Chemical Flooding Simulator Research Articles

Testing of a hydrodynamic chemical flooding simulator by comparing the results of polymer flooding simulation with similar results of existing simulators

The use of numerical modeling tools, including reservoir simulators, helps oil companies in making decisions and ensuring successful field development. This work is devoted to the testing of a reservoir simulator based on a new formulation in partial derivatives of a compositional model of chemical flooding by comparing the results of polymer flooding simulation with similar results of Eclipse 100.

Modeling and optimization of alkaline-surfactant-polymer flooding and hybrid enhanced oil recovery processes

This paper presents recent advances in the subject of modeling and optimization of ASP (Alkaline, Surfactant and Polymer) flooding. We used an approach that models the behavior of the surfactant-oil-water-microemulsion system based on solubility data. In Type III systems, the emulsion is distributed judiciously between the oil and water phases without the need to introduce a third liquid phase. This model captures the most important physical and chemical phenomena in the ASP process. The model is validated with numerous coreflooding experiments conducted by different research institutes as well as with a specialized chemical flood simulator. Agreements between the model and the experiments in terms of oil recovery and pressure drop were achieved for all core floods. In addition, the model is consistent with both UTCHEM-EQBATCH and UTCHEM-IPHREEQC.We investigated the potential of hybrid low salinity ASP flooding in which Low Salinity Waterflooding (LSW) was implemented in secondary production and followed by ASP flooding. This approach can provide a superior performance compared to the conventional chemical flooding because it provides better oil recovery in the secondary stage and promotes the synergy between a low salinity environment and ASP slugs. Finally, the proposed robust optimization workflow helps to increase a project NPV and significantly reduces the uncertainty range associated with geology.

An examination of the concept of apparent skin factor in modeling injectivity of non-Newtonian polymer solutions

In a conventional reservoir simulation, analytical well models relate well block pressure, wellbore pressure, fluid mobility, and geometric factors such as wellbore radius and grid size. Basically, fluid viscosity is assumed to be Newtonian in these models. Commercial reservoir simulators using these well models often predict unrealistic well injectivity in simulation of processes involving non-Newtonian fluids such as polymer solutions. Apparent viscosity of a polymer solution is a complicated function of flow velocity, polymer properties, permeability, porosity, salinity, etc. As the distance away from an injection well increases, flow velocity decreases; meanwhile, the apparent polymer viscosity changes drastically. For shear-thinning polymers, the average flow velocity in a well block is several orders of magnitude smaller than the flow velocity near the wellbore, especially when grid size is large. Using the average flow velocity for apparent viscosity calculation leads to an underestimate of well injectivity. Thus it is crucial to consider non-Newtonian effects in simulation of injectivity of polymer solutions.UTCHEM, a general purpose chemical flooding simulator developed at the University of Texas, uses a semi-analytical polymer injectivity model based on an extension of Peaceman's well model to non-Newtonian fluids. This model corrects the apparent viscosity in well index calculation and gives a more realistic estimate of polymer injectivity. However, the model needs to be programmed and implemented in a code. It is not practical for users of commercial simulators which do not have semi-analytical polymer injectivity model. Compared with UTCHEM, CMG-STARS, the thermal and advanced processes reservoir simulator developed by Computer Modeling Group, uses an apparent skin factor to account for non-Newtonian effects of power law fluids. The apparent skin factor can be used as an input parameter to the simulator.In this study, we revisited the analytical polymer injectivity model and proposed a general form for the calculation of apparent skin factor. The apparent skin factor is estimated using polymer rheological properties, grid size, wellbore radius, etc., and can apply to any polymer rheology. Simulation studies of polymer flooding cases were conducted to verify the concept of apparent skin factor. The results show that for shear-thinning polymers, the apparent skin factor can be estimated properly and correct the analytical well model to give close predictions compared to the semi-analytical polymer injectivity model and fine-grid simulation. The concept of apparent skin factor was proved to be useful for improving the accuracy of well injectivity calculations by commercial simulators. However, it is also worth pointing out that the apparent skin factor cannot be used for polymers which show shear-thickening behavior at high shear rates for cases of heterogeneous reservoir.

