arXiv subjects

Mohammad Nooraiepour

Publications and source records attributed to Mohammad Nooraiepour.

17 recordsLinked to original sources

arXiv2026

Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification

Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfer across data-scarce geological systems, and calibrated uncertainty estimates, capabilities that are rarely addressed together in existing frameworks. We present a physics-informed transfer learning framework that adapts a hydrogen sorption PINN to methane sorption prediction via Elastic Weight Consolidation, coal-specific feature engineering, and a three-phase curriculum that progressively balances transfer preservation with thermodynamic fine-tuning. Trained on 993 equilibrium measurements from 114 independent coal experiments spanning lignite to anthracite, the framework achieves R2 = 0.932 on held-out coal samples, a 227% improvement over pressure-only classical isotherms, while hydrogen pre-training delivers 18.9% lower RMSE and 19.4% faster convergence than random initialization. Five Bayesian uncertainty quantification approaches reveal a systematic divergence in performance across physics-constrained architectures. Monte Carlo Dropout achieves well-calibrated uncertainty at minimal overhead, while deep ensembles, regardless of architectural diversity or initialization strategy, exhibit performance degradation because shared physics constraints narrow the admissible solution manifold. SHAP and ALE analyses confirm that learned representations remain physically interpretable and aligned with established coal sorption mechanisms: moisture-volatile interactions are most influential, pressure-temperature coupling captures thermodynamic co-dependence, and features exhibit non-monotonic effects. These results identify Monte Carlo Dropout as the best-performing UQ method in this physics-constrained transfer learning framework, and demonstrate cross-gas transfer learning as a data-efficient strategy for geological material modeling.

cs.LG↗

arXiv2026

Physics-Informed Neural Networks for Predicting Hydrogen Sorption in Geological Formations: Thermodynamically Constrained Deep Learning Integrating Classical Adsorption Theory

Accurate prediction of hydrogen sorption in fine-grained geological materials is essential for evaluating underground hydrogen storage capacity, assessing caprock integrity, and characterizing hydrogen migration in subsurface energy systems. Classical isotherm models perform well at the individual-sample level but fail when generalized across heterogeneous populations, with the coefficient of determination collapsing from 0.80-0.90 for single-sample fits to 0.09-0.38 for aggregated multi-sample datasets. We present a multi-scale physics-informed neural network framework that addresses this limitation by embedding classical adsorption theory and thermodynamic constraints directly into the learning process. The framework utilizes 1,987 hydrogen sorption isotherm measurements across clays, shales, coals, supplemented by 224 characteristic uptake measurements. A seven-category physics-informed feature engineering scheme generates 62 thermodynamically meaningful descriptors from raw material characterization data. The loss function enforces saturation limits, a monotonic pressure response, and Van't Hoff temperature dependence via penalty weighting, while a three-phase curriculum-based training strategy ensures stable integration of competing physical constraints. An architecture-diverse ensemble of ten members provides calibrated uncertainty quantification, with post-hoc temperature scaling achieving target prediction interval coverage. The optimized PINN achieves R2 = 0.9544, RMSE = 0.0484 mmol/g, and MAE = 0.0231 mmol/g on the held-out test set, with 98.6% monotonicity satisfaction and zero non-physical negative predictions. Physics-informed regularization yields a 10-15% cross-lithology generalization advantage over a well-tuned random forest under leave-one-lithology-out validation, confirming that thermodynamic constraints transfer meaningfully across geological boundaries.

