Fast Upscaling of Polymer Flood Simulations Using Fractional Flow and Scaled Mobilities

2021 ◽  
Author(s):  
Hasan Al-Ibadi ◽  
Karl Stephen ◽  
Eric Mackay
Keyword(s):  
Relative Permeability ◽  
Control Method ◽  
Pressure Profile ◽  
Polymer Flooding ◽  
Mobility Control ◽  
Scale Model ◽  
Fine Scale ◽  
Fractional Flow ◽  
Water Viscosity ◽  
Flow Mobility

Abstract We introduce a pseudoisation method to upscale polymer flooding in order to capture the flow behaviour of fine scale models. This method is also designed to improve the predictability of pressure profiles during this process. This method controls the numerical dispersion of coarse grid models so that we are able to reproduce the flow behaviour of the fine scale model. To upscale polymer flooding, three levels of analysis are required such that we need to honour (a) the fractional flow solution, (b) the water and oil mobility and (c) appropriate upscaling of single phase flow. The outcome from this analysis is that a single pseudo relative permeability set that honours the modification that polymer applies to water viscosity modification without explicitly changing it. The shape of relative permeability can be chosen to honour the fractional flow solution of the fine scale using the analytical solution. This can result in a monotonic pseudo relative permeability set and we call it the Fractional-Flow method. To capture the pressure profile as well, individual relative permeability curves must be chosen appropriately for each phase to ensure the correct total mobility. For polymer flooding, changes to the water relative permeability included the changes to water viscosity implicitly thus avoiding the need for inclusion of a polymer solute. We call this type of upscaling as Fractional-Flow-Mobility control method. Numerical solution of the upscaled models, obtained using this method, were validated against fine scale models for 1D homogenous model and as well as 3D models with randomly distributed permeability for various geological realisations. The recovery factor and water cut matched the fine scale model very well. The pressure profile was reasonably predictable using the Fractional-Flow-Mobility control method. Both Fractional-Flow and Fractional-flow-Mobility control methods can be calculated in advance without running a fine scale model where the analysis is based on analytical solution even though produced a non-monotonic pseudo relative permeability curve. It simplified the polymer model so that it is much easier and faster to simulate. It offers the opportunity to quickly predict oil and water phase behaviour.

scholarly journals Experimental investigation of the displacement flow mechanism and oil recovery in primary polymer flood operations

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Ruissein Mahon ◽  
Gbenga Oluyemi ◽  
Babs Oyeneyin ◽  
Yakubu Balogun
Keyword(s):  
Relative Permeability ◽  
Oil Recovery ◽  
Polymer Flooding ◽  
Mobility Control ◽  
List Type ◽  
Motor Oil ◽  
Flow Mechanism ◽  
Fractional Flow ◽  
Recovery Method ◽  
Polymer Flood

Abstract Polymer flooding is a mature chemical enhanced oil recovery method employed in oilfields at pilot testing and field scales. Although results from these applications empirically demonstrate the higher displacement efficiency of polymer flooding over waterflooding operations, the fact remains that not all the oil will be recovered. Thus, continued research attention is needed to further understand the displacement flow mechanism of the immiscible process and the rock–fluid interaction propagated by the multiphase flow during polymer flooding operations. In this study, displacement sequence experiments were conducted to investigate the viscosifying effect of polymer solutions on oil recovery in sandpack systems. The history matching technique was employed to estimate relative permeability, fractional flow and saturation profile through the implementation of a Corey-type function. Experimental results showed that in the case of the motor oil being the displaced fluid, the XG 2500 ppm polymer achieved a 47.0% increase in oil recovery compared with the waterflood case, while the XG 1000 ppm polymer achieved a 38.6% increase in oil recovery compared with the waterflood case. Testing with the motor oil being the displaced fluid, the viscosity ratio was 136 for the waterflood case, 18 for the polymer flood case with XG 1000 ppm polymer and 9 for the polymer flood case with XG 2500 ppm polymer. Findings also revealed that for the waterflood cases, the porous media exhibited oil-wet characteristics, while the polymer flood cases demonstrated water-wet characteristics. This paper provides theoretical support for the application of polymer to improve oil recovery by providing insights into the mechanism behind oil displacement. Graphic abstract Highlights The difference in shape of relative permeability curves are indicative of the effect of mobility control of each polymer concentration. The water-oil systems exhibited oil-wet characteristics, while the polymer-oil systems demonstrated water-wet characteristics. A large contrast in displacing and displaced fluid viscosities led to viscous fingering and early water breakthrough.

