A Mechanistic Pore-Scale Analysis of the Low-Salinity Effect in Heterogeneously Wetted Porous Media

Michael G. Watson; Steven R. McDougall

REVIEW 4 major objections 4 minor 46 references

A Mechanistic Pore-Scale Analysis of the Low-Salinity Effect in Heterogeneously Wetted Porous Media

T0 review · 4 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read In mixed-wet porous media, low-salinity waterflooding adds oil only from the oil-wet pore fraction, and only when it increases the sweep of those pores.

desk verdict A credible steady-state pore-network study whose central mechanistic claim holds up, but whose quantitative LSE magnitudes rest on an openly heuristic tracer-timing equation and several 'results not shown' claims. read the letter →

arxiv 1908.02874 v1 pith:LQ6TRTBC submitted 2019-08-07 physics.geo-ph

Michael G. Watson , Steven R. McDougall This is my paper · ORCID

classification physics.geo-ph PACS 47.56.+r

keywords low-salinitywaterfloodingporenetworkmodelwettabilityalterationmixed-wetporousmediaenhancedoilrecoverytraceralgorithmcapillarydisplacementpercolation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks when low-salinity (LS) waterflooding, a method that often improves oil recovery but sometimes fails, can work in rocks containing both water-wet (WW) and oil-wet (OW) pores. It uses a 3D steady-state pore network model coupled to a tracer algorithm that follows injected LS brine as it mixes with resident high-salinity (HS) brine. The central claim is that when both wettability classes form connected paths through the network, the WW pores are already swept by ordinary imbibition, so LS brine can only add oil from the OW fraction. Extra oil appears only when LS-induced contact-angle reduction increases the microscopic sweep efficiency of the OW pores; that condition is necessary but not sufficient. The simulations identify the OW fraction, the network connectivity, and the initial HS-brine saturation as the controls, and they reproduce positive, neutral, and negative outcomes.

What carries the argument

The machinery is a capillary-dominated steady-state displacement model on a 3D cubic pore network, augmented by a tracer algorithm that estimates salinity after each saturation step. Each pore carries a radius $R$ and a contact angle $\theta$, and water invades in order of capillary entry pressure $2\sigma\cos\theta/R$. After every step the algorithm rewinds the flow and uses Eq. (1) to estimate how long the step took, advects a salinity tracer through connected water, and applies a Heaviside rule (Eq. (2)): if neighbouring water salinity falls below a critical value $C^*$, the contact angle of an oil-occupied pore is reduced by $\Delta\theta$. This dynamic contact-angle map changes the sequence of pore filling, and the timing of the contact-angle reduction relative to the drainage half of the flood is what determines whether microscopic sweep efficiency improves.

What would settle it

Time-resolved micro-CT or micromodel experiments on a mixed-wet rock that image both the salinity front and oil-water contact angles while oil is displaced would settle the core claim: if extra oil appears from pores that were water-wet before the flood, or if oil-wet pores yield incremental oil without an increase in the fraction of oil-wet pores invaded, the mechanism fails. A model-level check is to replace Eq. (1) with a full unsteady-state front calculation and observe whether the predicted connectivity trend and the non-monotonic $S_{wi}$ dependence survive.

Watch

Extended reading notes

Core claim

The paper's central claim is that in a network where both water-wet and oil-wet pores form spanning clusters before flooding, the oil-wet pores are the only viable source of incremental oil during low-salinity injection. Making water-wet pores more water-wet does not help, because those pores are already invaded by imbibition in the corresponding high-salinity flood; the two simulations converge to nearly the same water occupancy in the WW fraction. In the OW fraction, the standard drainage sequence fills pores from largest to smallest, so the only route to extra oil is to invade a larger fraction of the OW pores, the 'microscopic sweep efficiency effect'. A necessary but not sufficient condition is that OW contact angles are reduced during the drainage cycle rather than before it; whether this happens is set by how long connate HS brine protects OW pores from freshening. Low network connectivity and an intermediate initial HS-brine saturation delay the salinity front, while extreme $S_{wi}$ values weaken or erase the gain, and the fraction $\alpha$ of OW pores sets the ceiling on what can be recovered.

Load-bearing premise

The simulations assume the heuristic time estimate in Eq. (1) correctly predicts how long each saturation step takes, and that timing decides whether oil-wet pores have already been freshened before the drainage cycle begins; if the timing is wrong, the claimed roles of initial brine saturation and connectivity, and the specific incremental-oil figures, would be artifacts of that assumption.

