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REVIEW 4 major objections 5 minor 30 references

Reactive Transport Simulation of Silicate-Rich Shale Rocks when Exposed to CO2 Saturated Brine Under High Pressure and High Temperature

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that a 1D CrunchTope model of Permian shale in CO2-saturated brine reproduces measured mineral-reaction depths (1080 and 1480 μm vs.

desk verdict A transparent but overclaimed CrunchTope validation study: the model reproduces reaction depths and qualitative trends, but not the two dominant mechanisms (quartz stress corrosion, clay swelling) that the experiments flag. read the letter →

arxiv 2506.05122 v1 pith:ZQ525EZM submitted 2025-06-05 physics.geo-ph physics.comp-ph

Shaziya A. Banu , Venkata R.S.B. Varanasi , Arash Noshadravan , Sara Abedi This is my paper
classification physics.geo-phphysics.comp-ph
keywords CO2storagereactivetransportmodelingshaleCrunchTopemineraldissolutionandprecipitationbrine-rockinteractionporosityevolutionchemo-mechanicalalteration
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 tries to establish that reactive transport simulation can stand in for expensive rock experiments when assessing whether shale formations are safe, long-term storage sites for carbon dioxide. Using the CrunchTope code, the authors build a one-dimensional model of a Permian shale exposed to CO2-saturated brine at 100 °C and 12.4 MPa, and compare its mineral-phase evolution after 14 and 28 days against laboratory measurements. The simulated reaction depths—about 1080 and 1480 μm—land close to the measured 1100 and 1500 μm, and the model captures the same qualitative sequence: quartz dissolves then partly reprecipitates, clay precipitates, and feldspar dissolves near the brine-rock interface. The paper also shows porosity rises from 5% to about 15–18%, with only about 1% transient mineral precipitation, so pore spaces are not sealed. If these results hold, reactive transport modeling becomes a quick, low-cost screening tool for CO2 storage and for predicting where chemo-mechanical damage may concentrate.

What carries the argument

The central mechanism is the coupled reaction-transport loop implemented in CrunchTope, an open-source reactive transport code. Aqueous species diffuse through a 1D brine-rock grid under a porosity-dependent effective diffusion coefficient $D_i^e = \phi^m D_i$, while mineral dissolution and precipitation obey the transition-state-theory rate law $R = A k a_{H^+}^n (1 - IAP/K_{sp})$. Nucleation of secondary phases (amorphous SiO2, kaolinite, gibbsite) is added through the classical nucleation barrier $\Delta G^* = 16\pi v^2 a^3 / [3 k_B^2 T^2 (\ln(IAP/K_{sp}))^2]$, and porosity is updated from the evolving mineral volume fractions. This machinery converts the batch-reaction chemistry into spatial phase-concentration profiles that can be compared directly with the SEM-EDS measurements.

What would settle it

At 14 days, the paper reports the simulated quartz-rich phase concentration at the brine-rock interface as 48.30%, against about 13.90% measured; a decisive check would be to add a stress-corrosion-cracking rate law for quartz and see whether the interface concentration drops toward the measured value while still matching the reaction depths and porosity profile. If the quartz concentration remains near its initial 54.9% while the measured value is near 13.9%, the claim that the simulation replicates chemo-mechanical alteration is falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a continuum-scale, one-dimensional reactive transport simulation can replicate the key pore-scale chemo-mechanical response of silicate-rich shale to CO2-saturated brine: feldspar dissolves, quartz dissolves and then partly reprecipitates, clay precipitates near the reacted surface, and the reaction fronts advance at 1080 μm (14 days) and 1480 μm (28 days), matching the experimentally measured 1100 μm and 1500 μm. The authors conclude that CrunchTope successfully simulated the pore-scale transport processes and chemo-mechanical reactions in the shale, including the nucleation and growth of secondary minerals such as amorphous SiO2 and kaolinite, and that the simulation results are validated by the experimental findings. The paper is explicit that two experimentally observed processes—stress-corrosion cracking of quartz and clay swelling—are not captured by the code, which is why the simulated magnitudes of quartz dissolution and clay precipitation fall short of the measured values.

