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Results
Abstract Surfactant flooding is a promising technique that can reduce interfacial tension (IFT) between oil and water to ultra-low values, mobilizing previously trapped oil. For reservoirs at moderate to high pressures, understanding and modeling how pressure affects the phase behavior of a surfactant-brine-oil system is crucial to the design and implementation of efficient/cost-effective surfactant flooding project. Typical phase behavior experiments and models are done only at low pressures. Objective of this paper is to comprehensively model realistic range of pressure, temperature, and other parameters, using hydrophilic-lipophilic deviation (HLD) and net-average curvature (NAC) based equation-of-state (EoS). This paper shows how to model an anionic surfactant system consisting of a surfactant, co-solvent, brine (up to 10 wt%) and synthetic oil over a large range in pressure (up to 8000 psi), temperature (up to 60 °C), and compositions. The model is developed from measurements made using a high-pressure PVT cell. Parameters such as the oil-water ratio and the surfactant concentration were varied in ternary space under both atmospheric and reservoir conditions. Selected experimental results were then matched to our new EoS based on HLD-NAC. The advantage of this approach is that the tuned model can predict phase behavior in a unified way for all experiments. The pressure and temperature scans show that pressure has a significant effect on the surfactant microemulsion phase behavior, shifting it from an optimal three-phase system at low pressure to a nonoptimal two-phase system at high pressure. Further, multiple scans at different oil-water ratios show a shift in the optimum indicating that phase behavior partitioning of the various components is changing with oil saturation. In addition, we show how to determine the optimum pseudocomponent composition for such a ternary pseudocomponent system. We further show that the micellar correlation length in the three-phase region can be predicted well using linear functions with temperature, pressure, and salinity. The change in characteristic length is a critical aspect of modeling the phase behavior accurately with the HLD-NAC EoS, and ultimately to predict and scale the phase behavior for other reservoir conditions. We show that there is a well-defined optimum 3D surface in the pressure, temperature, and salinity space that can aid the design of surfactant floods for field use and reduce the risk of those projects. Further, the use of the tuned HLD-NAC EoS can define and reduce the number of experiments needed to model the optimum owing to a unified EoS prediction of the phase behavior. When input into a numerical simulator, the improved prediction of the size and shape of the two-phase lobes with changing pressure, temperature, and salinity will also improve estimations of surfactant slug size needed to maintain ultra-low IFTs.
Abstract Saturation distributions exhibiting unphysical "checkerboard" patterns, time-step size sensitivity, and slow convergence in certain instances are observed in a fully implicit surfactant simulator that is based on an industry-wide accepted formulation. In this paper, we discuss methods to address each of the above conditions and hereby achieve a robust algorithm with favorable convergence characteristics. The proposed remedies are result of in-depth studies of the physics of micro-emulsion appearance and disappearance as well as detailed analysis of the numerical convergence difficulty. Our method considers wide ranges of solution variables in a typical surfactant flood simulator and critical key parameters identified by flash algorithm [Han, et al. 2017] and general non-linear solver. The details of the improved formulation are provided and should enable readers to replicate all these results. Identifying grid cells in a reservoir model where and when the micro-emulsion phase appears is a key capability in the modeling of surfactant phase behavior. The Critical Micelle Concentration (CMC) is the commonly accepted triggering criterion for forming the micro-emulsion phase. We have observed unphysical "checkerboard" saturation patterns for several cases where water mobility is greater than oil mobility when using the conventionally accepted CMC calculation method. We have analyzed the reasons for this unphysical solution and propose a new CMC definition to ensure physically consistent simulation results. Typical CMC values for surfactant flood are in the range of 10 to 10. This requires surfactant concentration to be solved more accurately relative to other component concentrations as it directly affects micro-emulsion phase disappearance. The simulation results may vary with time-step sizes not only from the time-truncation errors but, more importantly, from the accuracy of the solved surfactant concentration for each time-step. Special treatments are introduced to reduce the time-step size sensitivity in our simulator. For cases with cation exchange, slow convergence is observed as the corresponding governing equations form an ill-conditioned matrix for cells with small surfactant concentration. An extra term is introduced into the formulation to speed up the convergence rate without changing the model behavior.
Abstract Many conventional surfactant-brine-oil phase behavior tests are conducted under ambient pressure conditions without the solution gas. It is known that the solution gas lowers the optimum salinity. Researchers often mix toluene (or cyclohexane) with the dead oil and form a surrogate oil to mimic the live oil. The objective of our work is to study the effect of gas and toluene on phase behavior, and to provide the proper amount of toluene to be mixed to mimic the live oil. Effects of toluene in surrogate oil and solution gas in live oil are examined by hydrophilic-lipophilic difference and net average curvature (HLD-NAC) structural model simulation and the equivalent alkane carbon number (EACN). Experimental values from literature and our experiments are also examined to compare those with the simulation results. For the simulation, both the mole fraction and mass fraction were used to calculate mixture EACN and examine the effect of additional components. HLD-NAC simulation results showed that the mass fraction-based simulation is more accurate (~7% error) than mole fraction-based simulation (~19% error) with a toluene EACN of 1. For larger molecules like toluene in surrogate oil, EACN using mole fraction also works with a toluene EACN of 5.2. The EACN of the surrogate oil should match the EACN of the live oil to determine the proper amount of toluene in the surrogate oil.