Editorial extensions

If this is right

In a mixed-wet reservoir where both wettabilities form through-going clusters, the water-wet pores cannot yield incremental oil to a low-salinity flood; the upper bound on the gain is fixed by the oil-wet fraction.
A low-salinity flood can weaken oil-wet contact angles and still produce no extra oil, because the required increase in oil-wet sweep efficiency is necessary but not sufficient.
Poorly connected pore networks should show larger low-salinity benefits, since circuitous flow delays the arrival of fresh brine at oil-wet pores.
The initial connate-brine saturation acts nonlinearly: too little lets the front freshen too early, too much causes early breakthrough that flushes the protective HS brine out.
The specific wettability arrangement (mixed-wet large, mixed-wet small, or fractionally wet) becomes decisive only when LS brine changes the wetting class of pores, not when it merely weakens or strengthens the existing wettability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

As an extension of the paper's logic, replacing the heuristic step-time formula with a fully dynamic flow simulation would test whether the predicted $S_{wi}$ and connectivity trends survive in degree or only in direction.
A practical screening inference follows: low-salinity candidates should be characterised by connate-water saturation and pore connectivity, not only by oil-wet fraction, because those hidden variables can flip a pilot between positive and neutral.
The delay-until-drainage principle suggests a design rule the paper does not simulate: a short high-salinity pre-flush or a viscosity-moderated front could hold the salinity boundary back and enlarge the incremental-oil window.
Because the geometric constraint is general, the same two-percolating-cluster argument may apply to other wettability-altering enhanced-oil-recovery agents, not just low-salinity brine.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know two things before reading. First, the central claim is genuinely new and defensible: in networks with spanning clusters of both water-wet and oil-wet pores, the oil-wet pores are the only viable source of incremental oil, and a low-salinity-induced increase in microscopic sweep efficiency in that fraction is necessary but not sufficient. That claim follows directly from the capillary displacement rules, not from fitting to data, so there is no circularity in the strong sense. Second, the quantitative side is softer. The magnitude of the low-salinity effect and its dependences on initial water saturation and connectivity are controlled by an openly heuristic tracer-timing factor, Eq. (1), and a few supporting simulations are only described as "results not shown."

What the paper does well: it extends the authors' own uniformly wetted steady-state model to fractionally wet, mixed-wet-large, and mixed-wet-small networks, and it gives a clean mechanistic story for why post-breakthrough low-salinity injection often fails while secondary injection can work. The distinction between pore-sequence effects and microscopic sweep efficiency is useful, and the secondary-mode result—connate high-salinity brine delays wettability modification so that more oil-wet pores are swept during drainage—is a plausible explanation for the patchy experimental literature. The paper is honest about its qualitative scope and about the model's simplifications. The self-citations are appropriate because the framework is carried over from prior work; the choice of Δθ = 20° is at least consistent with the Khishvand et al. contact-angle measurements.

The main soft spot is Eq. (1). The paper says that formula was "chosen to reflect qualitative observations from experimental coreflooding studies," not derived. That timing controls when oil-wet pores see low-salinity brine and hence whether their contact angles are modified before or during the drainage cycle. If a defensible transport calculation gave a shorter residence time, the 4.9% incremental oil could shrink or disappear; if longer, even the low-Swi case might show an effect. So the sensitivity to Swi and Z is real within the model, but the specific magnitudes rest on an unvalidated scalar factor. This is a moderate concern, not a fatal one, because the central conclusion about where incremental oil must come from does not depend on that factor.

I also count at least three places where quantitative support is asserted rather than shown: the claimed equivalence of FW/MWS networks in the post-breakthrough section, the delayed-injection simulations, and the alpha sensitivity in the Discussion (where numbers 2.5% and 9.1% are given but no curves or realizations). There are no error bars or ensemble averages, so single-realization effects cannot be separated from trends. The paper's own limitation statements are candid, which helps, but they don't fix the reproducibility gap.

Who is this for? Pore-network modelers and EOR experimentalists who want a mechanistic way to interpret inconsistent low-salinity corefloods. I would send it to peer review rather than desk-reject, and I would make the revision ask concrete: release the code and data, or at minimum show the missing simulations and rerun the key cases over an ensemble of network realizations. I would also ask for a sensitivity analysis of Eq. (1) itself, or a derivation from a more transparent transport model. If the authors can satisfy that, the paper would be a solid contribution.