Research and Practice of the Early Stage Polymer Flooding on LD Offshore Oilfield

Literature survey shows that polymer flooding was generally conducted during high water-cut stage (WCT>80% to 90%). Even the first China Offshore polymer flooding project was carried out in SZ when water cut was 60%. By then, conduction of polymer flooding in early phase (WCT<10%) was just discussed in theory. For offshore oilfield, the treatment of water could be costly. Because polymer improves mobility ratio of replacement fluid over oil and sweep efficiency, less water is injected and less water is produced. So, we did enormous research about the polymer flooding on early stage by theoretical analysis, series of experiments and chemical flooding simulation. Based on these researches, we carried out the first field test of polymer flooding on early stage in LD. Single well polymer injection test was started in Mar 2006 when the water cut in the pattern was lower than 10%. After the trial, there were other 5 water injectors being converted to polymer injectors from 2007 to 2009. The polymer flooding controlled reserve was about 25,250,000 m3. For the early stage polymer flooding, the characteristics of the responses on producers were different from the case in which polymer flooding was conducted during high water cut stage. The water producing of the producers continued to rise up after polymer flooding, but the simulation research showed that the water cut increasing rate was lower than the rate during merely water flooding. In addition, we observed the drop-down on the water cut in some wells, such as A11, A12, A13, A15, etc. For the well A11, the highest water cut reduction reached 41% after the injectors (A5/A10) profiles controlled, and net incremental oil for A11 even reached 154,510 m3. By Dec 2014, the total incremental oil by polymer flooding was about 754,650 m3, and the stage oil recovery efficiency was enhanced by 3.0%. The polymer flooding is still effective, and we will get more oil from the polymer flooding.

Predicting microemulsion phase behavior using physics based HLD-NAC equation of state for surfactant flooding

Screening optimum formulation for surfactant flooding is usually time consuming, and correctly modeling microemulsion phase behavior is critical for chemical flooding simulation. This paper extends the physics based Hydrophilic Lipophilic Difference (HLD) Net Average Curvature (NAC) model, and comprehensively demonstrated its capabilities in predicting the optimum formulation and microemulsion phase behavior. This paper uses quantitatively characterized HLD parameters, accurately predicted four optimum surfactant formulations for a target reservoir. This paper measured surfactant head area of extended surfactants and further predicted the equilibrium interfacial tension of four phase behavior test. Comparing to the empirical Hand's rule phase behavior model, the HLD-NAC equation of state shows great advantages in helping formulation design and modeling microemulsion phase behavior. Surfactant flooding sandpack laboratory tests are also interpreted by UTCHEM chemical flooding simulator coupled with the HLD-NAC phase behavior model. The results indicate the significance of HLD-NAC equation of state in not only shorten the surfactant screening processes for formulators, but also predicting microemulsion phase behavior based on surfactant structure. A compositional reservoir simulator with such an equation of state will increase its predictability and hence help with the design of surfactant formulation.

Modeling dynamic fracture growth induced by non-Newtonian polymer injection

Injection of viscous polymer solutions can lead to excessive wellbore pressure and dynamic fracture growth near wellbore which hence increases polymer injectivity. Polymer rheology can also play an important role in affecting the fracture growth due to the dramatic variation of fluid velocity. However, current reservoir simulations for chemical flooding generally overlook the induced fracture, which may lead to inaccurate modeling of polymer floods. In this paper, we developed a new polymer-injection-induced fracture model based on the GdK fracture model, and implicitly coupled it to a general-purpose chemical flood simulator. Mathematical description of the fracture mechanics is presented with consideration of different polymer rheology. The new model is used to simulate polymer floods in a five-spot field. The fracture growth predicted by the new model is verified against a semi-analytical numerical fracture model. The simulation results using the new model indicate that the fracture growth has positive effects on improving injectivity, pressure drop, oil recovery, etc. Also, the simulations illustrate for the first time how polymer rheology affects fracture growth during polymer injection, e.g., without consideration of polymer shear-thickening can postpone the prediction of fracture initiation. The simulations successfully explain why polymer can be economically injected in real polymer fields. The new simulator can be used to optimize polymer floods concerning polymer injectivity, impact of fracture growth on sweeping efficiency, flow out of zone, and others.

