Rock Properties Research Articles

Abstract. Heterogeneous structures and diverse volcanic, hydrothermal, and geomorphological processes hinder characterisation of the mechanical properties of volcanic rock masses. Laboratory experiments can provide accurate rock property measurements, but are limited by sample scale and labor-intensive procedures. In this contribution, we expand on previous research linking the hyperspectral fingerprints of rocks to their physical and mechanical properties. We acquired a unique dataset characterising the visible-near (VNIR), shortwave (SWIR), midwave (MWIR), and longwave (LWIR) infrared reflectance of samples from eight basaltic to andesitic volcanoes. Several machine learning models were then trained to predict density, porosity, uniaxial compressive strength (UCS), and Young's modulus (E) from these spectral data. Significantly, nonlinear techniques such as multilayer perceptron (MLP) models were able to explain up to 80 % of the variance in density and porosity, and 65 %–70 % of the variance in UCS and E. Shapley value analysis, a tool from explainable AI, highlights the dominant contribution of VNIR-SWIR absorptions that can be attributed to hydrothermal alteration, and MWIR-LWIR features sensitive to volcanic glass content, fabric, and/or surface roughness. These results demonstrate that hyperspectral imaging can serve as a robust proxy for rock physical and mechanical properties, potentially offering an efficient, scalable method for characterising large areas of exposed volcanic rock. The integration of these data with geomechanical models could enhance hazard assessment, infrastructure development, and resource utilisation in volcanic regions.

Core data provide valuable in situ information on the chemical and physical characteristics of subsurface formations. Thin sections, for instance, allow us to define rock types with similar mineral composition, lithologies, and pore types (i.e., petrofacies). Petrofacies logs illustrate the stratigraphic variability within a reservoir and are often used to constrain 3-D facies and petrophysical-property models. However, core data are often scarce, prompting us to explore alternative sources. Well logs, with their different vertical resolution, are commonly used to characterize mineralogy, lithology, and porosity. The challenge lies in reconciling the differences between thin sections and well logs. To address this, we developed novel Machine Learning (ML) methodologies. Our goal was to bridge the resolution gap between these two data types and identify subtle variations in rock properties. Specifically, we focused on collocated cores that provided thin-section-based petrofacies and X-ray fluorescence (XRF) data. Our approach involved two semi-supervised methods: self-training and labeled clustering. By combining XRF data with dimensionality reduction techniques, we achieved a reliable classification of thin-section-based petrofacies. Remarkably, both approaches achieved accuracies exceeding 90% on Sycamore Formation data. Among the dimensionality reduction techniques tested. Uniform Manifold Approximation and Projection (UMAP) yielded the best results. The outcome? Petrofacies logs that bridge the resolution gap between core-based thin sections and well-log data. Furthermore, integrating semi-supervised methods into routine core analysis offers substantial cost and time savings. These methods enhance stratigraphic correlation, aid in identifying target zones, design horizontal wells, and constrain subsurface models.

Evolving physical and mechanical rock properties during exposure at Earth's surface

Matrix acidizing impact on the geomechanical properties in carbonate rocks: an experimental approach using acetic acid at different contact times

Understanding the deformation and failure behavior of rock under complex boundary and environmental conditions is critical for the design and stability assessment of geotechnical structures. In particular, the influence of water content and end friction during laboratory testing significantly affects the mechanical properties of rocks. In this study, uniaxial compression tests were conducted on dry and water-saturated limestone specimens under two end conditions: with and without a coupling agent, to investigate the coupled effects of water saturation and end friction on rock deformation and failure behavior. The results indicate that the uniaxial compressive strength (UCS) in the dry state is 84.2% higher than that in the water-saturated state, and the elastic modulus (E) shows an 84.7% increase in the dry state compared to the water-saturated state. The use of a coupling agent was found to enhance overall strain and reduce the extent of end friction effects. Radial shrinkage occurred in the early loading phase near the specimen ends and gradually weakened toward the center. Failure modes varied with test conditions: specimens without vaseline primarily exhibited X-shaped conjugate shear fractures due to strong end restraint, whereas vaseline-coated specimens showed cleavage failure characterized by more pronounced end damage. Water-saturated specimens exhibited more ductile behavior, while dry specimens showed brittle splitting. Fracture observations indicated that early crack formation and stress rebound influenced radial deformation and crack evolution.

