Fractional Flow Behaviour and Transport Mechanisms During Low salinity and Protein Enzyme Bio-surfactant EOR Flooding

2020 ◽  
Author(s):  
Anthony Morgan ◽  
Lateef Akanji ◽  
Tinuola Udoh ◽  
Shaibu Mohammed ◽  
Prosper Anumah ◽  
...  
Keyword(s):  
Flow Behaviour ◽  
Transport Mechanisms ◽  
Fractional Flow ◽  
Low Salinity ◽  
Protein Enzyme

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.

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.

scholarly journals Semi-Analytical Solution of Chemical Flooding in Heterogeneous Non-Communicating Layers with a Focus on Low Salinity Water Flooding

2020 ◽  
Vol 135 (1) ◽  
pp. 101-135
Author(s):  
Hasan Al-Ibadi ◽  
Karl D. Stephen ◽  
Eric J. Mackay
Keyword(s):  
Analytical Solutions ◽  
Water Flooding ◽  
Salinity Range ◽  
Chemical Flooding ◽  
Fractional Flow ◽  
Sweep Efficiency ◽  
Flow Equations ◽  
Low Salinity ◽  
Low Salinity Water ◽  
Salinity Water

Abstract Chemical flooding has been implemented intensively for some years to enhance sweep efficiency in porous media. Low salinity water flooding (LSWF) is one such method that has become increasingly attractive. Historically, analytical solutions were developed for the flow equations for water flooding conditions, particularly for non-communicating strata. We extend these to chemical flooding, more generally, and in particular for LSWF where salinity is modeled as an active tracer and changes relative permeability. Dispersion affects the solutions, and we include this also. Using fractional flow theory, we derive a mathematical solution to the flow equations for a set of layers to predict fluid flow and solute transport. Analytical solutions tell us the location of the lead (formation) waterfront in each layer. We extend a correlation that we previously developed to predict the effects of numerical and physical dispersion. We used this correction to predict the location of the second waterfront in each layer which is induced by the chemical’s effect on mobility. We show that in multiple non-communicating layers, mass conservation can be used to deduce the interlayer relationships of the various fronts that form. This is based on similar analysis developed for water flooding although the calculations are more complex because of the development of multiple fronts. The result is a predictive tool that we compare to numerical simulations and the precision is very good. Layers with contrasting petrophysical properties and wettability are considered. We also investigate the relationship between the fractional flow, effective salinity range, salinity dispersion and salinity retardation. The recovery factor and vertical sweep efficiency are also very predictable. The work can also be applicable to other chemical EOR processes if they alter the fluid mobility. This includes polymer and surfactant flooding.

An Analysis of Numerically Induced Pulses in Simulations of Low-Salinity Waterflooding and Their Reduction by Flow Upscaling

SPE Journal ◽  
2021 ◽  
pp. 1-21
Author(s):  
Hasan Al-Ibadi ◽  
Karl D. Stephen ◽  
Eric Mackay
Keyword(s):  
Oil Recovery ◽  
Flow Behavior ◽  
Scale Up ◽  
Numerical Dispersion ◽  
Salinity Range ◽  
Fractional Flow ◽  
Numerical Errors ◽  
Low Salinity ◽  
Effective Manner ◽  
Courant Condition

Summary Numerical fidelity is required when using simulations to predict enhanced-oil-recovery (EOR) processes. In this paper, we investigate the conditions that lead to numerical errors when simulating low-salinity (LS) waterflooding (LSWF). We also examine how to achieve more accurate simulation results by scaling up the flow behavior in an effective manner. An implicit finite-difference numerical solver was used to simulate LSWF. The accuracy of the numerical solution has been examined as a function of changing the length of the grid cell and the timestep. Previously we have shown that numerical dispersion induces a physical retardation such that the LS front slows down while the formation water front speeds up. We also report for the first time that pulses can be generated as numerical artifacts in coarsely gridded simulations of LSWF. These effects reflect the interaction of dispersion, the effective-salinity range, and the use of upstream weighting during calculation, and can corrupt predictions of flow behavior. The effect of the size of the timestep was analyzed with respect to the Courant condition, traditionally related to explicit numerical schemes and also numerical stability conditions. We also investigated some of the nonlinear elements of the simulation model, such as the differences between the concentrations of connate water salinity and the injected brine, effective-salinity-concentration range, and the net mobility change on fluids through changing the salinity. We report that to avoid pulses it is necessary, but not sufficient, to meet the Courant condition relating timestep size to cell size. We have also developed two approaches that can be used to scale up simulations of LSWF and tackle the numerical problems. The first method is dependent on a mathematical relationship between the fractional flow, effective-salinity range, and the Péclet number and treats the effective-salinity range as a pseudofunction. The second method establishes an unconventional proxy method equivalent to pseudorelative permeabilities. A single table of pseudorelative permeability data can be used for a waterflood instead of two tables, as is usual for LSWF. This is a novel approach that removes the need for relative permeability interpolation during the simulation. Overall, by avoiding numerical errors, we help engineers to more efficiently and accurately assess the potential for improving oil recovery using LSWF and thus optimize field development. We also avoid the numerical pulses inherent in the traditional LSWF model.