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas > Upstream (1.00)
Abstract Most chemical EOR formulations are surfactant mixtures, but these mixtures are usually modeled as a single pseudo-component in reservoir simulators. However, the composition of an injected surfactant mixture changes as it flows through a reservoir. For example, as the mixture is diluted, the CMC changes, which changes both the adsorption of each surfactant component and the microemulsion phase behavior. Modeling the physical chemistry of surfactant mixtures in a reservoir simulator was found to be more significant than anticipated and is needed to make accurate reservoir-scale predictions of both chemical floods and the use of surfactants to stimulate shale wells.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.48)
- Geology > Mineral (0.46)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Chemical flooding methods (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
Development of Surfactant Formulation for Harsh Environment
Pinnawala, Gayani (Chevron Energy Technology Company) | Nizamidin, Nabijan (Chevron Energy Technology Company) | Spilker, Kerry (Chevron Energy Technology Company) | Linnemeyer, Harold (Chevron Energy Technology Company) | Malik, Taimur (Chevron Energy Technology Company) | Dwarakanath, Varadarajan (Chevron Energy Technology Company)
Abstract Good phase behavior is critical for identifying high performance surfactant formulations for coreflood recovery. For conventional CEOR projects, good phase behavior entails high solubilization parameters, rapid equilibration to low viscosity microemulsions and aqueous stability of aqueous surfactant mixtures. For reservoirs with harsh conditions, i.e high temperature (> 90°C), high salinity (>50,000 ppm TDS), high divalent ions (> 1500 ppm TDS), high GOR (>150) and presence of H2S, developing formulations with good phase behavior is challenging. Several carbonate reservoirs have conditions as outlined above and the scarcity of formulations that are stable in the above-described conditions makes surfactant applications challenging. We present results that show the development of surfactant formulations that show good behavior under harsh conditions. We validate the performance with a combination of phase behavior, thermal stability, and coreflood experiments and show that high-performance surfactants can be developed for harsh reservoir conditions.
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas > Upstream (1.00)
A Continuous and Predictive Viscosity Model Coupled to a Microemulsion Equation-Of-State
Khodaparast, Pooya (Department of Energy and Mineral Engineering and EMS Energy Institute, The Pennsylvania State University, University Park) | Johns, Russell T. (Department of Energy and Mineral Engineering and EMS Energy Institute, The Pennsylvania State University, University Park)
Abstract Surfactant floods can attain high oil recovery if optimum conditions with ultra-low interfacial tensions (IFT) are achieved in the reservoir. A new equation-of-state (EoS) phase behavior model based on the hydrophilic-lipophilic difference (HLD-NAC) has been shown to fit and predict phase behavior data continuously throughout the Winsor I, II, III, and IV regions. The state-of-the-art for viscosity estimation, however, uses empirical non-predictive models based on fits to salinity scans, even though other parameters change, such as the phase number and compositions. In this paper, we develop the first-of-its-kind microemulsion viscosity model that gives continuous viscosity estimates in composition space. This model is coupled to our existing HLD-NAC phase behavior EoS. The results show that experimentally measured viscosities in all Winsor regions (two and three-phase) are a function of phase composition, temperature, pressure, salinity, and EACN. More specifically, microemulsion viscosities associated with the three-phase invariant point have an "M" shape as formulation variables change, such as from a salinity scan. The location and magnitude of viscosity peaks in the "M" are predicted from two percolation thresholds after tuning to viscosity data. These percolation thresholds as well as other model parameters change linearly with alkane chain length (EACN) and brine salinity. We also show that the minimum viscosity in the "M' shape correlates linearly with alkane chain length (EACN) or viscosity ratio. Other key parameters in the model are also shown to linearly correlate with EACN and brine salinity Based on these correlations, two and three-phase microemulsion viscosities are determined in five-component space (surfactant, two brine, and two oil components) independent of flash calculations. Phase compositions from the EoS flash calculations are input into the viscosity model. Fits to experimental data are excellent, as well as viscosity predictions for salinity scans not used in the fitting process.