cs.LG↗

arXiv2026

Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer

Accurate prediction of permeability tensors from pore-scale microstructure images is essential for subsurface flow modeling, yet direct numerical simulation requires hours per sample, fundamentally limiting large-scale uncertainty quantification and reservoir optimization workflows. A physics-informed deep learning framework is presented that resolves this bottleneck by combining a MaxViT hybrid CNN-Transformer architecture with progressive transfer learning and differentiable physical constraints. MaxViT's multi-axis attention mechanism simultaneously resolves grain-scale pore-throat geometry via block-local operations and REV-scale connectivity statistics through grid-global operations, providing the spatial hierarchy that permeability tensor prediction physically requires. Training on 20000 synthetic porous media samples spanning three orders of magnitude in permeability, a three-phase progressive curriculum advances from an ImageNet-pretrained baseline with D4-equivariant augmentation and tensor transformation, through component-weighted loss prioritizing off-diagonal coupling, to frozen-backbone transfer learning with porosity conditioning via Feature-wise Linear Modulation (FiLM). Onsager reciprocity and positive definiteness are enforced via differentiable penalty terms. On a held-out test set of 4000 samples, the framework achieves variance-weighted R2 = 0.9960 (R2_Kxx = 0.9967, R2_Kxy = 0.9758), a 33% reduction in unexplained variance over the supervised baseline. The results offer three transferable principles for physics-informed scientific machine learning: large-scale visual pretraining transfers effectively across domain boundaries; physical constraints are most robustly integrated as differentiable architectural components; and progressive training guided by diagnostic failure-mode analysis enables unambiguous attribution of performance gains across methodological stages.

cs.LG↗

arXiv2026

Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical, Machine Learning, and Hybrid Methods

Partial differential equations (PDEs) govern physical phenomena across the full range of scientific scales, yet their computational solution remains one of the defining challenges of modern science. This critical review examines two mature but epistemologically distinct paradigms for PDE solution, classical numerical methods and machine learning approaches, through a unified evaluative framework organized around six fundamental computational challenges. Classical methods are assessed for their structure-preserving properties, rigorous convergence theory, and scalable solver design; their persistent limitations in high-dimensional and geometrically complex settings are characterized precisely. Machine learning approaches are introduced under a taxonomy organized by the degree to which physical knowledge is incorporated and subjected to the same critical evaluation applied to classical methods. Classical methods are deductive -- errors are bounded by quantities derivable from PDE structure and discretization parameters -- while machine learning methods are inductive -- accuracy depends on statistical proximity to the training distribution. This epistemological distinction is the primary criterion governing responsible method selection. We identify three genuine complementarities between the paradigms and develop principles for hybrid design, including a framework for the structure inheritance problem that addresses when classical guarantees propagate through hybrid couplings, and an error budget decomposition that separates discretization, neural approximation, and coupling contributions. We further assess emerging frontiers, including foundation models, differentiable programming, quantum algorithms, and exascale co-design, evaluating each against the structural constraints that determine whether current barriers are fundamental or contingent on engineering progress.

cs.LG↗

arXiv2026

Quantifying Salt Precipitation During CO2 Injection: How Flow Rate, Temperature, and Phase State Control Near-Wellbore Crystallization

Salt precipitation near injection wells can reduce permeability, induce excess pressure buildup, and reduce injectivity within days to weeks of CO2 injection, yet the pore-scale mechanisms coupling multiphase flow, evaporation, and crystallization warrant further detailed quantification across variable phase states and flow regimes. We present high-resolution microfluidic experiments that systematically quantify the dynamics of halite crystallization during CO2-driven brine evaporation across liquid, gaseous, and supercritical phases (50-80 bar, 20--60 C, Pe = 50--1440). Crystallization kinetics are controlled by transport, with the Avrami rate constant (K) increasing by two orders of magnitude with the Peclet number and exhibiting the dependence of the temperature of Arrhenius (Ea = 58.6 kJ/mol. Supercritical CO2 achieves superior displacement efficiency (residual saturation 0.22-0.36, fractal dimension D = 1.79-1.82) and the fastest evaporation (Sherwood numbers 2-3x higher than the liquid phase), reducing the nucleation time from 57 min (20C, liquid) to <1 min (40-60 C, gas/supercritical). The final fractions of crystal increase 10-fold from liquid (0.008) to gas-phase conditions (0.08--0.12), confirming that convective transport and phase state dominate over diffusion-limited mechanisms. Despite probabilistic nucleation, final crystal distributions are spatially rather uniform with no systematic inlet-outlet bias. These quantitative relationships between dimensionless parameters (Pe, Sh), kinetic constants (K, Ea) and phase-dependent displacement patterns provide critical benchmarks for validating pore-scale models and predicting near-wellbore permeability impairment in geological storage of saline and hypersaline CO2.