Scaling Up Low-Salinity Waterflooding in Heterogenous Reservoirs

SPE Journal ◽  
2021 ◽  
pp. 1-22
Author(s):  
Hasan Al-Ibadi ◽  
Karl Stephen ◽  
Eric Mackay
Keyword(s):  
Relative Permeability ◽  
Coarse Grid ◽  
Numerical Dispersion ◽  
Absolute Permeability ◽  
Scale Model ◽  
Salinity Range ◽  
Fine Scale ◽  
Fractional Flow ◽  
Low Salinity ◽  
Low Salinity Waterflooding

SummaryModeling the dynamic fluid behavior of low-salinity waterflooding (LSWF) at the reservoir scale is a challenge that requires a coarse-grid simulation to enable prediction in a feasible time scale. However, evidence shows that using low-resolution models will result in a considerable mismatch compared with an equivalent fine-scale model with the potential of strong, numerically induced pulses and other dispersion-related effects. This work examines two new upscaling methods that have been applied to improve the accuracy of predictions in a heterogeneous reservoir where viscous crossflow takes place.We apply two approaches to upscaling to bring the flow prediction closer to being exact. In the first method, we shift the effective-salinity range for the coarse model using algorithms that we have developed to correct for numerical dispersion and associated effects. The second upscaling method uses appropriately derived pseudorelative permeability curves. The shape of these new curves is designed using a modified fractional-flow analysis of LSWF that captures the relationship between dispersion and the waterfront velocities. This second approach removes the need for explicit simulation of salinity transport to model oil displacement. We applied these approaches in layered models and for permeability distributed as a correlated random field.Upscaling by shifting the effective-salinity range of the coarse-grid model gave a good match to the fine-scale scenario, while considerable mismatch was observed for upscaling of the absolute permeability alone. For highly coarsened models, this method of upscaling reduced the appearance of numerically induced pulses. On the other hand, upscaling by using a single (pseudo)relative permeability produced more robust results with a very promising match to the fine-scale scenario. These methods of upscaling showed promising results when they were used to scale up fully communicating and noncommunicating layers as well as models with randomly correlated permeability.Unlike documented methods in the literature, these newly derived methods take into account the substantial effects of numerical dispersion and effective concentration on fluid dynamics using mathematical tools. The methods could be applied for other models where the phase mobilities change as a result of an injected solute, such as surfactant flooding and alkaline flooding. Usually these models use two sets of relative permeability and switch from one to another as a function of the concentration of the solute.

Analytical Upgridding Method To Preserve Dynamic Flow Behavior

2010 ◽  
Vol 13 (03) ◽  
pp. 473-484 ◽  
Author(s):  
Seyyed Abolfazl Hosseini ◽  
Mohan Kelkar
Keyword(s):  
Single Phase ◽  
Flow Behavior ◽  
Pressure Profile ◽  
Scale Model ◽  
Optimum Number ◽  
Fine Scale ◽  
Coarse Scale ◽  
Flow Process ◽  
Number Of Layers ◽  
Pressure Profiles

Summary A geocellular model contains millions of gridblocks and needs to be upscaled before the model can be used as an input for flow simulation. Available techniques for upgridding vary from simple methods such as proportional fractioning to more complicated methods such as maintaining heterogeneities through variance calculations. All these methods are independent of the flow process for which simulation is going to be used, and are independent of well configuration. We propose a new upgridding method that preserves the pressure profile at the upscaled level. It is well established that the more complex the flow process, the more detailed the level of heterogeneity needed in the simulation model. In general, ideal upscaling is the process that preserves the "pressure profile" from the fine-scale model under the applicable flow process. In our method, we upgrid the geological model using simple flow equations in porous media. However, it should be remembered that to obtain a better match between fine scale and coarse scale, we also need to use appropriate upscaling of the reservoir properties. The new method is currently developed for single-phase flow; however, we used it for both single-phase and two-phase flows for 2D and 3D cases. The method differs fundamentally from the other methods that try to preserve heterogeneities. In those methods, gridblocks are combined that have similar velocities (or other properties) by assuming constant pressure drop across the blocks. Instead, we combine the gridblocks that have similar pressure profiles, although to release some of our assumptions such as having constant velocities in gridblocks, we balance our equation with the K2 term. The procedure is analytical and, hence, very efficient, but preserves the pressure profile in the reservoir. The gridblocks (or layers) are combined in a way so that the difference between fine- and coarse-scale pressure profiles is minimized. In addition, we also propose two new criteria that allow us to choose the optimum number of layers more accurately so that a critical level of heterogeneity is preserved. These criteria provide insight into the overall level of heterogeneity in the reservoir and the effectiveness of the layering design. We compare the results of our method with proportional layering and the King et al. method (King et al. 2006) and show that, for the same number of layers, the proposed method captures the results of the fine-scale model better. We show that the layer merging not only depends on the variation in the permeability between the gridblocks (K2 term), but also on the relative magnitude of the permeability values by combining 1/K2 and K2 terms.

