mehdi assareh

Screening of water-based Enhanced Oil Recovery (EOR) techniques is a preliminary task in designing an EOR technique for an oil reservoir. Commonly, a screening table is looked up either manually or automatically to screen out unfitted EOR techniques for a specific reservoir, based on a worldwide database of successful EOR projects. This approach disregards the physics of the EOR processes within the reservoir and does not consider the optimal values of decision variables such as well control parameters during the screening and ranking of EOR techniques. In this study, we address these challenges by presenting a workflow for screening water-based EOR techniques. This includes surfactant flooding, polymer flooding, low-salinity water flooding, and Alkaline-Surfactant-Polymer (ASP), flooding in an oil reservoir. To conduct this, several representative sectors are extracted from the oil reservoir’s dynamic model. In the first step, an automated screening based on the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) algorithm determines the ranking of EOR techniques and similar EOR projects. In the next step, each water-based EOR project is simulated on each reservoir sector to rank the EOR techniques using the Recovery Factor (RF). Finally, stochastic optimizations of decision variables in each EOR technique in each reservoir sector update the ranking and screening. The final rankings from these three steps are averaged across all sectors to list the suitable EOR techniques and find their relative technical performance. The differences in the ranking list using the table-based screening, flow simulation, and optimization highlight the benefits of the proposed workflow.

Gas recycling is a generally accepted method to improve production from condensate gas reservoirs and has been welcomed due to its low cost compared with drilling new wells. The use of the net present value, which is both a factor of time and cost, is a reliable way of evaluating this process which considers the costs of injection facilities and production costs. It is important to find new methods for planning and optimizing gas injection. In this research, it has been tried to identify the optimal amount of gas recycling as well as the amount of gas produced by combining optimizers and reservoir simulators and using evolution optimization algorithms. It is worth noting that the method used in this study is based on a case study of gas recycling. The study shows that with the recycling of the entire production gas in the studied reservoir, the net present value could be increased from $ 126 million to $ 40 million. The production and recycling of a portion of the gas in an optimal form also increases the reservoir's life due to economic constraints from 10 years to 18 years. In addition, with a closer examination, it was concluded that due to the higher price of injected gas in cold seasons compared to the warm seasons, 65 percent of the gas produced can be returned to the reservoir in these seasons. By sensitivity analysis of the optimization results, the gas prices as well as the amount of gas condensate richness have significant effects on the optimal amount of recovered gas.

The presence of ions in the solution causes the water to have non-ideal. On the other hand, water molecules have strong hydrogen bonds. When water and dissolved salt are in equilibrium with gas such as carbon dioxide, they form a two-phase system with complex intermolecular forces. In this study, the aim is to model the thermodynamic equilibrium of water containing sodium chloride salt and carbon dioxide gas. The thermodynamic equilibrium of this system is analyzed using the ePC-SAFT state equation. This electrolyte equation of state is a combination of the PC-SAFT state equation and Debye-Hukel theory. The presence of repulsive, scattering, and cumulative intermolecular forces is calculated by the PC-SAFT and the electrostatic forces are calculated by Debye-Hukel theory. In this study, we first implement and predict the ePC-SAFT equation of state for non-associative pure systems. Then, the phase behavior prediction for water-salt, water-carbon dioxide and water-salt-carbon dioxide systems is performed. Finally, according to empirical data from reliable sources, the regression of the binary interaction of water-carbon dioxide in the water-carbon dioxide and carbon dioxide-salt-water system was performed. The equation of state mentioned predicts the laboratory data for the desired electrolyte solution with a good accuracy that the percentages of average relative deviation from the saturated vapor pressure data without kij parameter adjustment and with kij adjustment are 21. 46, and 14.61, respectively. In this study, it was observed that the presence of ions in the solution had little effect on the binary interaction of carbon dioxide.