A Four-Phase Chemical/Gas Model in an Implicit-Pressure/Explicit-Concentration Reservoir Simulator

SummaryThis study describes a general four-phase model developed for gas/oil/water/microemulsion (ME) coexisting at local equilibrium. The original framework of a chemical reservoir simulator is used to implement the model. This model represents a new method to couple the black-oil model with surfactant-phase behavior [i.e., the Hand (1939) rule]. The Hand (1939) rule is used to capture the equilibrium among surfactant, oil, and water species as a function of salinity and species concentrations for oil/water/ME phases. The interphase-mass transfer between gas/oil in the presence of the ME phase is calculated at the equilibrium between phases. For this purpose, a new volume-balance equation is derived to consider the pressure equation for compressible and real mixing in such a model. Hence, the pressure equation is derived by extending the black-oil model to a pseudocompositional model for a wide range of components (water, oil, surfactant, polymer, anion, cation, alcohol, and gas). Mass-balance equations are then solved for the components to calculate the concentration. Finally, we implemented the coupled surfactant and black-oil phase-behavior models and the fluid-flow formulations in an implicit-pressure/explicit-concentration (IMPEC) chemical-flooding simulator: UTCHEM (2011) four-phase.The results were verified against existing reservoir simulators for two different three-phase test cases comprising gas/oil/water and oil/water/ME. Then, the performance of the model in the presence of four phases was tested and validated against coreflood experimental data. The results showed that the new phase behavior and the fluid-flow equations are consistent with three-phase reservoir simulators for the case studies.In addition, the findings of this work can be used to model and capture the mechanisms behind processes such as micellar slug foam and alkaline and surfactant flooding into saturated (gas cap) reservoirs as well as alternating or coinjection of surfactant and gas processes. Modeling of such processes is far from satisfactory in existing phase behavior and fluid-flow simulators.

A chemical EOR benchmark study of different reservoir simulators

Interest in chemical EOR processes has intensified in recent years due to the advancements in chemical formulations and injection techniques. Injecting Polymer (P), surfactant/polymer (SP), and alkaline/surfactant/polymer (ASP) are techniques for improving sweep and displacement efficiencies with the aim of improving oil production in both secondary and tertiary floods. There has been great interest in chemical flooding recently for different challenging situations. These include high temperature reservoirs, formations with extreme salinity and hardness, naturally fractured carbonates, and sandstone reservoirs with heavy and viscous crude oils.More oil reservoirs are reaching maturity where secondary polymer floods and tertiary surfactant methods have become increasingly important. This significance has added to the industry's interest in using reservoir simulators as tools for reservoir evaluation and management to minimize costs and increase the process efficiency. Reservoir simulators with special features are needed to represent coupled chemical and physical processes present in chemical EOR processes. The simulators need to be first validated against well controlled lab and pilot scale experiments to reliably predict the full field implementations.The available data from laboratory scale include 1) phase behavior and rheological data; and 2) results of secondary and tertiary coreflood experiments for P, SP, and ASP floods under reservoir conditions, i.e. chemical retentions, pressure drop, and oil recovery. Data collected from corefloods are used as benchmark tests comparing numerical reservoir simulators with chemical EOR modeling capabilities such as STARS of CMG, ECLIPSE-100 of Schlumberger, REVEAL of Petroleum Experts. The research UTCHEM simulator from The University of Texas at Austin is also included since it has been the benchmark for chemical flooding simulation for over 25 years.The results of this benchmark comparison will be utilized to improve chemical design for field-scale studies using commercial simulators. The benchmark tests illustrate the potential of commercial simulators for chemical flooding projects and provide a comprehensive table of strengths and limitations of each simulator for a given chemical EOR process. Mechanistic simulations of chemical EOR processes will provide predictive capability and can aid in optimization of the field injection projects. The objective of this paper is not to compare the computational efficiency and solution algorithms; it only focuses on the process modeling comparison.