Load-bearing premise

The load-bearing premise is that quartz is essentially inert and highly weathering-resistant, which the experiments contradict by showing large quartz loss through stress-corrosion cracking, and if that premise fails the simulated mineral balance and chemo-mechanical predictions understate reality; the model also cannot simulate clay swelling, further limiting clay-phase magnitudes.

Editorial extensions

If this is right

  • Reaction depth grows with the square root of exposure time, from 1080 μm at 14 days to 1480 μm at 28 days, matching the measured 1100 μm and 1500 μm; this means shorter simulations can be extrapolated to longer exposures by Fickian scaling.
  • Porosity near the brine-rock interface rises from 5% to about 15–18%, with only about 0.89–1.19% mineral precipitation, so CO2-saturated brine does not seal shale pores by precipitation and further brine diffusion remains possible.
  • The model nucleates and grows secondary kaolinite and amorphous SiO2, reproducing the experimentally inferred pathway in which feldspar transforms to clay and quartz precipitates.
  • Because the simulated reaction fronts align with measured depths, 1D reactive transport runs can estimate the thickness of the altered zone in shale caprocks without a full experimental campaign.

Reading between the lines

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

  • A natural next test is to replace the inert-quartz assumption with a stress-corrosion-cracking rate law; if calibrated to the measured interface concentration, the model could predict the experimentally observed >50% drop in quartz-rich indentation modulus and thus caprock mechanical integrity, not just mineralogy.
  • The same 1D setup could be extended to 2D or 3D with explicit fractures to ask whether the thin precipitation bands (0.89–1.19%) change fracture permeability differently than matrix porosity.
  • Because the authors validate only phase concentrations and reaction depths, a stronger test of the model would compare simulated effluent chemistry (Na+, SiO2(aq), pH evolution) and nanoindentation maps against the experimental record; the paper reports only pH at the interface, not the full solute record.
  • If the porosity feedback is right, the main risk to storage integrity in silicate-rich shales may be stress-corrosion weakening rather than pore clogging, which would shift monitoring and caprock-failure assessments toward mechanical damage indicators.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper presents a one-dimensional reactive transport simulation (CrunchTope) of a Permian shale sample exposed to CO2-saturated brine at 100 °C and 12.4 MPa for 14 and 28 days, with the stated aim of replicating the experimental chemo-mechanical alteration profiles reported by Prakash et al. (2024). The authors report feldspar dissolution, dissolution followed by precipitation of quartz-rich phases, clay precipitation, and reaction depths of 1080 μm and 1480 μm at 14 and 28 days, respectively, compared with experimental depths of 1100 μm and 1500 μm. They conclude that CrunchTope successfully simulated the pore-scale transport processes and chemo-mechanical reactions, while acknowledging in Section 4 two key limitations: the model cannot represent quartz dissolution by stress corrosion cracking or clay swelling.

Significance. If convincingly validated, the simulation would offer a faster and cheaper complement to batch experiments for screening shale-CO2 interactions. The paper has notable strengths: it states the governing equations and parameter values clearly, it compares against a specific experimental dataset, and it explicitly enumerates model limitations. However, as presented, the validation is largely qualitative: the two chemo-mechanical mechanisms identified by the experimental study as dominant—quartz stress-corrosion cracking and clay swelling—are absent from the model, and the quantitative phase-concentration agreement is poor. The reaction-depth agreement is consistent with a diffusion-controlled front under generic reactive chemistry and is not sufficient to establish predictive skill. The manuscript therefore needs substantial revision to temper its central claim and to provide a more rigorous validation.

