Petrophysical Properties Of Rocks Research Articles

Effects of saturated fluids on petrophysical properties of hot dry rock at high temperatures: An experimental study

Hydraulic fracturing is a crucial technology for efficient heat extraction from hot dry rock (HDR). The fracturing process involves the injection of large volumes of cold fluids, which can significantly affect the petrophysical properties of the rock, particularly when temperature differences and fluid-rock interactions are taken into account. However, there is a lack of research on the evolution of HDR petrophysical properties under the influence of different fluids at high temperatures. In this paper, we utilized a high-temperature high-pressure tri-axial loading system, rock spontaneous imbibition instrument, porosity-permeability tester, and thermal conductivity meter, along with comprehensive analysis techniques such as ultrasonic wave velocity monitoring, CT scanning, and nuclear magnetic resonance (NMR), to systematically investigate the effects of distilled water and nano emulsion on the physical and mechanical properties of the HDR at high temperatures. Through comparison with the experimental results of dry samples, comprehensive characteristics of physical parameters for the HDR under the influence of distilled water and nano emulsion are obtained, such as spontaneous imbibition mass and permeability, thermal physical properties, and rock mechanical parameters. The experimental results show that temperature increase promotes the formation of massive thermal damage fractures in the HDR samples, and the fluid weakens the mechanical strength of the HDR. When the effects of temperature and saturated fluids are combined, the hydraulic fracture network in the HDR samples exhibits increased complexity, contrasting with the fracture patterns observed in the heat-treated dry samples. The distilled water has a more pronounced effect on weakening the mechanical strength of the samples than the nano emulsion. Moreover, the analyses of permeability, thermal conductivity, ultrasonic wave velocity, and NMR results are consistent with the results of mechanical experiments. As the temperature rises, the wave velocity of saturated distilled water samples exceeds that of both saturated nano emulsion samples and dry samples, the velocity of the saturated fluid front in the samples increases, and the range of the fluid in the pores expands. Overall, these findings provide valuable experimental evidence and theoretical guidance for better understanding the hydro-thermal-mechanical behaviors of the HDR, which are crucial for the efficient development and utilization of geothermal energy.

GeosPIn: A MATLAB library for Geostatistical Petrophysical Inversion

Petrophysical inversion of seismic data is one of the key components of seismic reservoir characterization. The goal of petrophysical inversion is to estimate petrophysical properties of reservoir rocks, such as porosity, volumes of minerals or lithologies, and water and hydrocarbon saturations from seismic data. This process can be performed by combining seismic AVO modeling and rock physics relations with deterministic or probabilistic inverse theory methods, either in a two-step approach based on seismic AVO inversion and rock physics inverse mapping or in a single loop step that includes both. We present a comprehensive MATLAB library named Geostatistical Petrophysical Inversion (GeosPIn). The novelty of the library lies in a single-loop geostatistical inversion method based on the Probability Field Simulation (PFS) and the Ensemble Smoother Multiple Data Assimilation (ES-MDA), and its implementation for applications to 3D datasets. The library also includes a Bayesian petrophysical inversion based on the two-step approach for comparison. We illustrate the main algorithms, explain the structure of the source codes, and demonstrate the library applications through 1D and 3D examples.

Bayesian joint inversion of seismic and electromagnetic data for reservoir lithofluid facies, including geophysical and petrophysical rock properties

A Bayesian approach is developed to estimate lithofluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock-physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Because computing this probability for all possible facies configurations may be impractical, a Markov chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geologic scenario and a well from a selected marine oil field. Two other wells from the same reservoir are used to gather prior distributions. Data from the well, calibration of the rock-physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward-modeling adaptations based on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical data sets are then inverted for lithofluid facies models under different prior inputs. Two challenging geoelectric scenarios also are tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating these geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also indicate potential for quantitative reservoir interpretations.