Low-Salinity Surfactant Flooding—A Multimechanistic Enhanced-Oil-Recovery Method

SummaryWe have applied UTCHEM-IPhreeqc to investigate low-salinity (LS) waterflooding and LS surfactant (LSS) flooding. Numerical-simulation results were compared with laboratory experiments reported by Alagic and Skauge (2010). UTCHEM-IPhreeqc combines the UTCHEM numerical chemical-flooding simulator with IPhreeqc, the United States Geological Survey geochemical model. The IPhreeqc model was coupled to UTCHEM to model LS waterflooding as a function of geochemical reactions. The surfactant coreflood experiments were performed in vertical cores without using polymer or other mobility-control agents. These experiments were performed at a velocity greater than the critical velocity for a gravity-stable flood. After history matching the experiments, additional numerical simulations of surfactant floods at the critical velocity were run to estimate the performance under stable conditions. We also simulated a surfactant flood at higher salinity with lower interfacial tension (IFT) and compared the results with the LSS flood. These results provide new insights into LS waterflooding and surfactant flooding. Addition of surfactants prevents the retrapping of oil that was initially mobilized using LS-brine injection. The results show that the proper selection of surfactant and the design of the surfactant flood might surpass the potential benefits of LS waterflooding in terms of both higher oil recovery and lower cost. Specially, a more-effective method is expected in a stable design with no preflood.

Numerical Simulation and Optimization of Enhanced Oil Recovery by the In Situ Generated CO2Huff-n-Puff Process with Compound Surfactant

This paper presents the numerical investigation and optimization of the operating parameters of the in situ generated CO2Huff-n-Puff method with compound surfactant on the performance of enhanced oil recovery. First, we conducted experiments of in situ generated CO2and surfactant flooding. Next, we constructed a single-well radial 3D numerical model using a thermal recovery chemical flooding simulator to simulate the process of CO2Huff-n-Puff. The activation energy and reaction enthalpy were calculated based on the reaction kinetics and thermodynamic models. The interpolation parameters were determined through history matching a series of surfactant core flooding results with the simulation model. The effect of compound surfactant on the Huff-n-Puff CO2process was demonstrated via a series of sensitivity studies to quantify the effects of a number of operation parameters including the injection volume and mole concentration of the reagent, the injection rate, the well shut-in time, and the oil withdrawal rate. Based on the daily production rate during the period of Huff-n-Puff, a desirable agreement was shown between the field applications and simulated results.

A Novel Method To Model Low-Salinity-Water Injection in Carbonate Oil Reservoirs

Summary There are few low-salinity-water-injection (LSWI) models proposed for carbonate rocks, mainly because of incomplete understanding of complex chemical interactions of rock/oil/brine. This paper describes a new empirical method to model the LSWI effect on oil recovery from carbonate rocks, on the basis of the history matching and validation of recently published corefloods. In this model, the changes in the oil relative permeability curve and residual oil saturation as a result of the LSWI effect are considered. The water relative permeability parameters are assumed constant, which is a relatively fair assumption on the basis of history matching of coreflood data. The capillary pressure is neglected because we assumed several capillary pressure curves in our simulations in which it had a negligible effect on the history-match results. The proposed model is implemented in the UTCHEM simulator, which is a 3D multiphase flow, transport, and chemical-flooding simulator developed at The University of Texas at Austin (UTCHEM 2000), to match and predict the multiple cycles of low-salinity experiments. The screening criteria for using the proposed LSWI model are addressed in the paper. The developed model gives more insight into the oil-production potential of future waterflood projects with a modified water composition for injection.