major comments (4)
  1. [Section 4; Section 3.1] The concluding claim that "CrunchTope successfully simulated the pore-scale transport processes and chemo-mechanical reactions" is not supported by the quantitative comparison in Section 3.1. The simulated quartz-rich phase concentration falls only from 54.9% at 30 μm to 48.3% at the brine-rock interface, while the experimental profile falls from about 44.6% at 132 μm to about 13.9% at the interface. Section 3.1 also states that simulated clay precipitation is lower than measured because CrunchTope cannot represent clay swelling. Since quartz dissolution and clay swelling are the two dominant chemo-mechanical mechanisms identified in the experiments, the conclusion should be revised to claim reproduction of the qualitative reaction sequence and penetration depth, not successful simulation of chemo-mechanical reactions.
  2. [Section 2.2] The model is configured to reproduce the experimental outcomes in a circular manner. The authors write that "for actual replication of the experimental study, the nucleation of amorphous silicon dioxide, kaolinite, and gibbsite was considered," and they add a thin inert substrate layer in the brine domain to trigger this nucleation. The simulated precipitation of these phases is therefore an input assumption rather than an independent model prediction. Please separate calibrated inputs from predicted outputs and provide a sensitivity analysis (e.g., varying J0, interfacial energy a, and the presence of the substrate) to demonstrate that the main conclusions do not depend on these ad hoc choices.
  3. [Section 3.1; Eq. (3)] The reaction-depth agreement (1080 vs 1100 μm at 14 days, and 1480 vs 1500 μm at 28 days) is presented as validation, but a diffusion-controlled reaction front advances as the square root of time under almost any reactive chemistry, so this agreement is weak evidence. The front location is controlled by the effective diffusion coefficient D_i^e = φ^m D_i (Eq. 3) with m = 2.40 and D = 1.48×10^−9 m²/s, values that were selected without independent constraint, and the inert nucleation substrate introduced in Section 2.2 also influences the front. Please report parameter sensitivity or uncertainty bounds (e.g., variations in m, D, J0, and interfacial energy) to show that the depth match is not merely a tuning outcome.
  4. [Section 3.4] The porosity profile in Section 3.4 is presented without any comparison to experimental porosity measurements. The statement that porosity increases from 5.0% to 15.3% at 14 days and to 17.9% at 28 days is a model output, not a validated result. Since porosity evolution is a central component of the claimed "transport processes," either provide the corresponding experimental data or present this as a qualitative model prediction rather than a validated outcome.
minor comments (5)
  1. [Table 1] The mineral proportions in Table 1 sum to 105.14% (54.59 + 23.31 + 16.61 + 2.54 + 1.67 + 1.27 + 5.15), which is internally inconsistent; please clarify whether these are weight fractions, volume fractions, or unnormalized values and correct the normalization.
  2. [Section 2.1, Eq. (1)] The notation in Eq. (1) is confusing because the summation index k is used both for the denominator and for the running index k = 1, ..., n, while the left-hand side uses a different index i; please rewrite the equation with distinct indices and define φ, w_i, and ρ_i explicitly.
  3. [Abstract; Section 2.2] The software name is spelled inconsistently as "Crunch Tope" in the Abstract and "CrunchTope" in the body; please use a single consistent spelling throughout the manuscript.
  4. [References] Reference 27 (Varanasi, n.d.) is incomplete; please provide the full dissertation title, year, and institution, or remove the citation if it is not essential.
  5. [Figures] The manuscript refers to Figures 1–6, but the figures are not embedded in the text of the submission; please ensure that all figures are included with sufficient resolution so that the experimental data points can be distinguished from the simulated curves.

Circularity Check

1 steps flagged · score 6.0 of 10

Partial circularity: precipitation of SiO2(am), kaolinite, and gibbsite is inserted as model input to replicate the experiments, then reported as a successful simulation.

