization (MOO) workflow to search a set of Pareto-optimal operational parameters for the SAS process in heterogeneous reservoirs.
A set of synthetic homogeneous 2D is constructed using data representative of the Cold Lake reservoir. Next, multiple heterogeneous models (realizations) are built to incorporate complex shale heterogeneities. The resultant set of SAS heterogeneous models is subjected to flow simulation. A detailed sensitivity analysis examines the impacts of shale barriers on SAS production. It is used to formulate a set of operational/decision parameters (i.e., solvent concentration and duration of solvent injection cycles) and the objective functions (cumulative steam/oil ratio and propane retention). The nondominated sorting genetic algorithm II (NSGA-II) is applied to search for the optimal decision parameters. Different formulations of an aggregated objective function, including average, minimum, and maximum, are used to capture the variability in objectives among the multiple realizations of the reservoir model. Finally, several proxy models are included in the hybrid workflow to evaluate the defined objective functions to reduce the computational cost.
Results of the optimization workflow reveal that both the solvent concentration and duration of the solvent injection in the early cycles have significant impacts. It is recommended to inject solvent for longer periods during both the early and late SAS stages. It is also noted that cases with higher objective function values are observed with more heterogeneities. This work offers promising potential to derisk solvent-based technologies for heavy oil recovery by facilitating more robust field-scale decision-making.
... The globalobjective optimization aggregates various objective functions into a single global-objective function with weighting factors and then searches for a single global solution (Deb, 2001). In this circumstance, the global solution is strongly governed by weighting factors, and thus, the global-objective optimization is vulnerable in providing diversified solutions when individual objective function conflicts with each other (Min et al., 2017). As a breakthrough, multi-objective optimization has been investigated to explore a set of equiprobable solutions improving convergence and diversity. ...
... Besides, the multi-objective optimization is beneficial to generate probability density functions (pdfs) of input factors and responses. For this reason, multi-objective optimization has been applied to solve optimization problems in subsurface modeling such as contaminant transport (Kollat and Reed, 2006;Reed et al., 2007), reservoir characterization with history matching (Christie et al., 2013;Min et al., 2014;Min et al., 2017;Park et al., 2015), and well-placement for production (Chang et al., 2015;Zhang et al., 2019). ...
... As a combination of 1 st offspring population to the 1 st parent population, 2×N pop solutions represent a mating pool of 1 st generation. N pop superior solutions from the mating pool survive, while the other solutions are discarded, by comparison of their objective function values based on non-dominated sorting and crowding-distance sorting (Deb et al., 2002;Min et al., 2017). These objective values are estimated by the fine-tuned RSEs, and the survived solutions compose the 2 nd parent population. ...
Prior to determining the optimal operating parameters for CO2 injection, conditions for both injection wellbore and storage formation should be evaluated; the build-up pressure induced by the CO2 injection could promote fractures in the storage formation, even collapsing the wellbore. In this study, a hybrid optimization methodology, which combined the proxy modeling and multi-objective optimization, was engaged in searching appropriate operating conditions for CO2 injection. The study utilized a fully coupled wellbore-reservoir (WR) model to simulate the CO2 injection scenarios. Three responses, such as pressure, temperature, and CO2 mass flow rate at the bottom-hole of injection wellbore, were investigated. To reduce the computational cost, the statistical proxy models were developed for approximating three responses. The developed fine-tuned proxy models revealed four influential factors; wellhead pressure, injected CO2 temperature, wellbore diameter, and permeability of a storage formation were significant in predicting three responses. Among these four influential factors, permeability was treated to be an uncertainty factor, while the other three factors were treated as tuning factors. According to acquired optimal solution sets, the optimum values for wellhead pressure and injected CO2 temperature were distributed around 10.0 MPa and 35 °C, respectively. For the wellbore diameter, its mean of optimal solutions was 0.1 m, and more solutions were concentrated at this mean value with a decrease in permeability.
... The NSGA-II has been adopted in many oil and gas applications. Min et al. (2017) successfully applied this algorithm to screen a set of Pareto-optimal operating conditions (i.e., injector bottom pressure, temperature, and solvent fraction) for the ES-SAGD process. In the work of Camara et al. (2018), the NSGA-II was used for placement of oil platforms and wells to minimize costs, maximize oil production, and reduce environmental impacts in offshore production settings. ...