Smart wells are unique tools for the management of oil reservoirs under waterflooding to increase the oil flow rate and reduce the associated wastewater production costs. The costs associated with smart completion are considerable. Consequently, the decision for designing and controlling such completion elements can have considerable impacts on project profitability. This work presents an efficient production scheduling for a multi-layer reservoir during water flooding by regulating water movement in the layers to control associated wastewater using intelligent elements. The central focus of this research is to give a production schedule using smart well completions. To achieve this, several segments of the production and injection wells are controlled independently with the schedules provided by a model-based optimization technique. To perform optimization, three methods are used to regulate waterfront and velocity in different layers; the first approach is used to regulate production well according to the saturation distribution in the reservoir without considering NPV. The second approach is the sequential optimization of well controls including flow rates and bottom-hole pressures, to find an optimized NPV. The third approach is to optimize flow rates and bottom-hole pressures for different segments in the production and injection wells, simultaneously, to achieve a maximized NPV using a genetic optimization algorithm. To evaluate these approaches, 2D and 3D reservoir models are used as case studies. The study shows a considerable increase in NPV concerning conventional wells in a fair comparison ground. In the 2D model, 9.89%, 11.75%, and 11.78% additional recoveries are achieved compared to a conventional production well using the first, second, and third optimization approaches, respectively. For the 3D model, 5.87%, 5.99%, and 6.20% were additional recoveries concerning the equivalent conventional production wells, for the first, second, and third approaches, respectively. This additional recovery is due to lower produced associated water and bypassed oil.

Due to the complexities of oil reservoir simulation models, there is a need to find an optimization method that can have good accuracy and speed while reducing computational costs. In this regard, in this study, a parallel genetic algorithm has been used to accelerate the optimizer in order to prevent the increase of the computational load of the well placement problem and the prolongation of the execution time. Instead of one population of chromosomes (i.e., the same optimization variables), this algorithm works with several populations, which exchange chromosomes with each other. The proposed mathematical model is a single-objective problem, which is to maximize the present value of the project in the location of wells. The price of oil and the cost of separating water and gas are considered in this economic function. In evaluating the objective function, the CPU of a single computer, or multiple networked computers, each in parallel, are responsible for calculating the chromosomes’ fitness of parallel populations. After calculating the target function or suitability of the chromosomes of all populations (in which each population has common genetic function), migration between populations takes place. A case study has been used to validate the implementation of this method. In the proposed model, the location of vertical wells in an oil tank has been verified. As a result of the optimization, the wells are located at five points, four injection wells at the corners, and one production well at the center of the model, and in addition to reducing optimization time, the number of simulator performances in parallel processing mode is significantly reduced. In this research, the performance of genetic and parallel genetic algorithms for location optimization on the same reservoir model is shown. The execution time of optimization between genetics and parallel genetics is 7100 seconds and 1800 seconds, respectively. As it is known, the use of PGA has quadrupled the execution time in the studied oil reservoir.

Water flooding is one of the common secondary recovery methods. One of the major problems of water flooding is the production of water. Consequently, water breakthrough happens quickly. In these conditions, the production of the reservoir is not economical. In comparison to conventional flooding, cyclic water flooding (CWF) produces more oil and less water. CWF is performed to produce oil from a zone with less permeability that cannot be displaced. Studies have shown that oil production is more likely to occur during the period of injector shut-in. To achieve maximum oil production from oil fields and less produced water, optimization methods should be used in the process of cyclic water injection. By controlling and optimizing the well parameters, such as production and injection rates, as well as the economic evaluation of the process, one can achieve this goal. The purpose of this study is the scheduling and optimization of production using net present value (NPV) for this process. In this research, two-dimensional and three-dimensional models from reliable literature data are simulated. After validation and comparison to the literature, the sensitivity of the model for the duration of injection and shut-in of the injector is performed in CWF. To optimize, we used the genetic algorithm, which is an efficient stochastic approach without the requirement of derivative calculation. The results showed that the CWF in a two-dimensional model in water-wet and oil-wet reservoirs has increased in NPV value of 2.26% and 3.9% respectively. In 3D model, the results showed NPV value in the cyclic flooding method in comparison to conventional flooding in the oil-wet reservoir has increased 3.285% and, in the water-wet reservoirs decreased 3.038%. The result of this study suggests CWF as a suitable secondary recovery approach in comparison to conventional flooding, especially oil-wet reservoirs.