Analytical Upgridding of Geocellular Mode in the Presence of Gravity-Dominated Displacement

SPE Journal ◽  
2011 ◽  
Vol 16 (04) ◽  
pp. 828-841 ◽  
Author(s):  
Ake Rittirong ◽  
Mohan Kelkar
Keyword(s):  
Oil Recovery ◽  
Flow Characteristics ◽  
Scale Model ◽  
Fine Scale ◽  
Computationally Efficient ◽  
Fractional Flow ◽  
Two Phase ◽  
Analytical Methodology ◽  
Three Phase ◽  
2D Flow

Summary In simulating enhanced-oil-recovery (EOR) processes, it is critical that all the flow behaviors be properly accounted for in the simulation. Because of computation limitations, long calculation time, and complexity of physics, geological models cannot be directly used for fieldwide simulations. Upgridding reduces the number of gridblocks in the simulation model and therefore makes the simulation more efficient. An appropriate upgridding process needs to preserve the dynamic behavior of the fine-scale model. We propose such an analytical methodology. Our new technique is based on preserving the characteristics, which are based on the fractional-flow concept specifically modified for vertical flow between the layers. We develop our method with a specific application to gravity-dominated displacement. In upgridding the fine-scale model, we have developed a criterion by which the sequence in which the fine-scale layers are combined is proposed such that fractional-flow characteristics based on the fine-scale model are honored. Using this methodology, we can determine not only the sequence in which layers are combined, but also to what extent we can upgrid the fine-scale model. The proposed methodology is developed for two-phase, 2D flow under the effect of gravity-segregated displacement. However, it is also tested for three-phase, 3D flow in gravity-dominated displacement with moderate effect of viscous and capillary forces. The proposed solution is analytical; therefore, it is computationally efficient. We have validated the methodology with both synthetic and field examples and demonstrate that the proposed methodology is superior to conventional proportional layering and variance-based methodologies.

A Machine Learning Methodology for Rock-Typing Using Relative Permeability Curves

2021 ◽  
Author(s):  
Carlos Esteban Alfonso ◽  
Frédérique Fournier ◽  
Victor Alcobia
Keyword(s):  
Machine Learning ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Fractional Flow ◽  
Flow Curves ◽  
Rock Types ◽  
Rock Typing ◽  
Relative Permeability Curves ◽  
Permeability Data

Abstract The determination of the petrophysical rock-types often lacks the inclusion of measured multiphase flow properties as the relative permeability curves. This is either the consequence of a limited number of SCAL relative permeability experiments, or due to the difficulty of linking the relative permeability characteristics to standard rock-types stemming from porosity, permeability and capillary pressure. However, as soon as the number of relative permeability curves is significant, they can be processed under the machine learning methodology stated by this paper. The process leads to an automatic definition of relative permeability based rock-types, from a precise and objective characterization of the curve shapes, which would not be achieved with a manual process. It improves the characterization of petrophysical rock-types, prior to their use in static and dynamic modeling. The machine learning approach analyzes the shapes of curves for their automatic classification. It develops a pattern recognition process combining the use of principal component analysis with a non-supervised clustering scheme. Before this, the set of relative permeability curves are pre-processed (normalization with the integration of irreducible water and residual oil saturations for the SCAL relative permeability samples from an imbibition experiment) and integrated under fractional flow curves. Fractional flow curves proved to be an effective way to unify the relative permeability of the two fluid phases, in a unique curve that characterizes the specific poral efficiency displacement of this rock sample. The methodology has been tested in a real data set from a carbonate reservoir having a significant number of relative permeability curves available for the study, in addition to capillary pressure, porosity and permeability data. The results evidenced the successful grouping of the relative permeability samples, according to their fractional flow curves, which allowed the classification of the rocks from poor to best displacement efficiency. This demonstrates the feasibility of the machine learning process for defining automatically rock-types from relative permeability data. The fractional flow rock-types were compared to rock-types obtained from capillary pressure analysis. The results indicated a lack of correspondence between the two series of rock-types, which testifies the additional information brought by the relative permeability data in a rock-typing study. Our results also expose the importance of having good quality SCAL experiments, with an accurate characterization of the saturation end-points, which are used for the normalization of the curves, and a consistent sampling for both capillary pressure and relative permeability measurements.

