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Results
Optimization of the Low Salinity Water Injection Process in Carbonate Reservoirs
Al-Shalabi, Emad Waleed (U. of Texas at Austin) | Sepehrnoori, Kamy (U. of Texas at Austin) | Delshad, Mojdeh (U. of Texas at Austin)
Abstract The low salinity water injection (LSWI) is one of the emerging improved oil recovery techniques. Minimizing risk and uncertainty is a major part of any proposed improved oil recovery method by highlighting the most significant design parameters and optimizing the entire process. This paper investigates the optimization of the LSWI process at field scale for carbonate reservoirs by considering the most influential design parameters. Seven uncertain and decision design parameters were selected. 5-spot LSWI pilot models were simulated using UTCHEM reservoir simulator with an empirical LSWI model. The Design of Experiment (DoE) method was used for sensitivity analysis and screening out insignificant parameters. The Response Surface Methodology (RSM) was implemented to optimize the LSWI cumulative oil recovery where a response surface was built. The performed sensitivity analysis showed that the three most important design parameters are LSWI slug size, reservoir heterogeneity (VDP), and injected water salinity. An optimum LSWI design was suggested and the results were validated using the UTCHEM simulator. Moreover, two scenarios (best and worst) were created to highlight the individual and combined effects of the seven tested design parameters on cumulative oil recovery by LSWI. By understanding the most influential LSWI design parameters, the field scale development can be conducted with more certainty and lower risk. Introduction The low salinity water injection (LSWI) is gaining popularity as an improved oil recovery technique because of its simplicity compared to other techniques. The LSWI effect on oil recovery has been shown at laboratory scale and to a limited extent at field scale for both carbonate and sandstone rocks. For full field scale development, optimizing the LSWI process by minimizing the related risk and uncertainty, and identifying the most significant design parameters is still a concern, which is addressed in this paper. Reviews of LSWI effect on oil recovery at field scale, Design of Experiment (DoE), Response Surface Methodology (RSM), and optimization are presented. The field scale studies started in sandstone rocks to investigate the effect of LSWI on oil recovery. The first field pilot was reported by Webb et al. (2004) as a single well chemical tracer test (SWCTT) and then McGuire et al. (2005) in sandstone reservoirs. Both tests showed a positive response by reducing the remaining oil saturation in both secondary and tertiary modes. The reported remaining oil saturation for these studies ranged from 30 to 50%, which is in match with the conducted laboratory studies. Seccombe et al. (2008) investigated the benefits of tertiary LSWI at the Endicott Field located in the North Slope of Alaska. They observed a constant water relative permeability at residual oil saturation for both low and high salinity water injections, which was consistent with their corefloods, numerical matching of the data, and the constant productivity of the wells from the SWCTTs. Later in the same field, the first comprehensive inter-well application was reported by Seccombe et al. (2010) involving an injector and a producer 1040 feet apart. The results were in agreement with the corefloods and SWCTTs as 10% incremental oil was recovered after the injection of 1.6 pore volumes of LSWI.
- Asia > Middle East (1.00)
- North America > United States > Texas (0.94)
- North America > United States > Alaska > North Slope Borough (0.24)
- Europe > United Kingdom > North Sea > Central North Sea (0.24)
- Research Report > New Finding (0.66)
- Research Report > Experimental Study (0.46)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- North America > United States > Alaska > North Slope Basin > Duck Island Field > Endicott Field > Kekiktuk Formation (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 375 > Block 34/7 > Snorre Field > Statfjord Group (0.99)
- Europe > Norway > North Sea > Northern North Sea > East Shetland Basin > PL 375 > Block 34/7 > Snorre Field > Lunde Formation (0.99)
- (13 more...)