Injection of warm solvent vapor into the formation is often considered as a cleaner alternative for heavy oil production. However, the design of relevant operational conditions is challenging, as it involves the optimization of multiple distinct objectives including oil production and solvent-oil-ratio (solvent usage efficiency). This work develops a hybrid optimization framework involving Pareto-based multiple-objective optimization (MOO) techniques for the design of warm solvent injection (WSI) operations in heterogeneous reservoirs. First, a set of synthetic models are constructed based on field data gathered from several typical Athabasca oil sands reservoirs. Models with different heterogeneity settings and the impact of solution gas on WSI production are examined. Next, non-dominated sorting genetic algorithm II (NSGA-II), is employed to optimize two operational parameters, i.e., bottom-hole pressures, based on multiple design objectives. Several proxy models are integrated into the optimization workflow to evaluate the objective functions at reduced computational costs. The performance of the proposed workflow is validated via both homogeneous and heterogeneous cases, and it is demonstrated that a set of Pareto-optimal operating conditions for different reservoir settings can be obtained. The results reveal that the optimal bottom-hole pressure of the producer should be kept at a minimum, while there is more flexibility in the injection bottom-hole pressure. Compared with other conventional optimization strategies, the proposed workflow requires fewer costly simulations and facilitates the optimization of multiple objectives simultaneously. The result demonstrated a great potential for extending the developed MOO framework to optimize operational conditions for other natural resource extraction processes.
... The NSGA-II has been adopted in many oil and gas applications. Min et al. (2017) successfully applied this algorithm to screen a set of Pareto-optimal operating conditions (i.e., injector bottom pressure, temperature, and solvent fraction) for the ES-SAGD process. In the work of Camara et al. (2018), the NSGA-II was used for placement of oil platforms and wells to minimize costs, maximize oil production, and reduce environmental impacts in offshore production settings. ...
In comparison to Steam-Assisted Gravity-Drainage (SAGD), the technique of injecting of warm solvent vapor into the formation for heavy oil production offers many advantages, including lower capital and operational costs, reduced water usage, and less greenhouse gas emission. However, to select the optimal operational parameters for this process in heterogeneous reservoirs is non-trivial, as it involves the optimization of multiple distinct objectives including oil production, solvent recovery (efficiency), and solvent-oil ratio. Traditional optimization approaches that aggregate numerous competing objectives into a single weighted objective would often fail to identify the optimal solutions when several objectives are conflicting. This work aims to develop a hybrid optimization framework involving Pareto-based multiple objective optimization (MOO) techniques for the design of warm solvent injection (WSI) operations in heterogeneous reservoirs.
First, a set of synthetic WSI models are constructed based on field data gathered from several typical Athabasca oil sands reservoirs. Dynamic gridding technique is employed to balance the modeling accuracy and simulation time. Effects of reservoir heterogeneities introduced by shale barriers on solvent efficiency are systematically investigated. Next, a state-of-the-art MOO technique, non-dominated sorting genetic algorithm II, is employed to optimize several operational parameters, such as bottomhole pressures, based on multiple design objectives. In order to reduce the computational cost associated with a large number of numerical flow simulations and to improve the overall convergence speed, several proxy models (e.g., response surface methodology and artificial neural network) are integrated into the optimization workflow to evaluate the objective functions.
The study demonstrates the potential impacts of reservoir heterogeneities on the WSI process. Models with different heterogeneity settings are examined. The results reveal that the impacts of shale barriers may be more/less evident under different circumstances. The proxy models can be successfully constructed using a small number of simulations. The implementation of proxy models significantly reduces the modeling time and storages required during the optimization process. The developed workflow is capable of identifying a set of Pareto-optimal operational parameters over a wide range of reservoir and production conditions.
This study offers a computationally-efficient workflow for determining a set of optimum operational parameters relevant to warm solvent injection process. It takes into account the tradeoffs and interactions between multiple competing objectives. Compared with other conventional optimization strategies, the proposed workflow requires fewer costly simulations and facilitates the optimization of multiple objectives simultaneously. The proposed hybrid framework can be extended to optimize operating conditions for other recovery processes.