One-dimensional modelling of air injection into abandoned oil fields for heat generation

With the global drive for net-zero emissions, it has never been more important to find clean energy sources. There are thousands of abandoned oilfields worldwide with the potential to be reactivated to produce clean energy with air injection and subsequent waste fluid sequestration. Air injection, and the development of a fire-front, may be used with enhanced geothermal systems by taking advantage of the inherent increase in heat and pressure. Conventionally used as an enhanced oil recovery technique, air injection has gained the reputation of being a high-risk intervention due to the many failures in its history. Knowledge of how petrophysical rock properties and oil physical and chemical properties control the consequences of air injection is key to optimzing the selection of late-life, or even abandoned oilfields for use in such systems. Here we use one-dimensional modelling to test the effect of varying porosity, permeability, oil viscosity and API gravity on the success of air injection. Modelling shows that the most important factor controlling temperature is the porosity of the reservoir, followed by the API gravity and then the viscosity of the oil. The most important factors controlling velocity of the fire-front are API gravity followed by oil viscosity. We show that reservoirs with high porosity and low permeability with high viscosity and low API gravity oil reach the highest fire-front temperatures. The significance of this work is that it provides several geoscience-related criteria to rank possible candidate reservoirs for reactivation and clean energy generation via air injection: the best candidates will have the highest total porosity, relatively low permeability, highest oil viscosity and lowest API gravity, such fields can then move on to bespoke and more complex simulations.

Automated litho-fluid and facies classification in well-logs: the Rock Physics perspective

While accurate litho-fluid and facies interpretation from wireline log data is critical for applications like joint facies and impedance inversion of seismic data, extracting this information manually is challenging due to the complexity and high dimensionality of the logs. Traditional clustering methods also struggle with litho-fluid type inference due to different depth trends in petrophysical rock properties due to compaction and diagenesis. We introduce a Rock Physics Machine Learning workflow that automates litho-fluid classification and property depth trend modeling to address these challenges. This workflow employs a maximum-likelihood approach, explicitly accounting for depth-related effects via Rock Physics models, to infer litho-fluid types from borehole data. It utilizes a robust Expectation-Maximization algorithm to associate each litho-fluid type with a specific Rock Physics model instance, constrained within physically reasonable bounds. The workflow directly outputs litho-fluid type proportions and type-specific Rock Physics models with associated uncertainties, providing essential prior information for seismic inversion.

Comparative analysis of the application of the Lichtenecker and Lichtenecker – Asaad models for the theoretical assessment of the rocks effective thermal conductivity

The analysis of the conditions and limitations of using the Lichtenecker and Lichtenecker – Asaad models for predictive assessment of the effective thermal conductivity of rocks have been carried out. For a comparative assessment, experimental data on the thermal conductivity of carbonate rocks (limestone, dolomite) from the South-West German Molasse basin, the Chicxulub impact structure (Yucatan Peninsula, Mexico) and the Bazhenov Formation (Western Siberia, Russia) have been considered. Comparative assessments were carried out based on the use of correlation relationships obtained by the authors of experimental works as a result of regression analysis of the petrophysical properties of carbonate rocks. The results of the computational and analytical assessment have shown that the highest accuracy of thermal conductivity prediction using the Lichtenecker – Asaad model can be achieved if experimental data are available that provide a highly accurate determination of the structure factor. The features of the influence of various parameters of the Lichtenecker – Asaad model on the error in estimating the effective thermal conductivity of the rock have been studied. It has been established that at the stage of pre-design research, the Lichtenecker model can be used for a fairly correct theoretical assessment of the thermal conductivity of water-saturated, slightly porous rocks.

3D basin modeling of the Hils Syncline, Germany: reconstruction of burial and thermal history and implications for petrophysical properties of potential Mesozoic shale host rocks for nuclear waste storage