  1. fitted input called prediction [Section 2.2 (Reactive Transport Simulation Modeling); see also Section 4 (Conclusion)]
    "The model was also updated to include the nucleation of the minerals, such as amorphous silicon dioxide (SiO2(am)), kaolinite, and gibbsite. To simulate the nucleation of these minerals, a thin inert substrate layer was included in the brine domain closer to the brine rock interface."

    The Introduction states this was done "for actual replication of the experimental study" (Section 1), so the phases that the experiments had already shown precipitating are inserted into the model as nucleating phases with an artificial inert substrate.

full rationale

This is a validation-style study rather than a first-principles derivation, and the self-citation to Prakash et al. (2024) is not itself circular in the harmful sense: the cited nanoindentation/SEM-EDS data are external, falsifiable experimental measurements, not outputs of the model. The main circularity is narrower and located at the nucleation setup. The authors add nucleation of SiO2(am), kaolinite, and gibbsite "for actual replication of the experimental study" (Section 1) and install a thin inert substrate layer to make those phases nucleate near the interface (Section 2.2), then treat the resulting precipitation as evidence that CrunchTope "successfully simulated" the chemo-mechanical reactions (Section 4). That specific validated outcome is therefore an input assumption rather than a prediction. The paper's own limitations are correctness concerns, not additional circularity: Section 3.1 admits "CrunchTope simulated results do not seem to demonstrate significant dissolution" for quartz and "CrunchTope is its inability to simulate the swelling of the clay phase," which undercut the strength of the validation but do not make the derivation self-referential. The reaction-depth agreement is also weak evidence because a diffusion front scales with the square root of time under Fickian transport, but this is not counted as circular since the paper does not state that the diffusion coefficient or cementation exponent were fitted to those depths. Overall, one central "prediction" reduces to the model input, giving partial circularity (score 6) rather than a fully forced derivation.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The model depends on several assumed parameters (cementation exponent, diffusion coefficient, nucleation constants) and on domain assumptions that exclude or simplify key experimental mechanisms (quartz stress-corrosion cracking, clay swelling, 3D heterogeneity). The inclusion of secondary mineral nucleation is a deliberate calibration to the target experimental data, which raises the circularity burden.

free parameters (5)
  • Cementation exponent m = 2.40
    Chosen to account for rock tortuosity; not measured for this specimen. It directly controls the effective diffusion coefficient in Eq. (3), and thus the reaction front depth.
  • Effective diffusion coefficient D = 1.48e-9 m2/s
    Uniform value assumed for all aqueous species at 100 °C. This parameter strongly influences the simulated reaction depth and mineral alteration profiles.
  • Nucleation kinetic factor J0 = 1.00e-8 mol/m2/s
    Used in Eq. (5) for nucleation rate of secondary minerals; cited to Li et al. (2017) but not independently constrained for the Permian rock.
  • Interfacial energy a = 47 ± 1 mJ/m2
    Parameter in the nucleation energy barrier, Eq. (6); adopted from literature, not measured for this specimen.
  • Inclusion of SiO2(am), kaolinite, and gibbsite nucleation = n/a
    Ad hoc addition of a thin inert substrate layer in the brine domain to force nucleation of these secondary minerals, explicitly intended to replicate experimental observations (Section 2.2).
assumptions (5)
  • domain assumption Quartz is a highly stable and insoluble mineral with high resistance to weathering
    Invoked in Section 3.1; leads the model to under-predict quartz dissolution relative to experiments, a limitation the authors acknowledge.
  • domain assumption Clay swelling and grain detachment need not be simulated
    Section 3.1 notes CrunchTope cannot simulate clay swelling, yet this is a key experimental process; the model therefore omits a load-bearing mechanism.
  • standard math Transition state theory (TST) rate law describes mineral dissolution and precipitation kinetics
    Eq. (4) is the standard rate law implemented in CrunchTope and applies to all minerals in the model.
  • domain assumption A single uniform diffusion coefficient represents all aqueous species
    Justified in Section 2.2 by order-of-magnitude similarity of diffusivities; this simplification controls species transport and reaction front propagation.
  • domain assumption A 1D domain captures the pore-scale chemo-mechanical response of the heterogeneous rock
    The model resolves only one dimension perpendicular to the reacted surface, ignoring lateral heterogeneity, fractures, and mechanical deformation.
invented entities (1)
  • Thin inert substrate layer for mineral nucleation
    purpose: Provides a surface in the brine domain for nucleation of SiO2(am), kaolinite, and gibbsite
    This is a numerical construct inserted to trigger precipitation of secondary minerals, matching experimental observations; no independent physical evidence is provided.