Oil-Recovery Predictions for Surfactant Polymer Flooding
Rai, Khyati (Chevron) | Johns, Russell Taylor (U. of Texas at Austin) | Lake, Larry Wayne (U. of Texas at Austin) | Delshad, Mojdeh (U. of Texas at Austin)
Summary There is increasing interest in surfactant-polymer (SP) and alkali-surfactant-polymer (ASP) flooding because of the need to in-crease oil production from depleted and water flooded reservoirs. Prediction of oil recovery from SP flooding, however, is complex and time consuming. Thus, a quick and easy method is needed to screen reservoirs for potential SP floods. This paper presents a scaling model that is capable of producing reliable estimates of oil recovery for an SP flood using a simple spreadsheet calculation. The model is also useful for initial SP design. We present key dimensionless groups that control recovery for a SP flood. The proper physics for SP floods including the optimal salinity in the three-phase region and the trapping number for residual oil saturation determination has been incorporated. Based on these groups, a Box-Behnken experimental design is performed to generate response surface fits for oil recovery prediction at dimensionless times. The response surfaces derived can be used to estimate the oil recovery potential for any given reservoir and are ideal for screening large databases of reservoirs to identify the most attractive chemical flooding candidates. The response function can also be used for proper design of key parameters for SP and ASP flooding. Our model will aid engineers to understand how key parameters affect oil recovery without performing time consuming chemical simulations. This is the first time that dimensionless groups for SP flooding have been derived comprehensively to obtain a response function of oil recovery as a function of dimensionless groups. Introduction Surfactant-polymer (SP) and alkali-surfactant-polymer (ASP) flooding processes involve the injection of a surfactant-polymer slug followed by a polymer buffer and chase water injection. If designed correctly, the surfactant increases the capillary number, which is crucial for the mobilization and recovery of tertiary oil. Polymer increases the sweep efficiency by lowering the mobility ratio. If the reservoir crude oil has sufficient saponifiable components, soap is generated in situ by the reaction of these components with the injected alkali. Past screening models such as that of Paul et al. (1982) did not consider gravity and salinity effects. Wang et al. (1979) and Shook et al. (1988) carried out sensitivity studies on SP floods, but did not attempt to correlate oil recovery to the parameters studied. Thomas et al. (2000) described scaling criteria for the micellar flooding process from the basic mass balance equations using inspectional and dimensional analysis. Micellar flooding experiments were carried out in sandstone cores of two different sizes, and the scaled up recovery curves were compared. The agreement between the predicted and actual recoveries was good in some cases, but poor in others. Poor agreement is likely because they did not consider effects like heterogeneity. This paper presents a screening model that is capable of quickly producing quantitative estimates of oil recovery for a given surfactant-polymer flood including the effects of heterogeneity and salinity. It also develops the important dimensionless groups necessary to scale SP floods using inspectional analysis. The derived groups are used in a Box-Behnken experimental design to produce response surfaces for dimensionless oil recovery during SP flooding. We also give an approximate method to correct the scaling groups for the addition of alkali in the SP process.