scholarly journals Improved method for complete gas-brine imbibition relative permeability curves

2020 ◽  
Vol 146 ◽  
pp. 03003
Author(s):  
M. Ben Clennell ◽  
Cameron White ◽  
Ausama Giwelli ◽  
Matt Myers ◽  
Lionel Esteban ◽  
...  
Keyword(s):  
Relative Permeability ◽  
Gas Permeability ◽  
Test Methods ◽  
Standard Test ◽  
High Rate ◽  
Fractional Flow ◽  
Gas Reservoirs ◽  
Data Gap ◽  
Small Increments ◽  
Relative Permeability Curves

Standard test methods for measuring imbibition gas-brine relative permeability on reservoir core samples often lead to non-uniform brine saturation. During co-current flow, the brine tends to bank up at the sample inlet and redistributes slowly, even with fractional flow of gas to brine of 400:1 or more. The first reliable Rel Perm point is often only attained after a brine saturation of around Sw=40% is achieved, leaving a data gap between Swirr and this point. The consequent poor definition of the shape of the Rel Perm function can lead to uncertainty in the performance of gas reservoirs undergoing depletion drive with an encroaching aquifer or subjected to a water flood. We have developed new procedures to pre-condition brine saturation outside of the test rig and progress it in small increments to fill in the data gap at low Sw, before continuing with a co-current flood to the gas permeability end-point. The method was applied to series of sandstone samples from gas reservoirs from the NW Shelf of Australia, and a Berea standard. We found that the complete imbibition relative permeability curve is typically ‘S’ shaped or has a rolling over, convex-up shape that is markedly different from the concave-up, Corey Rel Perm curve usually fitted to SCAL test data. This finding may have an economic upside if the reservoir produces gas at a high rate for longer than was originally predicted based on the old Rel Perm curves.

scholarly journals Assessment of the Rheological Behavior of Polymer–Oxidant Mixtures and the Influence of the Groundwater Environment on Their Properties

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1698
Author(s):  
Qi Xu ◽  
Jiajun Chen ◽  
Xinran Song
Keyword(s):  
Xanthan Gum ◽  
Control Method ◽  
Shear Thinning ◽  
Mobility Control ◽  
Retention Rates ◽  
Viscosity Reduction ◽  
Leading Role ◽  
Salt Ions ◽  
Groundwater Environment ◽  
Water Ph

Shear-thinning polymers have been introduced to contaminant remediation in the subsurface as a mobility control method applied to mitigate the inefficient delivery of remedial agents caused by geological heterogeneity. Laboratory experiments have been conducted to assess the compatibility of polymers (xanthan and hydrolyzed polyacrylamide (HPAM)) and oxidants (KMnO4 and Na2S2O8) through quantitative evaluation of the viscosity maintenance, shear-thinning performance, and oxidant consumption. The mechanism that causes viscosity loss and the influence of the groundwater environment on the mixture viscosity were also explored. The xanthan–KMnO4 mixture exhibited the best performance in both viscosity retention and shear-thinning behavior with retention rates higher than 75% and 73.5%, respectively. Furthermore, the results indicated that xanthan gum has a high resistance to MnO4− and that K+ plays a leading role in its viscosity reduction, while HPAM is much more sensitive to MnO4−. The viscosity responses of the two polymers to Na2S2O8 and NaCl were almost consistent with that of KMnO4; salt ions displayed an instantaneous effect on the solution’s viscosity, while the oxide ions could cause the solution’s viscosity to decrease continuously with time. Since xanthan exhibited acceptable oxidant consumption as well, xanthan–KMnO4 is considered to be the optimal combination. In addition, the results implied that the effects of salt ions and the water pH on the mixture solution could be acceptable. In the 2D tank test, it was found that when xanthan gum was introduced, the sweeping efficiency of the oxidant in the low-permeability zone was increased from 28.2% to 100%. These findings demonstrated the feasibility of using a xanthan–KMnO4 mixture for actual site remediation.

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