Referee Report

4 major / 4 minor

Summary. The paper extends a steady-state pore-network model, previously developed for uniformly wetted media, to simulate low-salinity (LS) waterflooding in heterogeneously wetted networks that contain spanning clusters of both water-wet (WW) and oil-wet (OW) pores. The model couples capillary-controlled displacement with a tracer algorithm that estimates the spatial evolution of brine salinity, and it applies a Heaviside-type rule for local contact-angle modification when salinity falls below a threshold. The main claims are: (i) in such networks the OW pores are the only viable source of incremental oil; (ii) a LS-induced increase in microscopic sweep efficiency in the OW pore fraction is a necessary but not sufficient condition for additional oil production; and (iii) the OW fraction alpha, network connectivity Zbar, and initial water saturation Swi are key controls on the magnitude of the low-salinity effect (LSE). The central reported result is a secondary-mode simulation that produces roughly 5% more oil with LS brine than with HS brine, attributed to delayed wettability modification of OW pores until the drainage cycle. Sensitivity runs for Swi and Zbar, plus unreported runs for alpha, are used to support the parameter-dependence conclusions.

Significance. If the results hold, the paper provides a falsifiable mechanistic explanation for why LS waterflooding may be ineffective or effective depending on the initial wettability distribution, and it highlights a specific pore-scale mechanism (delayed modification of OW pores during drainage) that could guide experimental coreflood design. A notable strength is that the central logical argument that OW pores are the only incremental oil source follows directly from the stated displacement rules and is not fitted to data; the paper also explicitly lists its idealizations and limitations. The contact-angle change is tied to an experimental measurement, and the tracer algorithm is described in enough detail to be re-implemented. However, the quantitative LSE magnitudes and the Swi/Z/alpha sensitivities rest on the heuristic timing in Eq. (1) and on several unreported simulation sets, so the significance is tempered by the need for validation and more complete reporting.

major comments (4)

[II.C.1, Eq. (1)] The tracer timing equation T_DeltaV = (DeltaV/Q)[DeltaP_old/(DeltaP_old - DeltaP_new)] is explicitly described as 'chosen to reflect qualitative observations' rather than derived from transport physics. This time controls how far the LS front advances before each pressure step, and via Eq. (2) it determines whether neighbouring OW pores are wettability-modified before or during drainage. The entire positive-LSE mechanism in Section III.B (delayed OW modification producing 4.9% incremental oil) and the reported Swi and Zbar sensitivities are contingent on this functional form. If a defensible transport calculation gave a shorter T_DeltaV, the LS front would modify OW pores earlier and the incremental oil could vanish; if it gave a longer T_DeltaV, even the Swi=6% case could show a larger LSE. I request either a derivation of Eq. (1) from a stated transport model, a validation against the unsteady-state model of Boujelben et al., or a sensitivity analysis showing that the qualitative conclusions are unchanged under alternative plausible forms for T_DeltaV.
[III.B, Figures 6-10] All reported results appear to come from a single network realization for each parameter set. Pore-network simulations with randomly assigned radii, contact angles, and wettability are stochastic, and the claimed differences (62.2% vs. 58.1% pores displaced; 4.9% vs. 1.9% incremental oil for Swi=6%; 4.9% vs. 1.6% for Z=4.5) could be within realization-to-realization variability. No error bars, ensemble averages, or statistical tests are provided. The paper should either report statistics over multiple independent realizations (even a modest number) or explicitly state that the quantitative trends are single-realization observations; without this, the quantitative sensitivity claims in the abstract and conclusions are not established.
[IV and III.A] Several load-bearing supporting results are reported only as 'results not shown' or with no explicit simulation output. In particular, the alpha trend in Section IV is given as 2.5% incremental oil for alpha=0.465 and 9.1% for alpha=0.535, and this trend is used in the conclusions to assert that alpha is a critical control on the LSE, but no figure, simulation details, or error information are provided. Similarly, the claims that FW and MWS networks show 'little or no additional oil recovery' (Section III.A) and that delayed LS injection supports the proposed theory (Section III.A) are unverifiable. Given that these results are central to the stated parameter-dependence conclusions, the authors should either present the supporting simulations or explicitly downgrade these statements to preliminary hypotheses.
[IV, Fig. 11] The abstract and conclusions state that increased microscopic sweep efficiency in the OW pores is necessary but not sufficient to guarantee additional oil production. The 'necessary' part is well supported by the displacement logic and by the reported simulations. The 'not sufficient' part, however, rests on schematic arguments in Fig. 11 and on 'observations from an extensive model sensitivity analysis' for which no explicit numerical results are reported, including cases where OW pores become WW or WW pores become OW. Because this statement appears as a central, abstract-level claim, the authors should provide at least one documented simulation showing increased microscopic sweep efficiency without a net increase in oil production, or explicitly rephrase the claim as a conjecture from the authors' unpublished sensitivity work rather than a demonstrated result.