- North America > United States > Texas (0.49)
- North America > United States > California (0.46)
Development of a Reservoir Simulator for Souring Predictions
Delshad, Mojdeh (U. of Texas at Austin) | Bryant, Steven Lawrence (U. of Texas at Austin) | Sepehrnoori, Kamy (U. of Texas at Austin) | Farhadinia, Mohammad Ali (U. of Texas at Austin)
Abstract During the last two decades, several simple models have been developed to predict the onset of reservoir souring in seawater injected reservoirs. The key mechanism to generate hydrogen sulfide is a biological reaction between sulfate in the injection water and volatile fatty acids in the formation water in the presence of sulfate reducing bacteria (SRB). The produced hydrogen sulfide interacts with rock and partitions between oil and water phases. Comparison of field data with reservoir souring model predictions often shows inconsistent results. We present the development of a comprehensive reservoir souring model in a chemical flooding simulator that accounts for these mechanisms. Being able to estimate the likelihood and timing of the onset of H2S production would permit more realistic assessments of project economics. We incorporated mechanisms of generation and transportation of H2S in porous media to develop a reservoir souring simulator to predict the onset of souring in oil reservoirs. We implemented a general souring model in a 3D finite difference compositional non-isothermal reservoir simulator. The results indicate that depending on the type of SRB, the temperature propagation in the formation determines the onset of biological reactions and consequently, the generation of different hydrogen sulfide concentrations. The lag in the temperature front with respect to the injection front can cause a delay in the observed souring if the temperature is not favorable for SRB activation. A predictive model would enable operators to make better decisions for remedial actions to either prevent souring or to mitigate its impact. The developed finite difference simulator is 3D and accurately accounts for variation and impact of in-situ concentrations, temperature, pressure, and reservoir heterogeneity on H2S transport and production. Introduction Reservoir souring is the process of the production of hydrogen sulfide in a seawater injected reservoir. Using the knowledge of the mechanisms of generation and transportation of hydrogen sulfide in the reservoir, several reservoir souring models have been developed (Ligthelm et al., 1991; Sunde et al., 1993; Eden et al., 1993). The degree of exactness and reliability of these models depend on their capabilities to mimic the essential parameters which determine the generation and transportation of the hydrogen sulfide in the porous media. Figure 1 shows the process of reservoir souring. While injecting cold sea water which contains sulfate, nitrate, phosphate, and SRB into the hot formation, which provides organic acids, in the presence of SRB, sulfate reacts with organic acids to produce hydrogen sulfide. The produced hydrogen sulfide interacts with rock surfaces and partitions between oil and water phases (Ligthelm et al., 1991). The expected concentrations and temperature profiles are shown in Figure 2. The temperature distribution ranges from sea water (Tw) to the reservoir (Tres) temperatures. The activities of SRB, which are responsible for souring, depend on the temperature distribution and available nutrients (Herbert et al., 1985). At low temperatures, mesophiles, and at high temperatures thermophiles or hyperthermophiles, are activated and the biological reaction between sulfate and organic acids will initiate. Table 1 shows the range of activation of the discussed SRB.
- Materials > Chemicals (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Constituents > Bacteria (0.34)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery (1.00)
- Production and Well Operations > Production Chemistry, Metallurgy and Biology > Corrosion inhibition and management (including H2S and CO2) (1.00)
- Health, Safety, Environment & Sustainability > Health > Noise, chemicals, and other workplace hazards (1.00)
Parallel Numerical Reservoir Simulations of Nonisothermal Compositional Flow and Chemistry
Delshad, Mojdeh (U. of Texas at Austin) | Thomas, Sunil George (CSM, ICES, UT Austin) | Wheeler, Mary F.
Abstract This paper describes an efficient numerical scheme for non-isothermal compositional flow coupled to chemistry. An iterative IMPEC method is applied to solve the flow problem using a volume balance convergence criterion. A backward Euler mixed FEM with lowest order RT0 elements is applied to solve the pressure equation and a component local mass preserving explicit scheme is used to update concentrations. Chemical reactions are solved using explicit Runge-Kutta ODE integration schemes. A higher order Godunov method and a backward Euler mixed FEM are applied for thermal advection and conduction, respectively, in a time-split scheme. One of the major applications of the method is in the modeling of field scale CO2 sequestration as an EOR process or for containment in deep saline aquifers where chemical reactions and temperature variations may have an impact on the flow and transport of CO2. Leakage patterns when CO2 is injected near leaky abandoned wells, the displacement of methane from depleted gas reservoirs and accurate modeling of geochemical reactions involving injected CO2 are other applications of interest. Results of a benchmark problem in multiphase flow with several hydrocarbon components in formations with highly heterogeneous permeability on very fine grids, as well as a large scale parallel implementation of modeling CO2 sequestration are presented to justify the practical use of the model. A prallel efficiency of about 80% was observed on up to 512 cores in the benchmark study., Results from a problem simulating injection of CO2 in deep aquifers including non-isothermal and chemical effects are also presented. The results indicate a good agreement of the solutions with published data where available. Numerical modeling and simulation of CO2 sequestration plays a major role in future site selections and in designing storage facilities for effective CO2 containment. The main contribution of this paper lies in providing a parallel and efficient method of simulating challenging compositional flow problems such as in the study of CO2 sequestration as well as flow coupled to thermal and geochemical effects.