AbstractJurassic sedimentary sequences suitable for nuclear waste storage in northern Germany consist of organic-lean claystone and were uplifted to < 100 m depth in the Hils Syncline area (southern Lower Saxony Basin). This Hils Syncline, showcasing a northwestward increase in thermal maturity, facilitates the study of shale petrophysical properties influenced by burial history. This study introduces a 3D-thermally calibrated numerical model of the Hils Syncline area to analyze its geodynamic evolution and maturity variations. It provides new vitrinite reflectance and sonic velocity data for modeling calibration and erosion estimation. The Hils Syncline area has undergone continuous subsidence, interrupted by a Cretaceous uplift documented by an erosional unconformity. During the latest Early Cretaceous, Jurassic rocks underwent maximum burial reaching up to several thousand meters depth and temperatures up to 160 °C in the northwest. The Late Cretaceous inversion caused stronger erosion towards the northwest removing up to 3300 m of sediment compared to about 1300 m in the south, according to vitrinite reflectance-based estimations. Numerical modeling results along the study area indicate decreasing porosity and permeability northwestward with increasing thermal maturity. Porosity and vertical permeability decreased to 5–14% and 2.8 × 10–23 to 1.5 × 10–19 m2 [1 mD = 10−15 m2], respectively, while vertical thermal conductivity increased to 1.30–2.12 (W/m/K). These trends of porosity/permeability and thermal conductivity with burial align with sonic velocity and published experimental porosity data, except for the thermally most mature region (Haddessen). This anomaly is tentatively attributed here to localized overpressure generation in the Posidonia Shale during maximum burial, affecting both the underlying Pliensbachian and overlying Doggerian units. Graphical abstract 3D numerical model of the Hils Syncline and surrounding area revealing that a northwestward increase in maximum burial resulted in higher temperatures and varying maturity levels. While most locations align well with calibration data (i.e. measured vitrinite reflectance and porosity), discrepancies arise in the Haddessen/Bensen area. The mismatch between porosity, vitrinite reflectance, and sonic velocity response indicates local overpressure in the northernmost region mainly during the Cretaceous. It was likely caused by gas generation in the Posidonia Shale affecting nearby Lower and Middle Jurassic units.

Probabilistic physics-informed neural network for seismic petrophysical inversion

The main challenge in the inversion of seismic data to predict the petrophysical properties of hydrocarbon-saturated rocks is that the physical relations that link the data to the model properties often are nonlinear and the solution of the inverse problem is generally not unique. As a possible alternative to traditional stochastic optimization methods, we develop a method to adopt machine-learning algorithms by estimating relations between data and unknown variables from a training data set with limited computational cost. We develop a probabilistic approach for seismic petrophysical inversion based on physics-informed neural network (PINN) with a reparameterization network. The novelty of our approach includes the definition of a PINN algorithm in a probabilistic setting, the use of an additional neural network (NN) for rock-physics model hyperparameter estimation, and the implementation of approximate Bayesian computation to quantify the model uncertainty. The reparameterization network allows us to include unknown model parameters, such as rock-physics model hyperparameters. Our method predicts the most likely model of petrophysical variables based on the input seismic data set and the training data set and provides a quantification of the uncertainty of the model. The method is scalable and can be adapted to various geophysical inverse problems. We test the inversion on a North Sea data set with poststack and prestack data to obtain the prediction of petrophysical properties. Compared with regular NNs, the predictions of our method indicate higher accuracy in the predicted results and allow us to quantify the posterior uncertainty.

АНАЛІЗ МЕТОДІВ ВИЗНАЧЕННЯ ІНДЕКСУ КРИХКОСТІ ТА ЇХ ЗАСТОСУВАННЯ ДЛЯ ТЕРИГЕННИХ ПОРІД-КОЛЕКТОРІВ ДНІПРОВСЬКО-ДОНЕЦЬКОЇ ЗАПАДИНИ

The article discusses the concept of the rock brittleness index, which is widely used for the design of hydraulic fracturing. Three main approaches to its calculation (mineralogical, using elastic properties, using log data) are characterized. For each of the approaches, the characteristics of the most used calculation methods, which were created for rocks with different mineralogical composition, using different physical properties or based on different empirical dependencies, are given. For two sets of core material samples of terrigenous rocks of the Dnipro-Donetsk Basin, the mineralogical index of fragility according to the main petrogenic components was determined by two different methods and the brittleness index according to elastic properties – Young's modulus and Poisson's ratio, the angle of internal friction. A comparative characteristic of the results obtained by different methods was carried out, which showed the similarity of trends in lithology and petrophysical properties of rocks. The authors recommend the Jarvie mineralogical method, which is characterized by high accuracy and ease of use, for use in evaluating terrigenous reservoirs of the Dnipro-Donetsk Basin.