Simulation of single well tracer tests for surfactant–polymer flooding

A single well tracer test (SWTT) is a method to investigate the residual oil saturation near the wellbore. It presents an important tool to evaluate enhanced oil recovery (EOR) processes. For EOR evaluation, two SWTTs (one before and another after EOR application) can be used to estimate the reduction in S or due to the application of an EOR process. The change in S or is a measure of the incremental oil recovery of the applied EOR technology. In this work, we use University of Texas Chemical Flooding Simulator to guide the design of SWTTs that will be later run to evaluate chemical flooding potential. First, we perform thorough sensitivity simulations using an idealistic homogeneous model. Second, we perform simulations using a realistic model, which was generated based on the selected evaluation well (Well-X). In the sensitivity runs, we investigate the effects of various parameters such as partitioning coefficients, reaction rates, injection rates, injection volumes, and shut-in times. Based on the results, we provide recommendations for designing the SWTTs. Furthermore, simulations using the Well-X model suggest an incremental oil recovery factor of 14.7 % OOIP due to surfactant-polymer flooding. This is consistent with lab data and provides assurance to multi-well field applications. More importantly, those simulation results support the utility of SWTTs in evaluating chemical flooding potential. Based on the results, we expect to observe distinct back-production peaks, clear separation between the reactive and product tracers, and measurable variation in separation due to chemical EOR application that can be categorically analyzed.

Four-Fluid-Phase, Fully Implicit Simulation of Surfactant Flooding

Summary The Microemulsion phase behavior model based on oleic/aqueous/surfactant pseudophase equilibrium, commonly used in chemical-flooding simulators, is coupled to Gas/Oil/Water phase equilibrium in our new four-fluid-phase, fully implicit in-house research reservoir simulator (IHRRS) (Moncorgé et al. 2012). The method consists of splitting the equilibrium into two stages, in which all the components other than surfactant are equilibrated first—by use of a black-oil, K-value, or equation of state (EOS) model—and the resulting Gas, Oil, and Water phases are then lumped into pseudophases to be equilibrated by use of the Microemulsion model. This subdivision in stages is conceptual, and at each converged timestep the four phases (Gas, Oil, Water, and Microemulsion, when simultaneously present) will be in equilibrium with each other. The fluid properties (such as densities, viscosities, and interfacial tensions) and rock/fluid properties (such as relative permeabilities) required in the transport equations are evaluated with models from well-known industrial or academic simulators. Surfactant flooding being usually implemented as a tertiary recovery mechanism, on fields for which complete models that we do not wish to modify already exist, particular care is devoted to ensuring continuity of the physics at the onset of surfactant injection. Our code is first validated against a reference academic chemical-flooding simulator, on a 1D, three-fluid-phase (Oil/Water/Microemulsion) coreflood. Second, as application examples where it is necessary to account for four phases in equilibrium, we consider a scenario where the chemical flood is preceded by a vaporizing Gas drive, as well as a scenario where dissolved gas is released by the Oil during the flooding process. Some aspects of our implementation, such as numerical dispersion vs. timestep length and nonlinear convergence, are also discussed; in particular, we show that numerical performance is not degraded by the four-phase equilibrium.

Development of an Analytical Injectivity Model for Non-Newtonian Polymer Solutions

Summary In applications of polymer flood for enhanced oil recovery (EOR), polymer injectivity is of great concern because project economics is sensitive to injection rates. In-situ non-Newtonian polymer rheology is the most crucial factor that affects polymer injectivity. There are several ongoing polymer-injection field tests in which the field injectivities differ significantly from the simulation forecasts. We have developed an analytical model to more accurately calculate and predict polymer injectivity during the field projects to help with optimum injection strategies. Significant viscosity variations during polymer flood occur in the vicinities of wellbores where velocities are high. As the size of a wellblock increases, velocity smears, and thus polymer injectivity is erroneously calculated. In the University of Texas Chemical Flooding Simulator (UTCHEM), the solution was to use an effective radius to capture the “grid effect,” which is empirical and impractical for large-scale field simulations with several hundred wells. Another approach is to use local grid refinement near wells, but this adds to the computational cost and limits the size of the problem. An attractive alternative to previous approaches is to extend the Peaceman well model (Peaceman 1983) to non-Newtonian polymer solutions. The polymer rheological model and its implementation in UTCHEM were validated by simulating single-phase polymer injectivity in coreflood experiments. On the basis of the Peaceman well model and UTCHEM polymer rheological models covering both shear-thinning and shear-thickening polymers, an analytical polymer-injectivity model was developed. The analytical model was validated by comparing results of different gridblock sizes and radial numerical simulation. We also tested a field case by comparing results of a fine-grid simulation and its upscaled coarse-grid model. A pilot-scale polymer flood was simulated to demonstrate the capability of the proposed analytical model. The model successfully captured polymer injectivity in all these cases with no need to introduce empirical parameters.