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Chemical flooding methods (1.00)
Scale Up Methodology for Wettability Modification in Fractured Carbonates
Delshad, Mojdeh (U. of Texas at Austin) | Najafabadi, Nariman Fathi (U. of Texas at Austin) | Sepehrnoori, Kamy (U. of Texas at Austin)
Abstract Recent research findings suggest that wettability modification holds a great potential for increased oil production from mixed-wet and fractured carbonates. Our current knowledge of the field-scale performance of these processes is very limited despite very favorable results obtained in the laboratory-scale experiments. Reservoir simulation is required to properly scale up the process from laboratory to field conditions and to understand and interpret reservoir data. Without a mechanistic simulation tool and adequate scale up, it is unlikely that a cost-effective process can be developed and applied economically. The predictive simulation and methodology to scale up such a complex process will reduce the risk of failure of field projects. A chemical compositional reservoir simulator with the capability to model oil recovery from mixed-wet carbonate rocks in both static imbibition and dynamic fractured block experiments using chemicals to alter wettability is used for this scale up study. The simulator captured the key recovery processes of capillary and natural imbibition, wettability alteration, buoyancy, oil mobilization, and viscous pressure gradient in imbibition experiments. Proper scaling from laboratory to field indicates that the synergy of wettability alteration, ultra low interfacial tension, and emulsification under small viscous pressure gradient provides an attractive and profitable opportunity in fractured carbonate reservoirs. Dimensionless scaling groups and numerical simulations are presented for each experimental condition to aid in understanding the time dependence and up-scaling of the laboratory results to field scale applications. The oil recovery results of the static imbibition experiment were successfully scaled using a reference time based on gravity emphasizing that the buoyancy was a dominant mechanism in this case. The scale up simulations for the dynamic fractured block experiment indicated favorable conditions for field scale applications with more dominance of viscous forces. Introduction Laboratory alkali and surfactant floods have shown a great potential in increasing oil recovery for reservoirs that are naturally fractured with low permeability mixed-wet matrix rocks. Fractured, mixed-wet formations usually have poor waterflood performance because the injected water tends to flow in the fractures and spontaneous imbibition into the matrix is generally insignificant. Surfactants or alkalis have successfully been used to change the wettability and enhance oil recovery by increased imbibition of the water into the matrix rock. The oil recovery mechanisms using surfactant/alkali mixtures are enhanced imbibition and buoyancy due to combined effects of reduced interfacial tension, reduced mobility ratio, and wettability alteration. Several authors evaluated the relative contribution of gravity and capillary forces on oil recovery in imbibition experiments (Hirasaki and Zhang, 2004; Babadagli and Boluk, 2005; Hognesen et al., 2004; Zhang et al., 2008). The explanation common by these authors are that the surfactant or alkali chemicals modify the wettability of the rock to more water-wet allowing a counter current water imbibition as a capillary-driven imbibition while reducing interfacial tension by surfactant causes gravity forces to prevail. Hognesen et al. (2004) tested the dimensionless time correlation developed by Li and Horne (2006) for their imbibition experiments in carbonate rocks performed for a wide range of experimental conditions of interfacial tension, permeability, initial water saturation, core height and diameter, temperature, and sulfate concentration. All the parameters were scaled very well when the normalized oil recovery was plotted versus dimensionless time once the height of the core was used as the shape factor. They concluded that gravitational forces were significant oil recovery mechanisms in their experiments.