3D-FGAN: A 3D stochastic reconstruction method of digital cores

Digital core reconstruction can quantitatively characterize the interior microstructure of porous media and revealing variations in the petrophysical properties of rocks, which can be classified into physical experimental methods and numerical reconstruction methods. Numerical reconstruction methods utilize the information contained in 2D or 3D images to reconstruct 3D digital cores, which is relatively inexpensive and more efficient, but traditional numerical methods usually cannot reuse extracted statistical information from previous reconstruction process and may generate unsatisfying results. Nowadays, the development of deep learning has opened a door to the high-quality reconstruction of digital cores. Generative adversarial networks (GANs), viewed as an important branch of deep learning, have been used for the 3D digital core reconstruction. GANs have a strong feature extraction capability and can generate fake images that closely resemble the real ones. Nevertheless, due to the complex structure of 3D core images, especially those with high anisotropy, GANs and their variants still have difficulties in generating satisfying pore structure. To address the above issues, we propose a variant of GANs called 3D fast generative adversarial network (3D-FGAN) to rapidly generate high-quality 3D digital core images. Several channel attention modules called skip-layer excitation blocks are added to the generator of 3D-FGAN, enhancing the utilization of important features. Auto-encoders are employed to optimize the discriminator of 3D-FGAN, which makes the discriminator more comprehensively extract the features of input images and thus better guides the generator. Through the evaluation metrics of pore space distribution (e.g., isolated pore space and connected pore space, pore diameters and throat diameters), multiple-point connectivity, absolute permeability and diversity test, the quality of 3D-FGAN can be proved. Meanwhile, in the reconstruction of anisotropic shales with evident fractures, 3D-FGAN can quickly produce higher-quality images compared with some other numerical reconstruction methods.

The impact of CO2 saturated brine salinity on wormhole generation and rock geomechanical and petrophysical properties

Deep saline aquifers are good candidates for CO2 sequestration because of their huge reserves, wide distribution, cost-effectiveness, and relatively high security. The impact of CO2 sequestration on the rock's mechanical and petrophysical properties is a complex issue that depends on a number of factors, such as salinity. This study provides insights into the potential changes in rock petrophysical and geomechanical properties due to the reactivity of CO2-saturated brine with limestone. Specifically, this article provides insights into the impact of salinity on wormhole generation and the corresponding changes in rock properties. Three Indiana limestone samples with a 1.5-inch diameter and 3-inch length, 15% average porosity, and 2 mD average permeability were used. The samples were flooded at 1 mL/min, 2000 psi, and 60 °C with different brine concentrations mixed with CO2 at a 70:30 ratio. To examine the impact of salinity, CO2 was mixed with brine concentrations of 40,000 ppm, 120,000 ppm, and 200,000 ppm for the coreflooding experiments. The samples' Young's modulus (YM) and Poisson's ratio at different confining pressures were measured before and after coreflooding. A medical CT scan was also used to visualize the generated wormholes and quantify their volumes. Results showed that it took only 14.3 pore volumes (PVs) to create a wormhole at 40,000 ppm, while 24.8 and 25.6 PVs at 120,000 ppm and 200,000 ppm, respectively. Lower salinity live brine generated a thicker wormhole compared to the other salinities. The reduction in rock strength in terms of YMs was the highest at 200,000 ppm, followed by the 120,000 ppm and then the 40,000 ppm cases. This study demonstrates that wormhole generation can substantially increase CO2 storage capacity in low-salinity aquifers; however, an increase in CO2 relative permeability indicates that it will accumulate primarily beneath the caprock. In addition, this study reveals that fresh carbonated water or water with a low salinity can be used for acid stimulation.