Formation water is an important sample of electrolytic solutions. The amount of salts dissolved in water formation affects the volumetric properties of the formation water. In waterflooding, seawater, an electrolytic solution with very low concentrations of salt, forms an electrolytic solution with a high salt dissolved water (formation water), and therefore its properties change. The precise estimation of water formation properties plays an important role in estimating the flooding process. In this study, electrolyte PC-SAFT, in conjunction with Debye-Huckel's theory, was used to model the density and equilibrium of vapor and electrolytic solutions. In this equation, the state of ions is considered independent of the salt-forming it, and each ion needs two parameters of ionic diameter and extra dispersion energy over the usual PC-SAFT parameters. In this study, for the verification of the implementation of this equation, the vapor pressure, and the density of the solutions of sodium chloride, potassium chloride, sodium bromide, sodium sulfate, and lithium sulfate, as well as the vapor pressure of sodium chloride, potassium bromide, sodium bromide, and potassium chloride mixtures was compared with experimental data. Finally, using the validated program, changes in the formation water phase behavior is investigated by combining injectable water and the vapor pressure and molecular volume of their composition are predicted. Using this method, the properties’ changes of formation water are investigated by combining them with sea water in different proportions.

Having information on the chemical composition of hydrocarbon mixtures (the amount of paraffinic, naphthenic, and aromatics) is still a challenging problem in the area of reservoir fluids. Methods for determining these compositions include experimental techniques and empirical models. Laboratory methods are accurate, but they are costly and time-consuming. Therefore, researchers have tried to use empirical models instead of using laboratory methods. Generally, these models include two or more characteristic parameters such as Refractive Index (RI), normal boiling point (Tb), density in standard conditions (d), carbon to hydrogen ratio (CH), Watson coefficient (K), and viscosity - gravity constant(VGC). The problem with the proposed models is that for finding the compositions the mentioned characteristic parameters must be available, but usually, these parameters are not specified for petroleum cuts, therefore, in practice, the determination of chemical composition is difficult. In this research, we have tried to select parameters for the construction of a model that does not have this limitation. Selected parameters were MW, SG, and Tb which are molecular weight, specific gravity, and normal boiling point respectively. These parameters are commonly available for petroleum cuts, therefore for the determination of the composition of oil cuts, another specific characteristic parameter is not necessary. In this study, the neural network and the SAFT family equation of state have been used to determine the model for estimating the composition of families. For the development of this model, for synthetic cuts consisting of paraffinic, naphthenic, and aromatic, the specific gravity and normal boiling point values were determined using the PC-SAFT state equation. Finally, a neural network model was implemented on these data. In the end, the ability of the model for estimation has been tested using a series of evaluation data for oil cuts. The results indicate that the proposed model predicts the families present in the cuts effectively.

The measurement of the minimum miscibility pressure (MMP) is one of the most important steps in the project design of miscible gas injection for which several experimental and modeling methods have been proposed. On the other hand, the standard procedure for compositional studies of miscible gas injection process is the regression of EOS to the conventional PVT tests. Moreover, this procedure does not necessarily result in an accurate calculation of the MMP. In this study, an effective procedure is presented using both conventional PVT and slim tube data in the regression to provide appropriate EOS parameters for field studies including miscible gas injection. In the first step, the EOS parameters were subjected to regression to the conventional PVT data. In addition, these parameters were then used as inputs for simultaneous regression to the conventional PVT and MMP data. MMP is modeled through the automated execution of a series of compositional simulation of slim tube. Moreover, the regression uses a stochastic optimization for minimizing an objective function (regression) have been coupled with two separate core calculations, (1) equilibrium calculations of the conventional tests and (2) compositional simulation of the slim tube. For evaluation, a number of real reservoir fluids from field data are used from reliable datasets in the literature. Finally, the promising results demonstrated that this procedure is capable to provide EOS parameters for accurate predictions in the miscible gas injection processes.