physics.geo-ph↗

arXiv2026

Carbon mineralization in CO2-seawater-basalt systems: Reactive transport dynamics and vesicular pore architecture controls

Carbon mineralization in basaltic rocks may offer rapid, permanent \ce{CO2} storage, yet fundamental controls on reactive transport and precipitation patterns remain poorly understood. This study integrates flow-through experiments at 80\degree C using \ce{CO2}-acidified seawater with geochemical simulation and multi-scale pore imaging to elucidate mineralization dynamics in basaltic glass. Results reveal that carbonate precipitation is nucleation-controlled and stochastic rather than growth-controlled and deterministic, with isolated accumulations forming randomly despite continuous supersaturation. Residence time exerts primary control: reducing flow rate from 0.05 to 0.005\,mL/min proved necessary for visible precipitation. Post-experiment analyses identified calcium carbonate and smectite phases. Multi-scale characterization of three basalt facies revealed that connected porosity fractions (1.3--32\%) differ significantly from total porosity (18--42\%), demonstrating that network topology controls permeability. Micro-CT analysis revealed that pore coordination numbers in basalts (modal = 2) were notably lower than those in reservoir sandstones, creating serial flow paths that are vulnerable to catastrophic permeability loss from modest precipitation. Precipitation-induced clogging scenarios were proposed, where distributed small precipitates cause more severe permeability degradation than large accumulations. The use of seawater complicates geochemistry and reduces mineralization efficiency compared to freshwater. Findings emphasize the need for probabilistic reactive transport modeling frameworks and realistic pore topologies, which are fundamentally different from conventional CCS operations.

physics.geo-ph↗

arXiv2025

Self-Enhancing Halite Growth Creates Secondary Porous Networks During CO2 Storage in Saline Aquifers

Salt precipitation during CO2 injection into saline aquifers obstructs flow-controlling pore throats and reduces permeability, yet reactive transport models assume salt forms dispersed, non-porous crystals with minimal flow impact. We demonstrate that halite instead creates three-dimensional porous networks with 40% internal porosity through self-enhancing growth mechanisms absent from current models. Time-lapse X-ray micro-computed tomography and spectral imaging reveal preferential nucleation at gas-liquid interfaces, where porous hydrophilic aggregates generate capillary suction that draws brine films toward precipitation sites, accelerating growth and expanding reactive surface area in a positive feedback loop. Spectral tomography shows systematic density gradients reflecting two-stage precipitation: dense macrocrystalline cores formed under moderate supersaturation and evaporation, surrounded by microcrystalline overgrowths from rapid late-stage dynamics. These overgrowths create umbrella-like crusts that encapsulate residual brine beneath surface layers. This porous architecture explains why modest porosity reduction causes severe permeability decline, as aggregates preferentially obstruct flow percolation pathways rather than uniformly cementing grains or filling pore space. Five interconnected mechanisms drive self-enhancing growth across nano- to centimeter scales: interface nucleation, secondary porous structure formation, steep concentration gradients, hydrophilic substrate film maintenance, and capillary-driven solute delivery. Our quantitative characterization of internal salt architecture, reactive surface areas, and pore connectivity provides essential parameters for improving predictive models of evaporation-precipitation dynamics in carbon storage, soil salinization, and cultural heritage preservation.

physics.geo-ph↗

arXiv2025

Microfluidic Study of Evaporation-Driven Crystallization of Saline and Ammonia Brines under Hydrogen Flow