- North America > United States > Texas > Permian Basin > Delaware Basin > Yates Field > Whitehorse Group > Word Group > San Andreas Formation (0.99)
- North America > United States > Texas > Permian Basin > Delaware Basin > Yates Field > Whitehorse Group > Grayburg Formation > San Andreas Formation (0.99)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Carbonate reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
Development of a Three Phase, Fully Implicit, Parallel Chemical Flood Simulator
Delshad, Mojdeh (U. of Texas at Austin) | Han, Choongyong (Chevron ETC) | Sepehrnoori, Kamy (U. of Texas at Austin) | Najafabadi, Nariman Fathi (U. of Texas at Austin)
Abstract Field-scale applications of chemical flooding become more attractive with higher oil prices. Several pilot and commercial scale chemical floods are currently in operation or design. Economic feasibility of such projects relies on how cost-effectively the remaining oil volume is recovered. Robust design and optimization are essential for technical success and profitability. The main objective in chemical flooding design is to keep the surfactant in Type III near the optimum salinity. Salinity gradient design is a robust design since it can compensate for heterogeneity and reservoir uncertainties and guarantees the surfactant in Type III for a longer time compared to other designs. A comprehensive surfactant phase behavior model is required to take into account the salinity gradient design with all possible phase transitions. The development discussed in this paper enables accurate modeling and optimization of chemical flooding designs for realistic field-scale projects where a salinity gradient exists naturally or is imposed by design. The parallel processing capability combined with the fully implicit scheme allows high-resolution full field scale chemical flooding simulations. Comprehensive surfactant phase behavior as a function of water salinity has been implemented into a fully implicit and parallel equation of state (EOS) compositional simulator, GPAS. The fully implicit simulation results are validated against an IMPES chemical flooding simulator. Accurate simulation of chemical processes requires fine grid resolution which adds a large computational overhead for modeling field-scale projects. Our enhanced simulator will allow for high resolution, field-scale design and optimization of chemical flooding projects. This is the first fully implicit, chemical-EOS compositional simulator with a comprehensive surfactant phase behavior that can take into account the effect of salinity and the resulting two or three phase flow with efficient parallel scalability. Introduction Chemical flooding is an enhanced oil recovery (EOR) method that can efficiently recover the residual oil saturation by significant reduction of interfacial tension (IFT) between the aqueous and oleic phases. This IFT reduction causes the residual and trapped oil to be mobilized and produced at the production wells. Surfactants also increase oil recovery by solubilization of the oil in the aqueous phase. Normally, a chemical flood consists of few slugs. The first slug contains the aqueous surfactant solution at a desired salinity and polymer for mobility control. The surfactant slug is chased with a polymer drive to maintain the mobility control.
A Fully Implicit, Parallel, Compositional Chemical Flooding Simulator
Han, Choongyong (U. of Texas at Austin) | Delshad, Mojdeh (U. of Texas at Austin) | Sepehrnoori, Kamy (U. of Texas at Austin) | Pope, Gary Arnold (U. of Texas at Austin)
Abstract We have developed a fully implicit, parallel, compositional reservoir simulator that includes both a cubic equation of state model for the hydrocarbon phase behavior and Hand's rule for the surfactant/oil/brine phase behavior. The aqueous species in the chemical model include surfactant, polymer, and salt. The physical property models include surfactant/oil/brine phase behavior, interfacial tension, viscosity, adsorption, and relative permeability as a function of trapping number. The fully implicit simulation results were validated by comparison with results from our IMPEC chemical flooding simulator (UTCHEM). The results indicate that the simulator scales well using clusters of workstations. Also, simulation results from parallel runs are almost identical to those using a single processor. Field-scale, high-resolution surfactant/polymer flood simulations were successfully performed with over 1,000,000 gridblocks using more than 100 processors. Introduction Chemical flooding is a method to improve oil recovery that involves the injection of a solution of surfactant and polymer followed by a polymer solution. The surfactant causes the mobilization of oil by decreasing interfacial tension whereas the polymer increases the sweep efficiency by lowering the mobility ratio. Chemical flooding has the potential to recover a very high fraction of the remaining oil in a reservoir, but the process needs to be designed to