An artificial neural network model for predicting the recovery performance of surfactant polymer floods

Abstract A supervised feed-forward back-propagation neural network model has been developed for estimating the recovery performance of a reservoir subjected to a surfactant–polymer (SP) flood. The optimal network paradigm has been designed by conducting extensive experimentation on the proper number of hidden layers, and of neurons in each of these layers. The optimal multi-layered network topology consists of an 18-neuron input layer, three 11-neuron hidden layers, and a four-neuron output layer. The network input consists of 18 dimensionless groups that critically dominate the displacement efficiency of SP floods. These groups account for the effects of surfactant slug size, polymer slug size, surfactant concentration, surfactant/oil bank mobility ratio, polymer/surfactant mobility ratio, surfactant and polymer adsorption, interfacial tension, reservoir heterogeneity, relative permeabilities, capillary pressure, waterflood residual saturations, the optimal salinity in the three-phase region, gravity, and rock wettability. Principal component analysis indicated that all of these 18 dimensionless groups were essential for scaling SP floods. The network output consists of the oil recovery at 0.75, 1.5, and 2.25 pore volumes injected (PVI), along with estimates of the breakthrough dimensionless time. The network model has been trained on a data set consisting of 499 simulations, generated using a three-dimensional compositional chemical flood simulator. Tertiary chemical floods constitute 90% of the simulation runs. The rest of the simulation cases are secondary chemical injections. The optimal network architecture was able to estimate back the oil recovery from the training set within 1.5% average absolute error. The ANN model was able to predict the oil recovery for a blind-test data-set of 125 simulated field cases within approximately 3% average absolute error. With these remarkable results, the ANN model outperformed the non-linear multivariate models available in the literature. Compared to using extensive numerical modeling based on the detailed knowledge of the oil reservoir, the use of the introduced ANN model saves significant amount of time needed for the performance prediction of surfactant–polymer floods. The ANN model may, therefore, be used as a valuable tool for the preliminary assessment of oil reservoirs with respect to their suitability for surfactant–polymer flood.

Modeling of Pressure and Solution Gas for Chemical Floods

Summary The goal of surfactant/polymer (SP) flooding is to reduce interfacial tension (IFT) between oil and water so that residual oil is mobilized and high recovery is achieved. The optimal salinity and optimal solubilization ratios that correspond to ultralow IFT have recently been shown, in some cases, to be a strong function of the methane mole fraction in the oil at reservoir pressure. We incorporate a recently developed methodology to determine the optimal salinity and solubilization ratio at reservoir pressure into a chemical-flooding simulator (UTCHEM). The proposed method determines the optimal conditions on the basis of density estimates by use of a cubic equation of state (EOS) and measured phase-behavior data at atmospheric pressure. The microemulsion phase-behavior (Winsor I, II, and III) are adjusted on the basis of this predicted optimal salinity and solubilization ratio in the simulator. Parameters for the surfactant phase-behavior equation are modified to account for these changes, and the trend in the equivalent alkane carbon number (EACN) is automatically adjusted for pressure and methane content in each simulation gridblock. We use phase-behavior data from several potential SP floods to demonstrate the new implementation. The implementation of the new phase-behavior model into a chemical-flooding simulator allows for a better design of SP floods and more-accurate estimations of oil recovery. The new approach could also be used to handle free gas that may form in the reservoir; however, the SP-flood simulation when free gas is present is not the focus of this paper. We show that not accounting for the phase-behavior changes that occur when methane is present at reservoir pressure can greatly affect the oil recovery of SP floods. Improper design of an SP flood can lead to production of more oil as a microemulsion phase than as an oil bank. This paper describes the procedure to implement the effect of pressure and solution gas on microemulsion phase behavior in a chemical-flooding simulator, which requires the phase-behavior data measured at atmospheric pressure.

