Capillary pressure and relative permeability estimation for low salinity waterflooding processes using pore network models

2019 ◽  
Vol 182 ◽  
pp. 106253 ◽  
Author(s):  
Edgar G. Martínez-Mendoza ◽  
Martín A. Díaz-Viera ◽  
Manuel Coronado ◽  
Ana T. Mendoza-Rosas
Keyword(s):  
Capillary Pressure ◽  
Relative Permeability ◽  
Network Models ◽  
Pore Network ◽  
Low Salinity ◽  
Permeability Estimation ◽  
Low Salinity Waterflooding

Extended Fractional-Flow Model of Low-Salinity Waterflooding Accounting for Dispersion and Effective Salinity Range

SPE Journal ◽  
2019 ◽  
Vol 24 (06) ◽  
pp. 2874-2888 ◽  
Author(s):  
Hasan Al–Ibadi ◽  
Karl D. Stephen ◽  
Eric J. Mackay
Keyword(s):  
Relative Permeability ◽  
Flow Behavior ◽  
Flow Analysis ◽  
Salinity Range ◽  
Fractional Flow ◽  
Low Salinity ◽  
Low Salinity Water ◽  
Low Salinity Waterflooding ◽  
Salinity Water ◽  
The Impact

Summary Low–salinity waterflooding (LSWF) is an emergent technology developed to increase oil recovery. Laboratory–scale testing of this process is common, but modeling at the production scale is less well–reported. Various descriptions of the functional relationship between salinity and relative permeability have been presented in the literature, with respect to the differences in the effective salinity range over which the mechanisms occur. In this paper, we focus on these properties and their impact on fractional flow of LSWF at the reservoir scale. We present numerical observations that characterize flow behavior accounting for dispersion. We analyzed linear and nonlinear functions relating salinity to relative permeability and various effective salinity ranges using a numerical simulator. We analyzed the effect of numerical and physical dispersion of salinity on the velocity of the waterflood fronts as an expansion of fractional–flow theory, which normally assumes shock–like behavior of water and concentration fronts. We observed that dispersion of the salinity profile affects the fractional–flow behavior depending on the effective salinity range. The simulator solution is equal to analytical predictions from fractional–flow analysis when the midpoint of the effective salinity range lies between the formation and injected salinities. However, retardation behavior similar to the effect of adsorption occurs when these midpoint concentrations are not coincidental. This alters the velocities of high– and low–salinity water fronts. We derived an extended form of the fractional–flow analysis to include the impact of salinity dispersion. A new factor quantifies a physical or numerical retardation that occurs. We can now modify the effects that dispersion has on the breakthrough times of high– and low–salinity water fronts during LSWF. This improves predictive ability and also reduces the requirement for full simulation.

Scale Up of Pore-Network Models into Reservoir Scale: Optimization of Inflow Control Devices Placement

SPE Journal ◽  
2021 ◽  
pp. 1-18
Author(s):  
Farzad Bashtani ◽  
Mazda Irani ◽  
Apostolos Kantzas
Keyword(s):  
Pore Structure ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Scale Up ◽  
Pore Network ◽  
Flow In Porous Media ◽  
Well Completion ◽  
Control Devices ◽  
Relative Permeability Curves ◽  
Capillary Pressure Curves