be both cost effective and robust, which requires careful optimization. Several reservoir simulators with chemical flooding features have been developed as a tool for optimizing the design.1–3 The University of Texas chemical flooding simulator, UTCHEM1 is an example of a simulator that has been used for this purpose. However, because UTCHEM is an Implicit Pressure and Explicit Concentration (IMPEC) code and in its current form cannot run on parallel computers, realistic surfactant/polymer flooding simulations are limited to on the order of one hundred thousand gridblocks due to small time step restrictions. Recently, we added the appropriate chemical module to the fully implicit, parallel, EOS compositional simulator called GPAS (General Purpose Adaptive Simulator) based on a hybrid approach.4 GPAS uses a cubic equation of state model for the hydrocarbon phase behavior and the parallel and object-based Fortran 95 framework for managing memory, input/output, and the necessary communication between processors.5–6 In the hybrid approach implemented in GPAS, the material balance equations for hydrocarbon and water components are solved implicitly first. Then, the material balance equations for the aqueous components such as surfactant, polymer, and electrolytes are solved explicitly using the updated phase fluxes, saturations, and densities. Although the hybrid method has proved to be useful for certain cases, in other cases the material balance errors can be large because both volumes of dissolved oil in microemulsion phase and surfactant in volumetric constraint equation are neglected. The timestep size is also restricted similar to an IMPES formulation due to the explicit scheme to solve the material balance equations for aqueous components. To overcome these limitations and to obtain more accurate and fast simulation results, we have developed a fully implicit chemical flood module with relevant physical properties for GPAS. In this paper, we present the governing equations for the chemical flooding simulation followed by solution approach using the fully implicit scheme. Then, physical properties will be presented in detail with emphasis on surfactant/oil/brine phase behavior. A brief review of the framework and solver for parallel computation will be given, followed by simulation results to show validation of the simulator developed, consistency and speedup of parallel runs, and capability to simulate very large problems with over one million gridblocks.
An Efficient Reservoir-Simulation Approach To Design and Optimize Improved Oil-Recovery-Processes With Distributed Computing
Zhang, Jiang | Delshad, Mojdeh (U. of Texas at Austin) | Sepehrnoori, Kamy (U. of Texas at Austin) | Pope, Gary Arnold (U. of Texas at Austin)
Abstract An efficient approach to obtain the optimum design under uncertainty for a wide range of reservoir simulation applications has been developed and successfully implemented.The approach discussed here significantly reduces the time required to evaluate optimum designs for improved oil recovery (IOR) processes. Determining the optimum combination of design variables for an IOR process is a complex problem that depends on the crude oil price, reservoir and fluid properties, process performance, and well specifications.Due to the large number of design variables, numerical simulation is often the most appropriate tool to evaluate the feasibility of such a process.However, because of the economical and geological uncertainties, the optimum design should be expressed as a distribution to gauge the uncertainties. Our innovative simulation approach has the capability to determine an economically optimum design that includes the following variables for surfactant/polymer flooding projects.The duration of water injection prior to the surfactant flooding Surfactant concentration and slug size Polymer concentration injected with the surfactant The concentration and duration of the polymer drive The salt concentration in different stages of the flood The uncertain parameters considered in this study were Dykstra-Parsons coefficient as a measure formation heterogeneity, average reservoir permeability, horizontal correlation length, ratio of horizontal to vertical correlation lengths, vertical to horizontal permeability ratio, residual oil saturation, surfactant adsorption, price of crude oil and chemicals, and discount rate. In order to efficiently perform these complex design processes efficiently, a platform that distributes multiple simulations onto a cluster of computer processors has been developed.The platform integrates several oil reservoir simulators, an economic model, an experimental design and response surface methodology, and a Monte Carlo algorithm with a global optimization search engine to identify the optimum design under conditions of uncertainty. The technique incorporates the following steps:Factorial design to find the most influential design and uncertain factors. Response surface methodology (RSM) design