Underground storage of hydrogen and ammonia in geological formations is essential for renewable energy integration, but salt precipitation during gas injection may threaten storage performance. While extensively studied for CO2 systems, precipitation mechanisms in hydrogen-brine and ammonia-brine systems remain poorly understood. This study presents a comprehensive microfluidic investigation of salt crystallization during hydrogen injection into saline and ammonia-containing brines using high-pressure microfluidics. We conducted 81 high-pressure experiments systematically varying brine composition (1-5 mol/kg NaCl), chemical additives (surfactants, alcohols, ammonia), and hydrogen flow rates (200-1300 mL/min). Quantitative image analysis reveals that hydrogen-induced precipitation differs fundamentally from CO2 systems. Hydrogen drives physical precipitation via evaporation and capillary trapping, producing discrete, localized deposits. In contrast, CO2-ammonia systems generate extensive reactive precipitation of ammonium bicarbonate with interconnected crystal networks. Interfacial tension (IFT) controls both residual brine distribution and final crystal coverage: high-IFT fluids form large, interconnected brine pools promoting extensive crystallization, while low-IFT fluids create isolated pools reducing crystal coverage by 50\%. Alcohol and surfactant additives suppress precipitation by enhancing brine mobility, whereas ammonia paradoxically increases crystal fractions despite lower IFT. Higher flow rates accelerate crystallization across all compositions, enabling operational mitigation strategies. and demonstrate that gas-specific, rather than CO2-analog, risk assessments are essential for underground hydrogen storage design. The effectiveness of chemical additives offers promising pathways for near-wellbore protection in underground hydrogen storage operations.

physics.flu-dyn↗

arXiv2025

Geological CO2 storage assessment in emerging CCS regions: Review of sequestration potential, policy development, and socio-economic factors in Poland

Emerging carbon capture and storage (CCS) markets face critical challenges in developing systematic methodologies to assess geological CO2 storage potential under conditions of limited data availability, evolving regulatory frameworks, and nascent infrastructure development. This study establishes an assessment framework designed for lower-maturity CCS regions, using Poland as a representative case study to demonstrate methodology application and validate framework effectiveness. The framework integrates geological characterization, storage capacity assessment, regulatory analysis, and socio-economic evaluation through a structured approach adaptable to diverse global contexts. Poland's coal-reliant economy exemplifies the decarbonization challenges facing emerging CCS regions while meeting European Union climate mandates. The country's geological setting offers substantial sequestration opportunities across three major sedimentary regions. Through multidisciplinary analysis synthesizing scattered geological data, policy developments, CCUS value chain, and stakeholder perspectives, we systematically evaluate CO2 storage potential. Onshore saline aquifers and depleted hydrocarbon fields provide significant storage capacity, while offshore Baltic Basin sites face logistical and environmental regulatory constraints. Current assessments encounter critical limitations, including sparse data, restricted research access, and inadequate industry-academia collaboration, preventing basin-scale analyses from advancing to higher storage readiness levels and undermining business decision-making reliability. This study contributes a replicable methodology extending beyond Poland to lower-maturity CCS regions worldwide. The framework provides decision-makers with systematic tools for storage assessment, policy development, and stakeholder engagement, supporting evidence-based CCS deployment strategies.

physics.soc-ph↗

arXiv2025

Bayesian Parametric Matrix Models: Principled Uncertainty Quantification for Spectral Learning

Scientific machine learning increasingly uses spectral methods to understand physical systems. Current spectral learning approaches provide only point estimates without uncertainty quantification, limiting their use in safety-critical applications where prediction confidence is essential. Parametric matrix models have emerged as powerful tools for scientific machine learning, achieving exceptional performance by learning governing equations. However, their deterministic nature limits deployment in uncertainty quantification applications. We introduce Bayesian parametric matrix models (B-PMMs), a principled framework that extends PMMs to provide uncertainty estimates while preserving their spectral structure and computational efficiency. B-PMM addresses the fundamental challenge of quantifying uncertainty in matrix eigenvalue problems where standard Bayesian methods fail due to the geometric constraints of spectral decomposition. The theoretical contributions include: (i) adaptive spectral decomposition with regularized matrix perturbation bounds that characterize eigenvalue uncertainty propagation, (ii) structured variational inference algorithms using manifold-aware matrix-variate Gaussian posteriors that respect Hermitian constraints, and (iii) finite-sample calibration guarantees with explicit dependence on spectral gaps and problem conditioning. Experimental validation across matrix dimensions from 5x5 to 500x500 with perfect convergence rates demonstrates that B-PMMs achieve exceptional uncertainty calibration (ECE < 0.05) while maintaining favorable scaling. The framework exhibits graceful degradation under spectral ill-conditioning and provides reliable uncertainty estimates even in near-degenerate regimes. The proposed framework supports robust spectral learning in uncertainty-critical domains and lays the groundwork for broader Bayesian spectral machine learning.