The Cerchar Abrasiveness Index (CAI) is a vital parameter in geotechnical engineering, especially when it comes to tunneling and mechanized excavations. The study employed a comprehensive dataset of 163 samples representing various rock types, including igneous, sedimentary, and metamorphic formations. The methodology included three base algorithms (XGBoost, LightGBM, and Random Forest), improved by three distinct metaheuristic techniques: Arithmetic Optimization Algorithm (AOA), Reptile Search Optimization (RSO), and Harris Hawks Optimization (HHO). The Brazilian tensile strength (BTS), uniaxial compressive strength (UCS), equivalent quartz content (EQC), and brittleness index (BI) were the four main rock parameters used to make the predictive models. The model was further evaluated by splitting the data into 80% training and 20% testing sets. Subsequently, the model was compared to 17 real-world hard rock TBM projects in different countries and geological conditions. The AOA-optimized versions performed nicely, with AOA-LightGBM doing the best on the held-out test set (R² = 0.952, RMSE = 0.290, MAE = 0.208, VAF = 0.952). External validation showed that AOA-XGBoost performed properly, with the highest correlation coefficient of 0.8308 compared to field measurements from international tunneling projects. Also, the AOA-XGBoost did well on tests with R² = 0.951, RMSE = 0.296, MAE = 0.223, and VAF = 0.951. Using SHAP values to examine feature importance revealed unique parameter influence signatures. EQC was the most important parameter in XGBoost models, while UCS had the greatest impact in LightGBM and Random Forest-based models. The new method described here is an important advancement in CAI prediction methodology. It is more accurate and efficient than traditional experimental testing methods, and it works well on different types of rock. Its engineering applicability has been proven through real-world operational scenarios.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22098-9.

Carbon capture and storage is acknowledged as one of the key technologies in the energy system transition towards low greenhouse gas emissions while simultaneously meeting the demand for energy supply. Large-scale and safe CO 2 storage requires monitoring plans to address operational and regulatory requirements as well as public acceptance. We aim at quantifying the impact of various combinations of geophysical inputs to the accurate quantification of static and dynamic reservoir parameters in CO 2 storage sites. We use geophysical attributes as input data for multiphysics Bayesian rock physics inversion. We quantify the contribution of P- and S-wave velocity, density, and resistivity measurements to estimate CO 2 saturation, pore pressure, elastic rock properties, porosity, and a parameter related to the fluid distribution at the pore scale (patchiness). By considering case studies representative of Sleipner, Snøhvit and Smeaheia storage sites, we demonstrate that a well-constrained and low-uncertainty estimation of CO 2 saturation requires the use of resistivity input data. Accurate pore pressure estimation is difficult and has to take advantage of all possible input data and, if available, prior information to be able to be discriminated from saturation effects. The patchiness parameter cannot be accurately recovered regardless of data inputs considered in this study but it does not prevent a proper estimation of CO 2 saturation. Porosity estimation requires density data while rock frame mechanical moduli estimation requires S-wave velocity information. Use of prior information is always beneficial to better constrain the estimates. Quantifying the dynamic reservoir parameters with uncertainty estimation is crucial for conformance monitoring where observed and modeled behaviors of the CO 2 injection and migration in the reservoir need to be verified by the operator.

Geophysical monitoring of pore-pressure within geologic carbon storage reservoirs is critical for understanding reservoir containment and caprock integrity. Time-lapse seismic methods are useful for probing reservoir state at locations away from instrumented wells. Highly sensitive and stable time-lapse seismic methods are required for monitoring subtle changes induced by reservoir pressure. To test the limits of time-lapse seismic sensitivity to changes in pore-pressure, we used CASSM (Continuous Active-Source Seismic Monitoring), an automated cross-well time-lapse seismic technique, to monitor travel-time changes caused by pressure perturbations in a laboratory-scale analog reservoir. We refer to this analog reservoir as SMARTT (Seismic Monitoring of an Analog Reservoir Testing Tank), which is a highly instrumented tank filled with sand and bentonite clay to emulate a reservoir, caprock, and aquifer. We inject water directly into the reservoir to mimic the effects of increased pore-pressure caused by far-field CO 2 injection. We conducted a total of five injections over the course of two days with shut-in periods in between. CASSM successfully acquired data throughout the injections at high sensitivities, such that background changes in P-wave arrival times had an average standard deviation of 82 nanoseconds. CASSM was able to capture, at high correlations, changes in P-wave arrival (∆t) caused by changes in reservoir pore-pressure (∆P p ). We used granular contact theory models to show that these ?t were a result of ∆P p . In addition, using a reservoir simulator, we inverted for rock property values for both the reservoir and caprock layers by modelling the reservoir pressures for the entire injection and shut-in experiment inside SMARTT. Modeling results show that caprock permeability evolved during the experiment. This result combined with both the high sensitivity of CASSM and the high correlation of ∆t and ∆P p suggests that time-lapse seismic methods, such as CASSM, can be used for probing changes in sealing integrity.