Formulations for a three-phase, fully implicit, parallel, EOS compositional surfactant–polymer flooding simulator

As chemical flooding becomes more attractive due to increased oil prices and advances in chemical production, the need for a powerful and comprehensive chemical flood simulator increases. A key requirement of success in chemical flooding is optimization of the design in field scale using appropriate resolution which may be significantly more than the resolution adequate for waterflood modeling. This requires immense computation power and parallelization of the simulation. Another key requirement is comprehensive oil/brine/surfactant phase behavior modeling for exploring the performance of any attractive design in field scale. The simulator described by Han et al. (2007) was limited to only two-phases and under optimum Type I phase behavior.This paper describes the formulations for a three-phase, fully implicit, parallel, EOS compositional chemical flooding simulator with comprehensive compositional phase behavior model as a function of salinity. Presented formulation assumes that the reservoir is isothermal and no free gas or alkali is present. The improved simulator can handle the full transition from Type I to Type III to Type II microemulsion phase behavior. The parallel processing capability combined with fully implicit time stepping allows high-resolution full field scale chemical flooding simulations. The fully implicit simulation results are validated against an IMPEC chemical flooding simulator. This generalization required a substantial reformulation of the equations and extending the three phase water/oil/gas to up to four phases of water/oil/gas/microemulstion with corresponding physical property models. The full coupling of chemical with EOS model will provide a general framework to model the effect of live oil and pressure on surfactant phase behavior.This paper presents a 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.

Chemical flooding optimisation using the experimental design approach and response surface methodology

One of the most effective methods of enhanced oil recovery is chemical flooding. This process can significantly increase the oil recovery; however, it involves high risks and uncertainties. We determined the optimal chemical flooding design, which is the maximum of the net present value (NPV), using the UT_IRSP platform and an in-house chemical flooding simulator, as well as the application of experimental design and respond surface for a particular oil reservoir. Surfactant and polymer mass, kv/kh ratio, salinity of polymer drive, and salinity of post-water flood were variables used in our optimisation calculations. We found that the interaction between the surfactant concentration and surfactant slug size is the most important term affecting the NPV. Therefore, the oil recovery was improved by 8% of the original oil in place (OOIP) in a designed chemical flooding case scenario. Also, the NPV with tax and royalty increased notably from 6.42 MM USD to 8.08 MM USD for the 50 USD/bbl of oil price.

Transport Model Implementation and Simulation of Microgel Processes for Conformance and Mobility Control Purposes

Various forms of microgels, which are soft and flexible micrometer-scale globules consisting of water-soluble polymer networks, are available for near-wellbore conformance control and deep-penetrating mobility control purposes. Due to various descriptions of the nature and applicability of different microgel systems and conflicting reports regarding their effectiveness, a critical literature review is first performed on the characterization and efficacy of the colloidal dispersion gel (CDG), the preformed particle gel (PPG), the relative permeability-modifying microgel, and the temperature-sensitive and pH-sensitive microgels. We then present an implementation of a model for microgel transport and retention in our 3-D chemical-flooding simulator. In addition, preliminary simulation results on the effectiveness of the CDG and PPG hydrogels are presented. The effectiveness of various microgel systems can be quantitatively tested using the reservoir simulator with the microgel transport and retention model. ...

Investigation of polymer and surfactant-polymer injections in South Slattery Minnelusa Reservoir, Wyoming

This paper presents an investigation of the enhanced oil recovery (EOR) potential in the South Slattery Minnelusa formation. The South Slattery Field, which is characterized by low permeability and high saline brine, is stepping into the economic limits of secondary waterflood. A chemical flooding simulation model which was based on experimental parameters was set up for the potential investigation of EOR. Both polymer and surfactant-polymer floods were investigated. The recoveries of these EOR methods are presented, and the development efficiencies are analyzed.