Abstract The objective of this research was to develop a model to predict the optimum phase behavior of chemical formulations for a given oil based on the molecular structure of the surfactants and co-solvents. The model is sufficiently accurate to provide a useful guide to an experimental testing program for the development of chemical EOR formulations. There are thousands of combinations of surfactants and co-solvents that could be tested for each oil, so even approximate predictions are very useful in terms of reducing the time and effort required for testing and for prioritizing the chemical combinations to test that are most likely to yield ultra-low IFT at reservoir conditions. The effects of changing molecular structures (e.g. swapping head groups, swapping hydrophobes, increasing the length of hydrophobes, increasing the number of PO and EO groups, adjusting the ratios of surfactants) are shown. The variables with the greatest impact on the optimum salinity and solubilization ratio were identified, and methods are proposed to shift the optimum salinity and the optimum solubilization ratios in any desired direction. The structure-property model was developed and tested using a large dataset consisting of 684 microemulsion phase behavior experiments using 24 oils. The chemical formulations used 85 surfactants and 18 co-solvents in various combinations. Both optimum salinity and optimum solubilization ratio (and thus IFT) are modeled whereas other models have focused almost exclusively on the optimum salinity. Predicting the optimum solubilization ratio is actually of more value because of its relationship to IFT. The models include the effects of co-solvent partitioning, soap formation and the molecular structure of both the surfactants and co-solvents.
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas > Upstream (1.00)
Abstract This paper summarizes BP's Alaskan viscous oil resource appraisal strategy to de-risk viscous oil resource progression with a goal to improve recovery factor by 10%. A key to recovery improvement is application of improved oil recovery/enhanced oil recovery (IOR/EOR) methods. However, even after detailed studies, moving to the next stage including field pilots is not always easy in the mature and remote Alaskan North Slope. The paper also covers BP's Alaskan viscous oil technology strategy, extraction technologies selection, simulation and analytical studies, laboratory studies, and field trials for various shortlisted methods. A comprehensive study strategy conducted for progressing chemical EOR processes is discussed. The paper also addresses the challenges of obtaining new core and fluid samples for laboratory studies and logistical and economic considerations for field trials due to location and weather conditions in this part of the world.
- North America > Canada (0.68)
- Europe > United Kingdom (0.66)
- North America > United States > Alaska > North Slope Borough > Prudhoe Bay (0.28)
- Geology > Mineral (0.93)
- Geology > Geological Subdiscipline > Geomechanics (0.68)
- North America > United States > Alaska > Schrader Bluff Formation (0.99)
- North America > United States > Alaska > North Slope Basin > Milne Point Field > Kuparuk Formation (0.99)
- North America > United States > Alaska > North Slope Basin > Duck Island Field > Endicott Field > Kekiktuk Formation (0.99)
- (8 more...)
Abstract Current HLD-NAC theory and most simulators represent multicomponent mixtures with three lumped components, where the excess phases are also assumed pure. This can cause significant errors, and discontinuities in chemical flooding simulation for surfactant mixtures. We coupled the HLD-NAC and pseudo-phase models to develop an EOS for microemulsions where surfactant, polymer, alcohol, alkali and monovalent/divalent ions can partition differently into the excess phases and microemulsion phase as temperature and pressure are changed. We develop a pseudo-phase model to calculate partitioning of components between lumped components or namely pseudo-phases. The pseudo-phase model is based on a transformed composition space. The partitioning model is based on different mechanisms such as cation exchange like reactions for ions and surfactant hydration properties. Next, the three-pseudo-component HLD-NAC EOS is used to calculate curvature of the interface and microemulsion phase composition based on pseudo-phases. That is, the microemulsion phase consists of a curved ruled surface between water and oil pseudo-phases. Polymer partitioning is updated based on micelle radius. Finally, the phase compositions are converted back from pseudo-phase space to the original composition space. This model is the first comprehensive and mechanistic flash calculation algorithm based on HLD-NAC and pseudo-phase theory to calculate microemulsion properties for mixtures without the assumption of pure excess phases. This algorithm allows for modeling of the chromatographic separation of surfactant, soap, alcohol, alkali and polymer components in chemical flooding processes. Current microemulsion models usually ignore the differing partitioning of components between excess and microemulsion phases, generating discontinuities that slow computational time and adversely impact accuracy.
Abstract Favorable microemulsion rheology is required for achieving low surfactant retention and economic viability of chemical EOR. Co-solvents play a pivotal role in obtaining favorable microemulsion rheology as well as many other aspects of chemical EOR. We measured the partitioning of co-solvents between phases to better understand their behavior and how to select the best co-solvent for chemical EOR. There is an optimal co-solvent partition coefficient for microemulsion systems. Commercial co-solvents used for chemical EOR are actually mixtures of different components. We used HPLC to measure the partitioning of the constitutive components of phenol ethoxylate co-solvents between oil and water phases and between microemulsion and excess oil and water phases. These measurements show that the components partition independently and the partitioning of individual components is often different from the average. The co-solvent partition coefficients between oil and water were systematically evaluated as functions of the number of ethylene oxide groups, number of propylene oxide groups, temperature, salinity, and the equivalent alkane carbon number (EACN) of the oil. Novel alkoxylate co-solvents were also evaluated for chemical EOR. The novel alkoxylate co-solvents can be more effectively tailored to match the characteristics of different crude oils. Coreflood experiments were conducted to investigate co-solvent transport and retention. Co-solvents were identified that showed excellent performance and low retention.
- North America > United States > Oklahoma (0.29)
- North America > United States > Texas (0.28)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas (1.00)