over those most influential factors to fit a response surface using net present value (NPV) as the objective function. Monte Carlo simulation over the response surface to maximize the mean of the net present value and search for the optimum combination of the design variables at the same time. This approach is applied to a field-scale surfactant/polymer flood using the UTCHEM simulator to find the optimal values of design variables that will maximize the NPV. Introduction Improved oil recovery (IOR) techniques have been developed and successfully tested in pilots with high tertiary oil recovery by oil industry for several decades.As an example of IOR, surfactant flooding is considered a high risk process, and has not made a significant contribution to U.S. EOR production, because of the high chemical costs, low and uncertain oil price, relatively long project life, and the lack of confidence in the process performance.However, under the current high crude oil price, surfactant flooding has a potential to be economical but with a careful reservoir and fluids characterizations and process modeling and optimization. In the process of implementing a commercial surfactant flooding project, both technical and financial decisions must be made.It is extremely difficult to establish an optimal decision due to the lack of accurate reservoir and fluid data and uncertain chemical costs, oil price, tax, and discount rate.Clearly, an efficient method is required that helps the engineers make optimal decisions in spite of uncertainties. For a surfactant flood, the uncertain parameters are from the reservoir and fluid properties and crude oil price.Under these uncertainties, an optimal combination of the decision variables is obtained in order to make project decisions.Table 1 summarizes the previous published work[1–7] on surfactant flood design and optimization.
- Europe > France > Chateaurenard Field (0.99)
- Asia > China > Shandong > Bohai Basin > North China Basin > Gudong Field (0.99)
- Asia > China > Shandong > Bohai Basin > Jiyang Basin > Gudong Field (0.99)
A New Generation Chemical Flooding Simulator
John, Abraham (U. of Texas at Austin) | Han, Choongyong (U. of Texas at Austin) | Delshad, Mojdeh (U. of Texas at Austin) | Pope, Gary A. (U. of Texas at Austin) | Sepehrnoori, Kamy (The University of Texas at Austin)
Summary Compositional reservoir simulators that are based on equation-of-state (EOS)formulations typically do not handle the modeling of aqueous phase behavior, and those that are designed for modeling chemical processes typically assume simplified hydrocarbon phase behavior. There is a need to have a single reservoir simulator capable of combining both approaches to benefit from the advantages of both aqueous and hydrocarbons models. Developing and implementing fully implicit procedures for modeling both hydrocarbon and aqueous phase behavior simultaneously is a complex process. An approach to integrate a surfactant phase behavior model into an existing fully implicit, parallel, EOS compositional simulator is presented in this paper. Physical property models describing the flow and transport of surfactant and polymer species have been implemented. These properties include surfactant phase behavior, interfacial tension, capillary desaturation, viscosity, adsorption, and relative permeability as a function of trapping number. Polymer properties include viscosity, permeability reduction, inaccessible pore volume, and adsorption. The simulation results were validated by comparison with the explicit chemical-flooding simulator UTCHEM and are shown in this paper. Test runs were performed with high-resolution models in a parallel environment, with results indicating a good scalability of the simulator. Introduction Increased oil production using improved oil recovery processes requires numerical modeling of such processes to minimize the risk involved in development decisions. The oil industry is requiring much more detailed analyses with a greater demand for reservoir simulation with geological, physical, and chemical models of much more detail than the past. Reservoir simulation has become an increasingly widespread and important tool for analyzing and optimizing oil recovery projects. Numerical simulation of large petroleum reservoirs with complex recovery processes is computationally challenging because of the problem size and detailed property calculations involved. This problem is compounded by the finer resolution needed to model such processes accurately. Traditionally, such simulations have been performed on workstations or high-end desktop computers. These computers restrict the problem size because of their address- able memory limit, and simulation studies of the entire project life become time-consuming. Parallel reservoir simulation, especially on low-cost, high-performance computing clusters, has alleviated these issues to a certain extent. Recent publications describe the development of such approaches and emphasize the necessity and advantages of using parallel processing. 