minor comments (4)

[II.C.1, Eq. (1)] Equation (1) is undefined when DeltaP_old equals DeltaP_new, and the paper does not specify what happens if the pressure drop increases or stays constant between steps. A short comment on the domain of validity would prevent ambiguity in re-implementation.
[II.D, Eq. (2)] The Heaviside modification rule treats C_N < C* and C_N > C* but does not specify the boundary case C_N = C*. Since this equality determines whether a pore is modified, the strict inequality should be stated explicitly.
[III.A] The term 'early tertiary' is used for LS injection commencing at water breakthrough following HS injection; in conventional terminology, tertiary flooding typically follows a secondary flood to near its economic limit. The authors should clarify this definition to avoid confusion with standard experimental protocols.
[Figures 5 and 7] The pore-size occupancy histograms would be easier to interpret if the y-axis were normalized to the total number of pores in each bin or if the bin totals were stated, since the raw counts differ between the two network configurations and make cross-figure comparison difficult.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's central claims are consequences of the stated capillary displacement rules and explicit wettability-modification protocol, not restatements of fitted inputs.

full rationale

The paper's central claim—that in heterogeneously wetted networks with spanning WW and OW clusters the OW pores are the only viable source of incremental oil—follows from the model's explicit displacement rules: WW pores are always imbibed when connected to water, LS injection does not change wetting class, and OW drainage proceeds in a volumetrically favorable largest-to-smallest sequence. This is a derived consequence of capillary entry physics, not a restatement of an input. The tracer algorithm's Eq. (1) is admittedly heuristic ('chosen to reflect qualitative observations from experimental coreflooding studies'), but it is a modeling assumption that shapes the simulations; it is not fitted to the paper's own outcome data, and the paper explicitly disclaims quantitative prediction. The wettability-modification rule (Eq. (2)) and the Δθ = 20° shift are inputs taken from external experimental work (Khishvand et al.), not extracted from a subset of the simulated results. Citations to the authors' prior work (Watson et al. 2017; Boujelben et al. 2018) supply the modeling framework and vocabulary (pore sequence effect, microscopic sweep efficiency), but the relevant equations and displacement rules are restated in full in this paper, so the conclusions do not reduce to those citations. No step makes a prediction equivalent to its own inputs by construction.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

No new physical entities are postulated; the tracer is a numerical tag, and 'pore sequence effect' and 'microscopic sweep efficiency effect' are interpretive categories, not entities.

free parameters (6)

Critical tracer concentration C* = 0.8
Chosen so that wettability modification requires at least fivefold dilution of HS brine; controls spatial extent of the LSE in the simulations (Section II.D).
Contact angle change Delta theta = 20 degrees
Chosen to match the average 16 degree shift measured by Khishvand et al.; sets the magnitude of entry-pressure changes and thus the strength of the LSE.
Initial contact angles theta_HS,WW and theta_HS,OW = 60 and 140 degrees
Represent moderate water-wet and strong oil-wet pores; these values determine the capillary entry pressures and filling sequences.
Network geometry (size, PSD, volumetric exponent, Zbar) = 30x25x25; R in [1,50] or [1,16] micrometers; nu=2 or 0.5; Zbar=5 or 3.5
Idealized network properties chosen for spanning clusters and computational feasibility; PSD width and Zbar are also study variables.
Initial water saturation Swi = 0.12 (HS in about 20% of pores)
Chosen as a realistic connate water saturation for secondary injection; Swi is one of the key factors studied.
Pc step size = about 100 steps, about 1% of oil-filled pores per step
Represents an intermediate water injection rate and determines tracer timing; the paper notes this is a deliberate modeling choice.

assumptions (6)

domain assumption Pore filling obeys the capillary entry criterion Pc = 2 sigma cos(theta)/R with piston-like displacement only.
Invoked in Section II.B; this is the core displacement rule for the steady-state model and excludes film/corner flow and snap-off.
domain assumption Viscous forces are negligible; displacement is purely capillary.
Stated in Sections I and II as the steady-state assumption, applicable to far-field reservoir regions.
ad hoc to paper Wettability modification follows the Heaviside law in Eq. (2): contact angle changes only when neighbor salinity exceeds C*.
Adopted in Section II.D in the absence of definitive salinity-wettability data; this threshold rule is the mechanism that produces the LSE in the model.
ad hoc to paper The tracer update time TDeltaV is given by Eq. (1), whose form is chosen to reflect qualitative experimental observations.
Section II.C.1 states the format is 'chosen to reflect qualitative observations'; this heuristic controls when oil-wet pores are exposed to low-salinity brine.
domain assumption Non-spanning water clusters mix HS and LS brines perfectly; spanning clusters mix by advection only.
Assumed in Section II.C.2; the authors argue this is reasonable for tortuous pore space and non-spanning clusters.
domain assumption Connate HS brine resides in the smallest pores and those pores remain water-wet.
Initialized in Section III.B via primary drainage into a 100% water-wet network before wettability is assigned; this affects the delay of the salinity front.