An Overview of How the Petrophysical Properties of Rock Influenced After Being Exposed to Cryogenic Fluid

Exposure to cryogenic liquids can significantly impact the petrophysical properties of rock, affecting its density, porosity, permeability, and elastic properties. These effects can have important implications for various applications, including oil and gas production and carbon sequestration. Cryogenic liquid fracturing is a promising alternative to traditional hydraulic fracturing for exploiting unconventional oil and gas resources and geothermal energy. This technology offers several advantages over traditional hydraulic fracturing, including reduced water consumption, reduced formation damage, and a reduced risk of flow-back fluid contamination. In this study, an updated review of recent studies demonstrates how the thermal shock caused by the cryogenic liquid during the fracturing process substantially affects the rock's physical properties. Additionally, changes in permeability, porosity, and pore structure brought about by cryogenic treatments are highlighted. This work aims to draw attention to the studies that deal with the effect of thermal shock on rock petrophysical properties and establish the ideal conditions for employing cryogenic liquids in these contexts. Simulation studies, laboratory trials, and field application cases have been undertaken to assess the efficacy of cryogenic liquid fracturing technology. These investigations have provided important insights into the physical and mechanical impacts of thermal shock on rock and the performance of cryogenic liquid fracturing in real-world situations.

Thermal influence of sills on the chemostratigraphy and mineralogy of carbonate rocks from the Irati formation, Paraná basin

Carbonate rocks and shales are prominent lithologies in atypical petroleum systems. In these settings, the thermal effects of sills play a crucial role leading to mineralogical and geochemical alteration in the host rocks and in the maturation process of the associated organic matter. While extensive research has been conducted on shales within atypical systems, the understanding of the petrophysical and geochemical properties of carbonate rocks remains uncertain in numerous basins. In this study, we selected the Irati Formation (Paraná Basin) to investigate the thermal effects caused by diabase sills on the chemostratigraphic and lithologic features of carbonate rocks. We conducted mineralogical analyses, examined oxygen and carbon stable isotopes, and analyzed inorganic geochemical data from two distinct sections of the formation: one with diabase sills intruding the sequence and the other without identified intrusions in the area. Widespread recrystallization of minerals is observed throughout the studied rock wall, resulting in the formation of marbles and calc-silicate rocks, where dolomite is mainly replaced by calcite. The carbonate bank, located 5–7 m below the sill, is marked by brucite and chrysotile, while the rocks up to 1 m below and above the intrusion show the presence of tremolite, diopside, and antigorite. Substantial variations in elemental data are observed, including a significant increase in SiO2 and K2O below the sill, as well as elevated MgO levels within the carbonate bank. Additionally, stable isotopic signatures of oxygen and carbon display a negative shift influenced by decarbonation and intense fluid flow controlling the reactions. The contact aureole resulting from the intrusion is identified at least 7 m above and below the sill based on mineralogical and inorganic geochemical data.

Impact of H2-CH4 mixture on pore structure of sandstone and limestone formations relevant to subsurface hydrogen storage

Subsurface Hydrogen (H2) storage is a promising technology to achieve net-zero carbon emissions. Depleted gas reservoirs can provide safe storage of hydrogen in the long term, and effective recovery during the cycling process. The pre-existing Methane gas (CH4) or its traces in these formations can mix with the injected hydrogen and might cause in-situ geochemical reactions with the storage rock. Despite the recent interest in evaluating H2-rock interactions, the available knowledge on the impact of H2–CH4 mixture on the petrophysical properties of storage rocks is scarce. Motivated by the lack of experimental work that exists on this topic, we provide an experimental investigation on the reactivity of sandstone and limestone formations to H2–CH4 mixture. Samples from Bandera Gray Sandstone and Indiana Limestone samples were aged in H2–CH4 mixture at 75 °C and 1400 psi for 90 days, and Nuclear Magnetic Resonance (NMR) and gas porosity analysis were performed. The results showed limited reactivity of the samples to H2–CH4 mixture, and subsequently, insignificant changes occurred in the petrophysical properties. The average porosity of BG and IL samples decreased by 3.32 % and 1.00 % respectively. NMR analysis indicates no significant changes in T2 distribution times curves, indicating no major alterations occurred in the pore structure or rock geometry. Moreover, the average NMR porosity of BG was reduced by 3 %, while the reduction of IL porosity did not exceed 6 %. We conclude that the potential geochemical reactions (dissolution/precipitation) between the H2–CH4 mixture and rock minerals are very weak within 90 days of H2–CH4 mixture treatment, thus the suitability of sandstone and limestone rocks for hydrogen storage is promising.