1--4 Compositional reservoir simulators that are based on EOS formulations do not handle the modeling of aqueous phase behavior and those that are designed for chemical-flood modeling typically assume simplified hydrocarbon phase behavior. There is need to have a single reservoir simulator capable of combining both approaches to benefit from the advantages of both models. The overall objective of this research is to develop such technology using a computational framework that also allows parallel processing. The initial stage of development involved the formulation of a fully implicit, parallel, EOS compositional simulator. 5The description of the framework approach used for modular code development and the application to gas injection is in Wang et al. 6 In this paper, we focus on the implementation of the chemical module to the existing EOS simulator, its validation, and its application to large-scale chemical-flooding simulations. The formulation of the compositional model is briefly described. The assumptions for the chemical model and its formulation are described next. We use Hand's rule 7 to describe surfactant/oil/brine Type II(--) phase behavior. The trapping number model for relative permeability is implemented to capture the changes in residual saturations caused by the lowered interfacial tension. The validation of the implementation against the explicit chemical flooding simulator UTCHEM is shown. Application to large-scale problems and tests showing the parallel performance of the simulator are described. The approach we used to couple the models is easy to implement, computationally efficient, and extendable to many other interesting reservoir problems involving aqueous chemistry. With the capability of parallel processing, the general purpose adaptive simulator (GPAS) can now be used to simulate chemical flooding on a larger scale than before.
A Framework to Design and Optimize Surfactant-Enhanced Aquifer Remediation
Zhang, Jiang | Delshad, Mojdeh (U. of Texas at Austin) | Sepehrnoori, Kamy (U. of Texas at Austin)
This paper was part of a student paper session included in the conference. The paper was included in the proceedings as paper STUDENT3. Abstract Surfactant enhanced aquifer remediation has become an acceptable remediation technology to remove groundwater contaminants such as petroleum hydrocarbons and chlorocarbons.There have been several recent field demonstrations of surfactant remediation where surfactants are used to significantly increase the solubility of the nonaqueous phase liquid (NAPLs) in water.The success of these tests was due to a unified approach using site characterization coupled with a laboratory screening followed by 3D modeling of the process for design and optimization purposes. Here we present the design aspects of the subsurface surfactant flood with the emphasis on the flow and transport modeling.The University of Texas numerical model UTCHEM was used for this purpose.UTCHEM is a three-dimensional, multiphase, multicomponent chemical compositional simulator capable of modeling NAPL migration and groundwater flow and transport in aquifers.Simulations are performed to determine test design variables. These include wellfield configuration and rates, duration of injection and composition of surfactant solution, and hydraulic control achievement using hydraulic control wells. We illustrate an approach to design and optimize surfactant-enhanced aquifer remediation processes in a systematic and efficient manner.To make the approach efficient, a framework that can distribute multiple numerical simulations on a cluster of processors has been successfully implemented. This framework integrates a chemical-enhanced numerical model, UTCHEM, an experimental design methodology, and a Monte Carlo algorithm with a robust global optimization search engine to identify the optimal combination under conditions of uncertainty. Introduction Surfactant flooding process has been used in petroleum industry for many years to enhance the oil recovery[1].During recent years, surfactant-enhanced aquifer remediation has become an acceptable remediation technology to remove groundwater contaminants such as petroleum hydrocarbons and chlorocarbons[2–5].There have been several recent field demonstrations of surfactant remediation where surfactants are used to significantly increase the solubility of phase liquid NAPLs in water[6–8].The success of these tests was due to a unified approach using site characterization coupled with a laboratory screening[9–12] followed by 3D modeling of the process for design and optimization purposes[13–15]. Here we present an innovative approach on the design and optimization aspects of subsurface surfactant flood by using a 3D modeling numerical simulator, UTCHEM.The approach was demonstrated by simulating the dense nonaqeous phase liquid (DNAPL) remediation in a shallow aquifer flushed with surfactant at Hill Air Force Base in Utah[15].