Summary Improvements to more advanced tools, such as inflow control devices (ICDs), create a high drawdown regime close to wellbores. Gas liberation within the formation occurs when the drawdown pressure is reduced below the bubblepoint pressure, which in turn reduces oil mobility by reducing its relative permeability, and potentially reducing oil flow. The key input in any reservoir modeling to compare the competition between gas and liquid flow toward ICDs is the relative permeability of different phases. Pore-network modeling (PNM) has been used to compute the relative permeability curves of oil, gas, and water based on the pore structure of the formation. In this paper, we explain the variability of pore structure on its relative permeability, and for a similar formation and identical permeability, we explain how other factors, such as connectivity and throat radius distribution, can vary the characteristic curves. By using a boundary element method, we also incorporate the expected relative permeability and capillary pressure curves into the modeling. The results show that such variability in the pore network has a less than 10% impact on production gas rates, but its effect on oil production can be significant. Another important finding of such modeling is that providing the PNM-created relative permeabilities may provide totally different direction on setting the operational constraints. For example, in the case studied in this paper, PNM-created relative permeability curves suggest that a reduction of flowing bottomhole pressure (FBHP) increases the oil rate, but for the case modeled with a Corey correlation, changes in FBHP will not create any uplift. The results of such work show the importance of PNM in well completion design and probabilistic analysis of the performance, and can be extended based on different factors of the reservoir in future research. Although PNM has been widely used to study the multiphase flow in porous media in academia, the application of such modeling in reservoir and production engineering is quite narrow. In this study, we develop a framework that shows the general user the importance of PNM simulation and its implementation in day-to-day modeling. With this approach, the PNM can be used not just to provide relative permeability or capillary pressure curves on a core or pore- scale, but to preform simulations at the wellbore or reservoir scale as well to optimize the current completions.

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.

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

SPE Journal ◽  
2015 ◽  
Vol 20 (05) ◽  
pp. 1154-1166 ◽  
Author(s):  
Emad W. Al-Shalabi ◽  
Kamy Sepehrnoori ◽  
Mojdeh Delshad ◽  
Gary Pope
Keyword(s):  
Capillary Pressure ◽  
Relative Permeability ◽  
Oil Recovery ◽  
Carbonate Rocks ◽  
History Matching ◽  
Water Injection ◽  
Low Salinity ◽  
Low Salinity Water ◽  
Salinity Water ◽  
Low Salinity Water Injection

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.

A Pore-Network-Modeling Approach To Predict Petrophysical Properties of Diatomaceous Reservoir Rock

2007 ◽  
Vol 10 (06) ◽  
pp. 597-608 ◽  
Author(s):  
Liping Jia ◽  
Cynthia Marie Ross ◽  
Anthony Robert Kovscek
Keyword(s):  
Multiphase Flow ◽  
Capillary Pressure ◽  
Network Model ◽  
Relative Permeability ◽  
Oil Recovery ◽  
Pore Network ◽  
Reservoir Rock ◽  
Pore Network Model ◽  
Rock Types ◽  
Capillary Pressure Curves

Summary A 3D pore-network model of two-phase flow was developed to compute permeability, relative permeability, and capillary pressure curves from pore-type, -size, and -shape information measured by means of high-resolution image analysis of diatomaceous-reservoir-rock samples. The diatomite model is constructed using pore-type proportions obtained from image analysis of epoxy-impregnated polished samples and mercury-injection capillary pressure curves for diatomite cores. Multiple pore types are measured, and each pore type has a unique pore-size and throat-size distribution that is incorporated in the model. Network results present acceptable agreement when compared to experimental measurements of relative permeability. The pore-network model is applicable to both drainage and imbibition within diatomaceous reservoir rock. Correlation of network-model results to well log data is discussed, thereby interpolating limited experimental results across the entire reservoir column. Importantly, our method has potential to predict the petrophysical properties for reservoir rocks with either limited core material or those for which conventional experimental measurements are difficult, unsuitable, or expensive. Introduction Model generation for reservoir simulation requires accurate entering of physical properties such as porosity, permeability, initial water saturation, residual-oil saturation, capillary pressure functions, and relative permeability curves. These functions and parameters are necessary to estimate production rate and ultimate oil recovery, and thereby optimize reservoir development. Accurate measurement and representation of such information is, therefore, essential for reservoir modeling. Relative permeability and capillary pressure curves are the most important constitutive relations to represent multiphase flow. Often, it is difficult to sample experimentally the range of relevant multiphase-flow behavior of a reservoir. In addition to the availability of rock samples, measurements are frequently time consuming to conduct, and conventional techniques are not suitable for all rock types (Schembre and Kovscek 2003). It is impossible, therefore, to measure all the unique relative permeability functions of different reservoir-rock types and variations within a rock type. This lack of constitutive information limits the accuracy of reservoir simulators to predict oil recovery. Simply put, other available data must be queried for their relevance to multiphase flow and must be used to interpret the available relative permeability and capillary pressure information.

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