Mineral Scaling Impact on Petrophysical Properties of Reservoir Rock in a Geothermal Field Located in Northwestern Iran

Summary As the usage of geothermal energy as a zero-emission power resource continues to grow in significance, comprehending the interplay between physical and chemical processes within geothermal reservoirs becomes crucial. In this study, a computationally efficient fluid flow and heat transfer model, combined with a fluid chemistry model, is used to simulate fluid circulation and mineral precipitation in reservoir rock, resulting in changes in rock porosity and permeability. A 2D hybrid approach is employed to solve transient mass and momentum conservation equations, coupled with an analytical solution of the energy equation proposed in the literature for geological formations. A marching algorithm is utilized to calculate velocity and temperature fields in the axial direction within the production zone. Mineral scaling is addressed using the outputs of the hybrid model to perform saturation index (SI) and solution/dissolution computations for qualitative and quantitative mineral precipitation modeling. Multiple criteria are considered to assess the likelihood and intensity of fouling issues. The analysis results are used in an empirical model to estimate rock secondary porosity and permeability changes over a 5-year period of heat extraction. The developed simulator is applied to model a site in the Sabalan geothermal field in Iran, and its initial verification is conducted using data from the same site in the literature. The findings in the study for a sensitivity on fluid circulation rate reveal that increasing water circulation flow rate increases precipitation rate and pumping power required. Furthermore, even minor instances of pore blockage can result in notable reductions in permeability. Consequently, ensuring precise control over pressure and temperature during the production phase becomes progressively crucial for both reservoir integrity and production assurance. The proposed framework provides a promising approach for accurate and efficient simulation of geothermal reservoirs to optimize power generation and minimize environmental impact.

Prediction of formation pressure based on numerical simulation of in-situ stress field: a case study of the Longmaxi formation shale in the Nanchuan area, eastern Chongqing, China

The in-situ stress and formation pressure are important parameters in shale gas development. They directly affect the well wall stability, the direction of horizontal well drilling, and the fracturing effect during the shale gas development process. There are abundant shale gas resources in the southeastern Chongqing-Sichuan area, but the structure in the area is complex, and it is difficult to predict the in-situ stress and formation pressure. Therefore, in this paper, a finite element simulation model was established based on a large number of seismic, logging, and experimental rock mechanics data and the prediction accuracy of the stress field simulation was effectively improved. The construction of the stress field was based on the combined spring model, as well as the data related to the measured in-situ stress and the formation pressure obtained during drilling. The coupling relationship between the in-situ stress, the formation strain, and the formation pressure were derived to carry out the prediction of the distributions of the formation pressure and the formation pressure coefficient. The prediction results showed that the present-day maximum principal stress direction in the study area was about NE65°–110°, and the present-day maximum principal stress was 56.12–93.79 MPa. The present-day minimum principal stress direction was about NE335°–20°, and the present-day minimum principal stress was 48.06–71.67 MPa. The formation pressure was 2.8–88.25 MPa, and the formation pressure coefficient was 0.74–1.55. The formation pressure distribution was greatly affected by fault, tectonic location, in-situ stress and rock petrophysical properties, and the overpressure areas of the formation were distributed in the synclines and the deeply buried areas. This study shows that the finite element based formation pressure prediction method is effective.