Steam Alternating Solvent (SAS) process has been proposed as a more environmentally-friendly alternative to traditional steam-based processes for heavy oil production. It consists of injecting steam and a non-condensable gas (solvent) alternatively to reduce the oil viscosity. However, optimizing multiple process design (decision) variables is not trivial since multiple conflicting objectives (i.e., maximize the recovery factor, reduce steam-oil-ratio) must be considered. Three different Multi-Objective Evolutionary Algorithms (MOEAs) are employed to identify a set of Pareto-optimal operational parameters. A multi-objective optimization (MOO) workflow is developed: first, a 2D reservoir model is constructed based on the Fort McMurray formation. Second, a sensitivity analysis is performed to identify the most impactful decision parameters. Third, two response surface (proxy) models and three different MOEAs are employed and compared. This paper is the first to compare different MOEAs for optimizing a wide range of operational parameters for the SAS process. The results show that if more steam is injected, extending the steam cycle duration is preferable. Conversely, if more solvent is injected, it is recommended to start with injecting a solvent with high propane concentrations over short cycles and switch to lower propane concentrations over long cycles near the end.
The steam-assisted gravity drainage (SAGD) recovery process is strongly impacted by the spatial distributions of heterogeneous shale barriers. Though detailed compositional flow simulators are available for SAGD recovery performance evaluation, the simulation process is usually quite computationally demanding, rendering their use over a large number of reservoir models for assessing the impacts of heterogeneity (uncertainties) to be impractical. In recent years, data-driven proxies have been widely proposed to reduce the computational effort; nevertheless, the proxy must be trained using a large data set consisting of many flow simulation cases that are ideally spanning the model parameter spaces. The question remains: is there a more efficient way to screen a large number of heterogeneous SAGD models? Such techniques could help to construct a training data set with less redundancy; they can also be used to quickly identify a subset of heterogeneous models for detailed flow simulation. In this work, we formulated two particular distance measures, flow-based and static-based, to quantify the similarity among a set of 3D heterogeneous SAGD models.
First, to formulate the flow-based distance measure, a physics-basedparticle-tracking model is used: Darcy’s law and energy balance are integrated to mimic the steam chamber expansion process; steam particles that are located at the edge of the chamber would release their energy to the surrounding cold bitumen, while detailed fluid displacements are not explicitly simulated. The steam chamber evolution is modeled, and a flow-based distance between two given reservoir models is defined as the difference in their chamber sizes over time. Second, to formulate the static-based distance, the Hausdorff distance (Hausdorff 1914) is used: it is often used in image processing to compare two images according to their corresponding spatial arrangement and shapes of various objects.
A suite of 3D models is constructed using representative petrophysical properties and operating constraints extracted from several pads in Suncor Energy’s Firebag project. The computed distance measures are used to partition the models into different groups. To establish a baseline for comparison, flow simulations are performed on these models to predict the actual chamber evolution and production profiles. The grouping results according to the proposed flow- and static-based distance measures match reasonably well to those obtained from detailed flow simulations.
Significant improvement in computational efficiency is achieved with the proposed techniques. They can be used to efficiently screen a large number of reservoir models and facilitate the clustering of these models into groups with distinct shale heterogeneity characteristics. It presents a significant potential to be integrated with other data-driven approaches for reducing the computational load typically associated with detailed flow simulations involving multiple heterogeneous reservoir realizations.
This paper proposes multi-objective optimization of the Steam Assisted Gravity Drainage (SAGD) process for
improving the energy efficiency and recovery factor of oil sand reservoirs. Previous studies conducted on optimizing the
SAGD process have focused on single-objective optimization with fixed economic factors. In this study, multiple
trade-off operating scenarios were selected by applying a multi-objective optimization algorithm that aims at maximizing
the recovery factor and minimizing the cumulative steam-oil ratio for efficiently addressing volatile market conditions.
Thus, the proposed method can overcome the limitations of conventional optimization methods that not only yield a
single solution based on a particular objective function but also are hard to adapt to fluctuating oil prices. Furthermore,
the proposed method can provide optimum trade-off operating scenarios, and hence can aid in planning operating (i.e.,
marketing) strategies according to the variation in oil prices and operating costs, without the need for an additional
optimization process.
