Abstract:Carbonates are characterized by low oil recovery due to their positive surface charge and consequent high affinity to negatively charged crude oil, rendering them to a state of mixed-to-oil wettability. In order to understand the rock/brine/oil interactions and their effect on potential-determining-ions (PDIs) adsorption/desorption during engineered water injection is needed for realistic and representative estimations of oil recovery. Therefore, this study reveals a novel approach to capture various interacti… Show more
“…Therefore, dolomite concentration increased and finally approach to stabilized value more than initial dolomite concentration of rock sample. This behavior is in line by other researchers’ findings such as Khurshid et al stating that the adsorption of magnesium increases with the injection of sulfate-spiked water and consequently Ca2+ is replaced by Mg2+ in the rock 36 . Figure 14 The corresponding plot of dolomite concentration profile in case of SW/40-4S injection water sample.…”
Geochemical modeling along with chemical reactions is one of the challenges in modeling of low salinity water injection. The most important issue in the geochemical model is to determine the correct electrical charge distribution model and its tuning parameters. The composition of the rock as well as the candidate water used is effective in determining the type of model and its parameters, so that the tuning parameters are determined based on the history of zeta potential experiments. In this study, in order to determine the correct model of electrical charge distribution and its tuning parameters in carbonate rock samples, first, equilibrium samples of Candidate water with crushed rock are subjected to static zeta potential tests. Then, the diffuse electrical double layer model is used to determine the electrical charge of the rock/water and water/oil surfaces and to predict the zeta potential. In the following, by adjusting the tuning parameters of the model to match the prediction results of the model with the history of the laboratory data, the density of the carbonate rock surface, the equilibrium constants and the kinetics of the governing reactions are determined. The obtained results show that the range of error in zeta potential prediction by the model compared to the laboratory data is from 2 to 20%, which is within the acceptable range of the performance of electrical charge distribution models. Moreover, it could be observed that the error of prediction using DLM model is significantly less than the conventional models (CD-MUSIC and BSM) for different candidate water. Finally, the effect of calculated zeta potential changes is used to calculate the contact angle changes of low salinity water injection based on the coupling of DLVO theory and geochemical model. The results of the study prove that the prediction error is less than 5% compared to the results of the static wettability tests. Based on this, according to the good match between the model and the laboratory results, it is possible to determine the properties of surface sites in surface complexation models of carbonate samples using the proposed approach and the subsequent tuning data of the geochemical model.
“…Therefore, dolomite concentration increased and finally approach to stabilized value more than initial dolomite concentration of rock sample. This behavior is in line by other researchers’ findings such as Khurshid et al stating that the adsorption of magnesium increases with the injection of sulfate-spiked water and consequently Ca2+ is replaced by Mg2+ in the rock 36 . Figure 14 The corresponding plot of dolomite concentration profile in case of SW/40-4S injection water sample.…”
Geochemical modeling along with chemical reactions is one of the challenges in modeling of low salinity water injection. The most important issue in the geochemical model is to determine the correct electrical charge distribution model and its tuning parameters. The composition of the rock as well as the candidate water used is effective in determining the type of model and its parameters, so that the tuning parameters are determined based on the history of zeta potential experiments. In this study, in order to determine the correct model of electrical charge distribution and its tuning parameters in carbonate rock samples, first, equilibrium samples of Candidate water with crushed rock are subjected to static zeta potential tests. Then, the diffuse electrical double layer model is used to determine the electrical charge of the rock/water and water/oil surfaces and to predict the zeta potential. In the following, by adjusting the tuning parameters of the model to match the prediction results of the model with the history of the laboratory data, the density of the carbonate rock surface, the equilibrium constants and the kinetics of the governing reactions are determined. The obtained results show that the range of error in zeta potential prediction by the model compared to the laboratory data is from 2 to 20%, which is within the acceptable range of the performance of electrical charge distribution models. Moreover, it could be observed that the error of prediction using DLM model is significantly less than the conventional models (CD-MUSIC and BSM) for different candidate water. Finally, the effect of calculated zeta potential changes is used to calculate the contact angle changes of low salinity water injection based on the coupling of DLVO theory and geochemical model. The results of the study prove that the prediction error is less than 5% compared to the results of the static wettability tests. Based on this, according to the good match between the model and the laboratory results, it is possible to determine the properties of surface sites in surface complexation models of carbonate samples using the proposed approach and the subsequent tuning data of the geochemical model.