Abstract The chemical and mechanical interactions between fluids and rocks, impairing the permeability and porosity of the reservoir, can lead to operational and economic challenges. These interactions can have a negative impact on the mechanical virtues of the rock, ultimately altering the petrophysical characteristics of the rock. The interactions cause alterations in the geometry of the pore space and the strength of the rock. Therefore, it is crucial to assess these variables before designing any oil recovery and gas storage project. The rock properties, particularly strength, permeability, and porosity, are changed during various stages such as drilling, production, and the injection of water or chemicals. In this regard, this research presents an examination review of the impact of fluid-rock interactions on the mechanical attributes of the formation rock, specifically focusing on the occurrence of formation damage. Generally, it is crucial to possess a strong comprehension of the interactions between fluids and rocks, as well as their effects on mechanical attributes and formation damage. This manuscript compiles recent studies to study the effect of interactions on rock petrophysical properties as well as mechanical properties, and in this regard, provides new perspectives on fluid-rock interactions in reservoirs. This understanding is vital in order to minimize both economic losses and technical complexities. Also, this manuscript can help researchers gain a complete perspective on the effect of fluids on rock mechanical properties in storage operations.

Abstract The Khorat UNESCO Global Geopark in northeastern Thailand features a distinctive double cuesta landscape, the “Khorat Cuesta,” developed in Mesozoic sandstones of the Phra Wihan, Sao Khua, and Phu Phan Formations. Although the geology of the Khorat Plateau has been extensively studied, the role of rock properties in controlling geomorphological development remains poorly understood. This study investigates the relationships between lithological, physical, chemical, and mechanical properties of sandstones and their influence on cuesta evolution. Field investigations were conducted at eight localities within the Geopark, complemented by laboratory analyses of petrography, X-ray diffraction, density, porosity, uniaxial compressive strength, and slake durability. Geomorphological analysis incorporated geographic information system and remote sensing using 30 m resolution Shuttle Radar Topography Mission data. Results reveal significant lithological variability among the three formations. Phra Wihan and Phu Phan sandstones, though quartz-rich and forming prominent escarpments, display relatively low strength (R2–R4) and degrade rapidly under slake durability testing. In contrast, Sao Khua sandstones, characterized by fine grain size and calcite cementation, exhibit higher strength (R4–R5) and greater resistance to mechanical breakdown, though they remain vulnerable to chemical dissolution. These differences account for contrasting geomorphic expressions: mechanical weathering dominates in the Phra Wihan and Phu Phan Formations, whereas chemical weathering, particularly acid-induced dissolution, plays a key role in the Sao Khua Formation. Contrary to models emphasizing tectonic uplift or mass wasting in other cuesta systems, the Khorat Cuesta is primarily shaped by differential weathering of sedimentary strata with contrasting properties. These findings underscore the importance of lithological control in cuesta evolution and provide new perspectives for geopark evaluation, geoconservation, and geotourism interpretation. By linking rock properties to geomorphic development, this study contributes to understanding landscape evolution in northeastern Thailand and enhances the scientific value of the Khorat Geopark.

Coal-rock dynamic disasters, especially rock bursts, pose serious threats to mining safety and production efficiency in deep mining operations. To improve the accuracy and intelligence of coal-rock burst risk assessment, this paper proposes a BP neural network model optimized by Particle Swarm Optimization (PSO). The model integrates coal seam mechanical parameters, mining conditions, and surrounding rock properties as input indicators to construct a comprehensive evaluation system. PSO is applied to optimize the initial weights and thresholds of the BP neural network to avoid local minima and improve convergence speed and prediction accuracy. The optimized model is trained using field monitoring and testing data. Comparative experiments demonstrate that the PSO-BP model exhibits higher prediction accuracy and better generalization ability compared to the traditional BP network. The results indicate that this method can effectively evaluate the risk of coal-rock burst and provides technical support for early warning and disaster prevention in coal mines.