Surface active agents (surfactants) as the most important chemicals to enhance oil recovery (EOR) can reduce interfacial tension between the injected aqueous solution and the oil in a reservoir. They change wettability of the porous media to release and move the remaining oil trapped in the pores and throats towards the well. According to the important roles of the surfactants, it is necessary to predict their performance for EOR process. In this research, two data-based mathematical models were developed to estimate interfacial tension of the oil, salty water and anionic surfactant system using 598 experimental data. To obtain the correlations between the independent variables and the objective function, genetic programing has been applied. Squared correlation coefficient (R2) of the models is 0.946 and 0.9387; moreover, root-mean-square deviation (RMSD) of the models is 3.4439 mN/m and 3.3261 mN/m respectively. Simplicity and acceptable estimation are particular features of the models.

This paper introduces an effective production optimization and a water injection allocation method for oil reservoirs with water injection. In this method, a two-stage adaptive simulated annealing (ASA) is used. A coarse-grid model is made based on average horizon permeability at the beginning iterations of the optimization to search quickly. In the second stage, the fine-grid model is used to provide the accuracy of the final solution. A constrained optimization problem to maximize an objective function based on net present value is implemented. Allocation factors from the streamline simulation are used to help for the appropriate estimation of initial water injection rates. The proposed optimization scheme is used for a field sector simulation model. The results show that the optimized rates confirm the increment of total oil production. Optimized oil production and total water injection rates lead to an increase in the total oil production from 385.983 (initial guess) to 440.656 Msm3. This means a recovery factor increment by 14.16%, while the initial rates were much higher than the optimized rates. Moreover, the recovery factor of optimized production schedule with an optimized total injection rate is 2.20% higher than the initial production schedule with an optimized total water injection rate. The allocation of the water injection rates and the revision of allocation rates result in 446.383 and 450.164 Msm3. The revision of the water rates allocation provides a reduction of water cut during production.

For some of the EOS models the dimension of equilibrium problem can be reduced. Stability and difficulties in implementation are among the problems of flash calculation. In this work, a new reduction technique is presented to prepare a reduced number of equilibrium equations. Afterwards, a number of appropriate solution variables are selected for the prepared equation system to solve the equations in an efficient numerical scheme. All the derivatives and solution procedures for the new reduced flash calculation framework were prepared based on Peng-Robinson equation of state. One reservoir oil sample and one gas condensate sample were selected from published literature to evaluate the proposed method for the calculations of reservoir fluids equilibrium. The equilibrium calculations with the proposed reduction technique were compared to full flash calculations. The reduced formulation implementation is simple and straightforward as it is derived from full flash fugacity equality criteria. The presented technique not only reduces the number of equations, and hence simplifies flash problem, but also presents a comparable convergence behavior and offers the same solution system for different reservoir fluid types. The results, demonstrates the proposed method performance and the accuracy for modeling with complex equilibrium calculations like compositional reservoir simulation when there are many components available in the mixture fluid description.

One of the common methods of enhanced oil recovery in naturally fractured reservoirs is gas injection. The majority of Iranian hydrocarbon reservoirs are fractured reservoirs. Producing mechanisms is different in fractured reservoirs in comparison with conventional reservoirs. Molecular diffusion is one of the mechanisms that along with gravity drainage can increase oil recovery in fractured reservoirs during gas injection. In this work, the effect of molecular diffusion in CO2 and hydrocarbon gas injection as an EOR (enhanced oil recovery) process is investigated using compositional simulation in a single matrix block and a sector model of an Iranian natural fractured reservoir. The effect of the matrix permeability, matrix and fracture permeability difference, matrix porosity, matrix gas-oil capillary pressure and injection gas composition are checked in single matrix blocks, and then the influence of diffusion is investigated on the recovery of the sector model during the CO2 and hydrocarbon gas injection. The results show that molecular diffusion raises oil recovery by increasing the mass transfer rate between the matrix and fracture during miscible gas injection (CO2 and hydrocarbon gas). The low matrix permeability and high gas-oil capillary pressure within the matrix oil make smaller displacement efficiency during gravity drainage. In the sector model, the molecular diffusion increases the ultimate oil recovery by about %2 and %5 in CO2 and hydrocarbon gas injection, respectively by delaying gas breakthrough in an production well and maintaining reservoir pressure.