cs.LG↗

arXiv2025

Adaptive Physics-Informed Neural Networks with Multi-Category Feature Engineering for Hydrogen Sorption Prediction in Clays, Shales, and Coals

Accurate prediction of hydrogen sorption in clays, shales, and coals is vital for advancing underground hydrogen storage, natural hydrogen exploration, and radioactive waste containment. Traditional experimental methods, while foundational, are time-consuming, error-prone, and limited in capturing geological heterogeneity. This study introduces an adaptive physics-informed neural network (PINN) framework with multi-category feature engineering to enhance hydrogen sorption prediction. The framework integrates classical isotherm models with thermodynamic constraints to ensure physical consistency while leveraging deep learning flexibility. A comprehensive dataset consisting of 155 samples, which includes 50 clays, 60 shales, and 45 coals, was employed, incorporating diverse compositional properties and experimental conditions. Multi-category feature engineering across seven categories captured complex sorption dynamics. The PINN employs deep residual networks with multi-head attention, optimized via adaptive loss functions and Monte Carlo dropout for uncertainty quantification. K-fold cross-validation and hyperparameter optimization achieve significant accuracy (R2 = 0.979, RMSE = 0.045 mol per kg) with 67% faster convergence despite 15-fold increased complexity. The framework demonstrates robust lithology-specific performance across clay minerals (R2 = 0.981), shales (R2 = 0.971), and coals (R2 = 0.978), maintaining 85-91% reliability scores. Interpretability analysis via SHAP, accumulated local effects, and Friedman's H-statistics reveal that hydrogen adsorption capacity dominates predictions, while 86.7% of feature pairs exhibit strong interactions, validating the necessity of non-linear modeling approaches. This adaptive physics-informed framework accelerates site screening and enables risk-informed decision-making through robust uncertainty quantification.

cs.LG↗

arXiv2025

Ultrasonic sensing of the mechanical fingerprint of reactive transport in rock

Mineral carbon storage in rock formations has gained significant interest in recent years. In principle, changes in mechanical rock properties driven by carbon mineralization could be quantified using seismic methods, opening the door toward field monitoring of (the progress of) carbon storage. However, these changes may vary spatially within a rock mass when reactive transport occurs. In this vein, full-field ultrasonic characterization of reacted specimens can help shed light on the process. We use a 3D Scanning Laser Doppler Vibrometer to perform full-field monitoring of one-dimensional (1D) ultrasonic waves in rod-shaped sandstone specimens exposed to NaCl-rich fluid. Our initial experiments were conducted on intact sandstone specimens with high aspect ratio (length/diameter $\simeq 15$) to cater for 1D axial wave propagation. To investigate the evolution of the Young's modulus and attenuation of rock due to reactive transport, we exposed the specimens to an under-saturated NaCl solution, achieving supersaturation -- and so mineralization -- through evaporation. The upward movement of the fluid, supplied at the bottom of each specimen, was achieved through capillary action. We deploy an elastography-type approach to back-analysis, known as modified-error-in-constitutive-relation (MECR) approach, to expose the spatially-heterogeneous evolution of mechanical rock properties due to reactive transport. Our results consistently demonstrate (i) degradation of the Young's modulus, and (ii) increase in attenuation due to mineralization. To better understand the root causes of these changes, we made use of the X-ray micro-computed tomography ($μ$CT) and scanning electron microscopy (SEM) of selected cross-sections. The grain-scale information suggests that microcracking (resp. pore filling) is predominantly (resp. partly) responsible for the observed macroscopic changes.