Acid Mine Drainage (AMD) attributed to mine operation is considered one of serious environmental problems in the world. Cover system is utilized as an environmentally-friendly and an effective way for prevention of AMD in many cases in open cast coal mines in Indonesia. Waste rocks are classified into Potentially Acid Forming (PAF) or Non Acid Forming (NAF) on the basis of geochemical properties of rocks. PAF which is a cause of AMD is covered with NAF which is considered rocks unrelated to AMD at waste dump in order to prevent the contact of PAF with water and oxygen. The placement of backfill of waste rocks in waste dump is determined only by the classification at the aim of the prevention of AMD. According to past studies, pH affects the progress of AMD through the change of dissolution behavior of metals. In regard to the occurrence of AMD on the inside of waste dump, waste rocks in the lower part of waste dump are possibly affected by leachate from the upper part. Thus, the placement of waste rocks in waste dump need to be discussed in terms of not only the types of PAF or NAF but also the effects of water quality on the occurrence of AMD. This study presents the effects of pH on the progress of AMD with the results of leaching test under various pH conditions using rock samples taken in coal mine: pH was set at 3.0, 6.0, and 8.0. The results indicate that pH is not important factor as compared to the supply of oxygen to discuss the progress of AMD for a long term. In other words, the placement of waste rocks in cover system has to be determined by considering not so much the effect of water quality as the supply of oxygen.

Young’s Modulus is an important mechanical property of rocks in hydraulic fracturing design, as it determines their stiffness and indicates the extent to which they will deform elastically under uniaxial compressive stress. Young’s Modulus for reservoir ro ck can be determined via laboratory testing or using well log geophysical data. However, the laboratory testing depends on the availability of core samples, while data analysis based on well log data requires appropriate conversion formulas, which demand significant time, cost, and complexity. The approach presented in this study is based on the fact that real time drilling data, such as weight on bit (WOB), torque on bit (TQR), standpipe pressure (SPP), rotary speed (RPM), rate of penetration (ROP), and drilling fluid flow rate (FLOWIN), are re adily available in the early stages of the drilling process without incurring additional costs. Two machine learning algorithms were used to correlate drilling data with the dynamic Young’s Modulus : Random Forest and Decision Tree. Two different datasets were used in this study, the first dataset was utilized to develop and train the model, while the other dataset was used to validate the developed models. The first of the two methods employed demon strated a remarkable match between the given values and the predicted values. The correlation coefficients ranged from 0.71 to 0.95, with the average absolute percentage error being less than 5%. Based on the obtained results, the use of drilling data combined with artificial intelligence models for predicting Young’s Modulus proves to be a viable approach.

The electrical properties of rocks are widely used in characterizing reservoir rocks due to their ability to identify porosity, fluid types, and saturation levels. This study aims to determine the effect of microstructure on the electrical properties of Ngrayong Formation rocks through laboratory measurements and numerical calculations. Twelve samples from three-grain size categories with porosity ranges of 34-48% were prepared for resistivity measurements under partially and fully brine-saturated conditions using a 6% NaCl solution. Scanning results of the three categories revealed that grain size influences the microstructure of rocks, including the distribution of grain size and pore size. The estimated electrical properties show that at low saturation, microstructure significantly affects resistivity response. Conversely, at high saturation, variations in microstructure tend to result in uniform resistivity, indicating minor microstructural influence on high-saturation electrical property estimations. Additionally, Archie parameters were determined with ranges of 2.1-3.4 for the cementation exponent and 1.2-2.4 for the saturation exponent. A strong correlation was also observed between laboratory measurements and numerical calculations, especially for samples with small grain sizes. This study provides a deeper understanding of the electrical properties of rocks as a function of their microstructure, which can serve as a base for interpreting electrical data from Routine/Special Core Analysis, resistivity log data, or field resistivity data in Applied Geophysics.

The increase in demand for building materials and stones in recent years has led to the expansion of random quarrying. Therefore, the current study highlights the environmental impact of gypsum rock quarries in the Almulawatha area in Nineveh Governorate, northern Iraq. The aims of the study are to evaluate the gypsum rocks of the Fatha Formation within Nineveh Governorate, specifically in Almulawatha area, where the rocks were evaluated environmentally and engineeringly to demonstrate their suitability for various uses. As well as studying the impact of rock quarrying and the environmental and geological problems it causes. This study focused on the environmental and geological impact resulting from these quarries. Field surveys and field studies were conducted to determine the field physical and geotechnical properties of the region's rocks, in addition to sampling them for the purpose of laboratory study to determine their engineering properties. Field observations and satellite images monitoring have shown the massive expansion of random quarrying operations in the environment of the study area in terms of the spread of large holes and high rock cuts, which will ultimately lead to the formation of swamps and the occurrence of rock slides. It was also noted that all types of extracted rocks are used in the plaster industry, and this has a major negative impact, as the use of the erosion-resistant type in the plaster industry is considered an economic waste. Moreover, the erosion-resistant type is also characterized by the presence of textile veins in it, which gives it an aesthetic appeal when used for packaging purposes. The study area is a ​​low hills, and as a result of the spread of quarrying operations, it has an unclear topography due to the absence of well-studied scientific plans, and the quarrying work being limited to the easiest and cheapest quarrying methods, which has led to a significant distortion of the region’s environment.