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Cite this review

Pith. "Pith review of Reactive Transport Simulation of Silicate-Rich Shale Rocks when Exposed to CO2 Saturated Brine Under High Pressure and High Temperature." pith.science (2026). https://pith.science/paper/ZQ525EZM

@misc{pith2026250605122,
  author       = {Pith},
  title        = {Pith review of: Reactive Transport Simulation of Silicate-Rich Shale Rocks when Exposed to CO2 Saturated Brine Under High Pressure and High Temperature},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZQ525EZM}},
  note         = {Machine review of arXiv:2506.05122}
}
read the original abstract

This study examines the feasibility of carbon dioxide storage in shale rocks and the reliability of reactive transport models in achieving accurate replication of the chemo-mechanical interactions and transport processes transpiring in these rocks when subjected to CO2 saturated brine. Owing to the heterogeneity of rocks, experimental testing for adequate deductions and findings, could be an expensive and time-intensive process. Therefore, this study proposes utilization of reactive transport modeling to replicate the pore-scale chemo-mechanical reactions and transport processes occurring in silicate-rich shale rocks in the presence of CO2 saturated brine under high pressure and high temperature. For this study, Crunch Tope has been adopted to simulate a one-dimensional reactive transport model of a Permian rock specimen exposed to the acidic brine at a temperature of 100 {\deg}C and pressure of 12.40 MPa (1800 psi) for a period of 14 and 28 days. The results demonstrated significant dissolution followed by precipitation of quartz rich phases, precipitation and swelling of clay rich phases, and dissolution of feldspar rich phases closer to the acidic brine-rock interface. Moreover, porosity against reaction depth curve showed nearly 1.00% mineral precipitation occur at 14 and 28 days, which is insufficient to completely fill the pore spaces.

Figures

Figures reproduced from arXiv: 2506.05122 by the authors.

Figure 1
Figure 1. CrunchTope simulation model framework For the cementation coefficient of the Permian rock, a value of 2.40 was used in the model, due to the significant tortuosity of the rock specimen. The diffusion coefficient for each species of the rock was set at a value of 1.48 x 10−9 m2 /s for a temperature of 100 °C. Since the diffusivities are in order of 10−9 m2 /s, utilization of a uniform diffusion coefficient is justifi… view at source ↗
Figure 2
Figure 2. Quartz-rich, clay-rich, and feldspar-rich phase concentrations of Permian rock at 14 days using CrunchTope simulation and experimental findings from Prakash et al. (2024) [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. provides the simulated results of phase concentrations over the reacted distance against the experimental findings from (Prakash et al., 2024) at 28 days of reaction time. The phase concentrations of quartz-rich, clay-rich, and feldspar-rich phases demonstrate similar behavior at 14 and 28 days of reaction time (refer to Figs. 2 and 3). So, similar to the reaction of 14 days, 28 days showed dissolution followed by p… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Normalized phase concentrations of quartz-rich, clay-rich, and feldspar-rich phases of Permian rock at 14 and 28 days using CrunchTope simulation 3.3. pH of Brine and Rock Specimen [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Variation of pH of CO2-saturated brine and Permian rock over time 3.4. Porosity of the Rock Specimen [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Porosity variation with respect to the distance from the reacted surface of the rock specimen [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

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Reviewed August 7, 2026 · model on record in the stance chip above.