“…The primary recovery factor from carbonate reservoirs is notoriously low, with estimates typically ranging between 10 and 20%, compared to sandstone reservoirs. − Approximately 50–60% of the world’s conventional petroleum is in place in carbonate rocks, making it imperative to find ways to increase recovery rates …”
Wettability alteration has been proposed as one of the dominating effects of engineered salinity waterflooding (EWF). Most of the existing approaches for modeling wettability alteration and enhanced oil recovery by EWF often oversimplify, if not ignore, the critical role of geochemical reactions at both rock− brine and oil−brine interfaces in this process. Therefore, this paper presents a new approach wherein the flow simulation and salt transport are explicitly coupled with geochemical calculations based on bond product sum (BPS). In the BPS method, the surface complexation reactions at oil−brine and rock−brine interfaces are integrated and then used as basis for wettability alteration modeling during the flow simulation. The wettability alteration is accomplished by incorporating an interpolating parameter (θ) to update locally relative permeability and capillary pressure as a function of brine composition. This approach directly accounts for the effect of water composition and pH, oil composition (acid number and base number), and surface reaction site densities on EWF performance. Moreover, it can distinguish between directly and indirectly adsorbed oil components, affecting the initial wettability of the system. The method was validated through comparisons of the simulation results with those from published coreflood studies. We report that the directly adsorbed oil components, which promote oil-wetting conditions, aid oil recovery through EWF only under specific pH conditions, especially at pH > 8 for crude oils with high acid numbers. However, this cannot be easily practical for all systems. Conversely, indirectly adsorbed oil components enable mixed-wetting conditions and can enhance oil recovery through EWF. This can be accomplished via employing specific strategies, such as reducing water salinity or modifying the concentration of potential-determining ions (PDIs). The developed BPS-based model improves the prediction of transient oil recovery, particularly before reaching the residual oil saturation (S or ), as it is critically important for field planning and design.
“…Numerous authors highlighted the practical applications of NWFSSP in advanced industries, including the Steel industry 5 , 6 , Patient and Surgery scheduling problems 7 , 8 . Flight scheduling 9 , 10 , Parallel computing 11 , 12 , Traffic control system 13 , Train Scheduling 14 , Bakery production 15 , Robotic cells 16 , 17 , and Glass manufacturing 18 . The NWFSSP concept can be illustrated by considering a steel factory where heated metal must undergo continuous processing to maintain its temperature.…”
This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strategies (ES) and iterated greedy (IG) algorithm, as hybridizing algorithms helps different algorithms mitigate their weaknesses and leverage their respective strengths. The ES algorithm begins with a random initial solution and uses an insertion mutation to optimize the solution. Reproduction is carried out using (1 + 5)-ES, generating five offspring from one parent randomly. The selection process employs (µ + λ)-ES, allowing excellent parent solutions to survive multiple generations until a better offspring surpasses them. The IG algorithm’s straightforward search mechanism aids in further improving the solution and avoiding local minima. The destruction operator randomly removes d-jobs, which are then inserted one by one using a construction operator. The local search operator employs a single insertion approach, while the acceptance–rejection criteria are based on a constant temperature. Parameters of both ES and IG algorithms are calibrated using the Multifactor analysis of variance technique. The performance of the HES-IG algorithm is calibrated with other algorithms using the Wilcoxon signed test. The HES-IG algorithm is tested on 21 Nos. Reeves and 30 Nos. Taillard benchmark problems. The HES-IG algorithm has found 15 lower bound values for Reeves benchmark problems. Similarly, the HES-IG algorithm has found 30 lower bound values for the Taillard benchmark problems. Computational results indicate that the HES-IG algorithm outperforms other available techniques in the literature for all problem sizes.
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