This study investigated the carbonation potential of peridotite from the Kolaka Ultramafic Complex in Sulawesi, Indonesia, for carbon capture and storage (CCS) applications. Peridotites, particularly those enriched in magnesium, are known for their high reactivity with CO?, forming stable mineral carbonates. However, the Kolaka region’s peridotites have not been thoroughly assessed for their carbonation prospects. This research addresses this gap by examining the petrology, geochemistry, and physical-magnetic properties of peridotite, focusing on its serpentinization and carbonation characteristics. An integrated approach applying petrographic analysis, X-ray fluorescence (XRF), X-ray diffraction (XRD), scanning electron microscopy (SEM), Schmidt hammer, and magnetic susceptibility tests, was used to determine the mineral composition, specifically the carbonation minerals, and the changes in the physical properties of the rocks during carbonation. The results showed that the peridotites, particularly serpentinized lherzolites, exhibit high carbonation potential characterized by the abundance of magnesium-rich olivine-pyroxene minerals. Carbonation reactions are characterized by the presence of magnesite and brucite, leading to significant changes in rock strength and magnetic susceptibility. Carbonation occurs by an advanced serpentinization process, which increased mineral reactivity and leads to reducing uniaxial compressive strength (UCS). Additionally, magnetic susceptibility exhibits positive correlation with serpentinization, accompanied by magnetite formation. These findings suggest that Kolaka's serpentinized peridotite, as mining waste, is a viable candidate for CO? storage. The ex-situ carbonation mechanism allows Kolaka's fine-grained peridotite to capture CO?, while also improving nickel ore recovery, minimizing dust, neutralizing acid mine drainage, and enhancing soil quality.

Abstract Electrical conductivity (EC) is a key physical property of minerals and rocks that constrains the composition and structure of Earth's deep interior. Theoretical studies predict that the CaCl2‐type hydrous Al‐bearing SiO2 phase, present in subducted crustal materials, becomes superionic—where protons are no longer bonded to specific oxygen atoms but instead become mobile within the SiO2 lattice—under high‐pressure and high‐temperature conditions of the lower mantle. The enhancement of the EC upon such superionic transition has not been experimentally verified yet. Here, we measured the EC of Al‐bearing SiO2 containing 1,750 ppm H2O at pressures up to 82 GPa and temperatures up to 2610 K by employing a recently developed technique designed for measuring transparent materials. Results demonstrate a sudden increase in EC to approximately 10 S/m at temperatures of 1,100–2,200 K, depending on pressure. This is several to 10 times higher than the conductivity of the surrounding shallow to mid‐lower mantle and is consistent with a transition to the superionic state. If hydrous SiO2 is substantially weaker than other coexisting phases and thus forms an interconnected film in subducted mid‐oceanic ridge basalt (MORB) crust, the EC of the bulk MORB materials is significantly enhanced by superionic SiO2 to ∼1,800 km depth, which may explain the high EC anomalies observed at subduction zones underneath northeastern China. The observed EC anomalies can be matched by the EC of subducted MORB materials containing Al‐bearing SiO2 with a water content of approximately 0.2 wt%, providing insights into deep H2O circulation and mantle distribution.

Influence of water content on the failure modes and macro-micromechanical properties of sulfate rocks: Insights from experimental and DEM simulations

The pore-network-continuum modeling of two-phase flow properties for multiscale digital rocks

Abstract Numerical calculations of elastic properties for several clastic rocks were performed on 3-D micro-CT digital images. The finite element method developed by Garboczi, based on minimum energy, is applied to calculate the stress and strain of the medium. Different resolutions of CT-scan and sub-samples are compared to understand the influence of resolution and sample size on the elastic properties, i.e., elastic moduli and Poisson’s ratio. By assigning the solid phase as quartz, the results show that the estimated Poisson’s ratio, bulk modulus, shear modulus, and Young’s modulus depend strongly on the porosity and less on the microstructure. The relationship is linear for Poisson’s ratio. Furthermore, the bulk modulus, shear modulus, and Young’s modulus exhibit an exponential relation with their porosity and slightly show the influence of the microstructure.