Numerical Investigation on Low Salinity Augmented Microbial Flooding LSAMF within a Sandstone Core for Enhanced Oil Recovery Under Nonisothermal and Fluctuating pH Conditions

2021 ◽  
Author(s):  
Susmit Chakraborty ◽  
Suresh Kumar Govindarajan ◽  
Sathyanarayana N. Gummadi
Keyword(s):  
Crude Oil ◽  
Enhanced Oil Recovery ◽  
Relative Permeability ◽  
Oil Recovery ◽  
Present Model ◽  
Transport Model ◽  
Water Salinity ◽  
Fractional Flow ◽  
Low Salinity ◽  
Microbial Flooding

Summary In an era of increasing energy demand, declining oil fields and fluctuating crude oil prices globally, most oil companies are looking forward to implementing cost effective and environmentally sustainable enhanced oil recovery (EOR) techniques such as low salinity waterflooding (LSWF) and microbial EOR (MEOR). The present study numerically investigates the combined influence of simultaneous LSWF and microbial flooding for in-Situ MEOR in tertiary mode within a sandstone core under spatiotemporally fluctuating pH and temperature conditions. The developed black oil model consists of five major coupled submodels: nonlinear heat transport model; ion transport coupled with multiple ion exchange (MIE) involving uncomplexed cations and anions; pH variation with salinity and temperature; coupled reactive transport of injected substrates, Pseudomonas putida and produced biosurfactants with microbial maximum specific growth rate varying with temperature, salinity and pH; relative permeability and fractional flow curve variations due to interfacial tension reduction and wettability alteration (WA) by LSWF and biofilm deposition. The governing equations are solved using finite difference technique. Operator splitting and bisection methods are adopted to solve the MIE-transport model. The present model is found to be numerically stable and agree well with previously published experimental and analytical results. In the proposed MIE-transport mechanism, decreasing injection water salinity (IWS) from 2.52 to 0.32 M causes enhanced Ca2+ desorption rendering rock surface towards more water wet. Consequently, oil relative permeability (kro) increases with >55% reduction in water fractional flow (fw) at water saturation of 0.5 from the initial oil-wet condition. Further reducing IWS to 0.03 M causes Ca2+ adsorption shifting the surface wettability towards more oil-wet thus increasing fw by 52%. Formation water salinity (FWS) showed minor impact on WA with <5% decrease in fw when FWS is reduced from 3.15 to 1.05 M. During LSAMF, biosurfactant production is enhanced by >63% on reducing IWS from 2.52 to 0.32 M with negligible increase on further reducing IWS and FWS. This might be due to limiting nonisothermal (40 to 55 °C) and nutrient availability conditions. LSAMF caused significant WA, increase in kro with fw reduction by >84%. Though pH increased from 8.0 to 8.9, it showed minor impact on microbial metabolism. Formation damage due to bioplugging observed near injection point is compensated by effective migration of biosurfactants deep within sandstone core. The present study is a novel attempt to show synergistic effect of LSAMF over LSWF in enhancing oil mobility and recovery at core scale by simultaneously addressing complex crude oil-rock-brine chemistry and critical thermodynamic parameters that govern MEOR efficiency within a typical sandstone formation. The present model with relatively lower computational cost and running time improves the predictive capability to pre-select potential field candidates for successful LSAMF implementation.

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