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Pith. "Pith review of A Mechanistic Pore-Scale Analysis of the Low-Salinity Effect in Heterogeneously Wetted Porous Media." pith.science (2026). https://pith.science/paper/LQ6TRTBC

@misc{pith2026190802874,
  author       = {Pith},
  title        = {Pith review of: A Mechanistic Pore-Scale Analysis of the Low-Salinity Effect in Heterogeneously Wetted Porous Media},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LQ6TRTBC}},
  note         = {Machine review of arXiv:1908.02874}
}

read the original abstract

The enhanced oil recovery technique of low-salinity (LS) water flooding is a topic of substantial interest in the petroleum industry. Studies have shown that LS brine injection can increase oil production relative to conventional high-salinity (HS) brine injection, but contradictory results have also been reported and an understanding of the underlying mechanisms remains elusive. We have recently developed a steady-state pore network model to simulate oil recovery by LS brine injection in uniformly wetted pore structures (Watson et al., Transp. Porous Med. 118, 201-223, 2017). We extend this approach here to investigate the low-salinity effect (LSE) in heterogeneously wetted media. We couple a model of capillary force-driven fluid displacement to a novel tracer algorithm and track the salinity front in the pore network as oil and HS brine are displaced by injected LS brine. The wettability of the pore structure is modified in regions where water salinity falls below a critical threshold, and simulations show that this can have significant consequences for oil recovery. For networks that contain spanning clusters of both water-wet and oil-wet (OW) pores prior to flooding, our results demonstrate that the OW pores contain the only viable source of incremental oil recovery by LS brine injection. Moreover, we show that a LS-induced increase in microscopic sweep efficiency in the OW pore fraction is a necessary, but not sufficient, condition to guarantee additional oil production. Simulations suggest that the fraction of OW pores in the network, the average network connectivity and the initial HS brine saturation are key factors that can determine the extent of any improvement in oil recovery in heterogeneously wetted networks following LS brine injection. This study highlights that the mechanisms of the LSE can be markedly different in uniformly wetted and non-uniformly wetted porous media.

Figures

Figures reproduced from arXiv: 1908.02874 by the authors.

**Figure 2.** FIG. 2. Snapshots of an evolving salinity front during a typical model simulation where injected [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

**Figure 3.** FIG. 3. Plot that shows how the number of spanning wettability clusters in a regular 3D pore [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

Figures from the paper (8 more)

**Figure 4.** Figure 4: FIG. 4. Simulated [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]

**Figure 5.** Figure 5: FIG. 5. Pore size fluid occupancy plots that correspond to the (a, c, e) HS brine injection and (b, [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]

**Figure 6.** Figure 6: FIG. 6. Simulated [PITH_FULL_IMAGE:figures/full_fig_p026_6.png]

**Figure 7.** Figure 7: FIG. 7. Pore size fluid occupancy plots that correspond to the (a, c, e) HS brine injection and (b, [PITH_FULL_IMAGE:figures/full_fig_p027_7.png]

**Figure 8.** Figure 8: FIG. 8. Plots of (a) the size distribution and (b) the spatial distribution of water-filled pores at [PITH_FULL_IMAGE:figures/full_fig_p028_8.png]

**Figure 9.** Figure 9: FIG. 9. Simulated [PITH_FULL_IMAGE:figures/full_fig_p030_9.png]

**Figure 10.** Figure 10: FIG. 10. Simulated [PITH_FULL_IMAGE:figures/full_fig_p031_10.png]

**Figure 11.** Figure 11: FIG. 11. Schematic plots showing idealised depictions of potential outcomes from representative [PITH_FULL_IMAGE:figures/full_fig_p038_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

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microscopic sweep eﬃciency eﬀect

Physical Interpretation Based on the tracer injection methodology described above, it should be clear that the outcome of a given LS waterﬂooding simulation will intrinsically depend on the number of Pc steps that are taken to displace the resident oil. This is a deliberate feature of the model and reﬂects the fact that the Pc step size is correlated to t...

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