physics.geo-ph↗

arXiv2025

Microfluidic studies of Salt Precipitation: Influence of Brine Composition, Interfacial Tension, Flow Conditions, and Chemical Additives

This study investigates the interfacial tension, fluid mobility, and crystallization behavior of various saline and additive-modified solutions in a microfluidic chip environment, simulating pore-scale processes during CO2 injection. The brine compositions included NaCl solutions at different concentrations, surfactant-modified fluids, alcohol-water mixtures, and ammonia solutions. Microfluidic experiments were performed on a range of flow rates and the dynamics of CO2 breakthrough, brine evaporation, and salt precipitation were analyzed. The results show that higher NaCl concentrations accelerate crystallization and increase the final fraction of the crystal, though they also introduce spatial variability and localized precipitation. Additives such as alkylbenzene sulfonate and propan-2-ol reduce interfacial tension, promote greater mobility, and suppress salt accumulation. Ammonia-based solutions demonstrate rapid ammonia bicarbonate crystallization immediately upon CO2 contact, leading to elevated water saturation and frequent chip clogging. Despite faster brine evaporation and earlier crystal nucleation at higher CO2 flow rates, no significant impact was observed on initial brine saturation or final crystal coverage. Crystal growth occurs within and outside brine pools, driven by capillary flow, with spatial distributions governed by stochastic breakthrough dynamics. The random nature of the residual brine geometry results in heterogeneous crystal patterns, which were found to be repeatable but not deterministic.

physics.geo-ph↗

arXiv2025

The Norwegian-Polish CCS Network: A Case Study in Bilateral Collaboration for European Climate Action

In the face of escalating climate change, achieving significant reductions in greenhouse gas emissions from hard-to-abate industrial sectors is imperative. Carbon Capture and Storage (CCS) represents an essential technological advancement to achieve sustainable decarbonization. This manuscript reports on the bilateral CCS network between Norway and Poland, designed and implemented to leverage their capabilities to expedite technology deployment via mutual cooperation and accelerate CCS initiatives targeted to member states' challenges across Europe, aiming for a meaningful contribution to climate goals. Norway is renowned for its operational acumen, as demonstrated by landmark projects like Sleipner and Snohvit, and its forward-looking initiatives, such as the open-source cross-border Northern Lights project, which offer advanced infrastructure and expertise. Conversely, Poland, characterized by its coal-dependent economy and the challenge of decarbonizing extensive industrial emissions, presents a significant geological CO2 storage potential, estimated at over 15.5 gigatonnes. This study delves into the potential synergies derived from collaborative endeavors in academic education, research and development, industrial implementation, regulatory coherence, and public engagement. By underscoring the reciprocal benefits of such partnership, the study underscores the indispensable role of bilateral cooperation in harnessing CCS's capabilities to meet the EU's ambitious climate objectives, paving the way toward a sustainable and low-carbon future. Additionally, it outlines a scalable model for fostering and supporting broader bilateral and multi-lateral collaborations, emphasizing the pivotal role of interconnected networks in shaping effective global climate action strategies.

physics.soc-ph↗

arXiv2024

How does surface wettability alter salt precipitation and growth dynamics during CO$_2$ injection into saline aquifers: A microfluidic analysis

Salt precipitation triggered by the evaporation of formation brine into injected supercritical CO2 can cause injectivity and containment issues in near-wellbore regions. Predicting the distribution of precipitated salts and their impact on near-wellbore properties remains challenging. This study investigates the influence of surface wettability on CO2-induced halite precipitation and growth within hydrophilic and hydrophobic microfluidic chips designed to mimic rock-structure porous geometries. A series of high-pressure brine-CO2 flow experiments, direct microscopic observations, and detailed image processing were conducted to explore how substrate wettability affects salt precipitation. The experiments show that wettability markedly controls residual brine relocation, film flow movement, solute consumption, and salt formation. Tracking halite precipitation dynamics revealed distinct crystal formation signatures: hydrophilic chips exhibited irregular, larger aggregation patches, while the hydrophobic network showed more numerous, smaller, and limited aggregations. Large individual crystals were observed in both chips, with a notable dominance in the hydrophobic one. Crystallization dynamics varied, with nucleation and growth occurring earlier, progressing faster, and forming bulkier aggregates in the hydrophilic chip. Despite these differences, the three identified temporal stages of brine evaporation and halite surface coverage were comparable. Spatial analysis along the chips indicated that crystal aggregation properties, such as size and distribution, were position-dependent, with hydrophilic chips exhibiting greater probabilistic variability. These observations underscore the impact of surface wettability on salt precipitation through brine accessibility and capillarity, with implications for mitigating and remediating salt issues in saline aquifers.

physics.geo-ph↗

arXiv2024

Intricacies of CO2-Basalt Interactions, Reactive Flow and Carbon Mineralization: Bridging Numerical Forecasts to Empirical Realities

Subsurface fluid flow and solute transport are pivotal in addressing pressing energy, environmental, and societal challenges, such as geological CO2 storage. Basaltic rocks have gained prominence as suitable geological substrates for injecting substantial CO2 volumes and carbon mineralization, driven by their widespread occurrence, high concentrations of cation-rich silicate minerals, reported fast mineralization rate, and favorable characteristics such as porosity, permeability, and injectivity. The mineralization process within basaltic rocks is intricately linked, involving the dissolution of silicate minerals and the subsequent precipitation of carbonate minerals. Columnar flow and batch surface growth experiments revealed the spontaneous formation of a limited number of large crystals at various locations, rationalized by the overarching influence of probabilistic mineral nucleation. Experiments with CO2-acidified brine versus freshwater prove to be more challenging regarding the sweet spots for heavy carbon mineralization due to clay formation on the surface, particularly smectites. Despite numerical predictions suggesting the formation of MgFeCa-carbonates in CO2-basalt interactions at higher temperatures, our laboratory findings primarily indicated the growth of calcium carbonates. The experimental and numerical outcomes highlight the necessity of a probabilistic approach for accurately modeling reaction kinetics, crystal growth distribution, and the dynamic interplay between reactive flow, geochemical reactions, mineral carbonation, and geometry alteration.

physics.flu-dyn↗

arXiv2024

Assessing salt precipitation and weak acid interaction in subsurface CO2 injection: Potential 50% strength decline in near-wellbore reservoir sandstones

Predictive modeling of CO2 storage sites requires a detailed understanding of physico-chemical processes and potential challenges for scale-up. Dramatic injectivity decline may occur due to salt precipitation pore clogging in high-salinity reservoirs, even over a short time frame. This study aims to elucidate the adverse impact of CO2-induced salt crystallization in porous media on the geomechanical properties of near-wellbore reservoir sandstones. As the impact of salt precipitation cannot be isolated from the precursor effects of interaction with CO2 and carbonic acid, we initiated our study by a comprehensive review of CO2 chemo-mechanical interactions with sandstones. We conducted laboratory geochemical CO2-brine-rock interactions at elevated pressures and temperatures on two sets of porous sandstone with contrasting petrophysical qualities. Two paths were followed: treatment with (a) CO2-acidified brine at 10 MPa fluid pressure and 60C for 7 days, and a second subset continuation with (b) supercritical injection until complete dry-out and salt precipitation. Afterward, the core samples were tested in a triaxial apparatus at varying stresses and temperatures. The elastic moduli of intact, CO2-reacted, and salt-damaged sandstones were juxtaposed to elucidate the extent of crystallization damages. The salt-affected specimens showed a maximum of 50 percent reduction in Young's and shear moduli and twice an increase in Poisson's ratio compared to intact condition. The deterioration was notably higher for the tighter rocks with higher initial stiffness.

physics.geo-ph↗