The impact of CO2 saturated brine temperature on wormhole generation and rock geomechanical and petrophysical properties

One solution to attain net zero CO2 emissions is the sequestration of CO2 in subterranean formations. Deep limestone saline aquifers provide excellent storage sites for CO2 due to their high solubility in water and vast aquifer volumes. This study investigates the potential changes in rock petrophysical and geomechanical properties caused by the reaction of carbonated brine (CO2+brine) and limestone rock, as well as the effect of temperature on wormhole generation. Three samples of Indiana limestone (100% calcite) with dimensions of 1.5 inches in diameter and 3 inches in length, 15% average porosity, and 2 mD average permeability were used. The samples were flooded at 1 mL/min and 2000 psi with a carbonated brine consisting of total dissolved salts of 120,000 ppm. To evaluate the effect of temperature on limestone-carbonated brine reaction at CO2 underground storage conditions, the experiments were conducted at three different temperatures (35, 60, and 85 °C). The samples' Young's modulus and Poisson's ratio at different confining pressures were measured before and after carbonated brine coreflooding. Additionally, we used a medical Computed Tomography (CT) scanner to visualize the created wormholes and quantify their volumes. We observed the generation of a wormhole in each of the experiments after 19.7, 26.7, and 46.9 pore volumes (PVs) of CO2-saturated brine were injected at 35 °C, 60 °C, and 85 °C, respectively. Moreover, the lowest temperature treatment created a straight wormhole with less void volume compared to the other temperatures. Wormhole generation at 85 °C showed the largest reduction in rock strength in terms of Young Moduli (YMs) than the other experiments despite showing the lowest reactivity. To the best of our knowledge, this is the first study to reveal the impact of temperature on wormhole generation due to the injection of CO2-saturated brine in limestone. This study also demonstrates that wormhole generation increases CO2 storage capacity; however, CO2 relative permeability increases, implying that CO2 will flow faster and accumulate primarily underneath the caprock. Furthermore, before injecting CO2, the well integrity in hotter carbonate reservoirs should be thoroughly examined. Conversely, this study reveals that CO2 or carbonated water could be utilized for acid stimulation.

Experimental and simulation study on the estimation of surface relaxivity of clay minerals

Nuclear Magnetic Resonance (NMR) is an important tool for the evaluation of pore structure and petrophysical properties of reservoir rocks. Pore Surface relaxivity (ρ2), which controls the relaxation decay due to the interaction between the spins and the pore surface, is an important parameter that impacts the interpretation of NMR for petrophysical properties. Sandstone rocks have complex minerology and consist of different clay minerals. Earlier efforts have reported the surface relaxivity of different rock types such as sandstone or carbonate, but less attention was given to the estimation of individual clay minerals surface relaxivity. The surface complexity and variable mineralogy (especially paramagnetic and ferromagnetic minerals) of clays can both impact the surface relaxivity and relaxation times in clays and porous media containing clays. This study, using for the first time both simulation and experimental approaches, aims to systematically investigate the surface relaxivity of clays characterized by variable mineralogy and iron content. A glass beads sample (iron free) and 5 different pure clay minerals samples (ranked by increasing iron content: Kaolinite, Smectite, Illite, Chlorite, and Nontronite) were prepared with similar average grain sizes (500 μm). The determination of surface relaxivity requires prior measurement of pore surface-to-volume ratio (S/V) using an independent method in addition to obtaining T2 relaxation time. Two methods, among the most widely used, were used here for S/V determination including BET N2 gas adsorption and 3D micro CT imaging. The 3D images were also used to conduct NMR simulations to reproduce the experimental data. The estimated surface relaxivity values from both methods were compared and correlated to the iron content of the different clays. The surface relaxivity values obtained based on BET-derived S/V showed reasonable values (29–343 μm/s) that have a strong positive relationship with iron content of the clays. The documented equation relating surface relaxivity and iron content can be potentially utilized to predict surface relaxivity based on the compositional analysis of clays. On the other hand, digital images did not capture the surface complexity of clays thus significantly underestimated S/V which consequently resulted in overestimated surface relaxivity (898–11000 μm/s) that were physically non-feasible. Supporting the same argument, the simulated T2 relaxation distribution showed significant mismatch with the experimental data for the clay samples while an excellent match was observed for the glass beads sample. This can be attributed to the smooth surface of the glass beads, unlike the complex clays. The reported surface relaxivity values (based on BET-derived S/V) for the individual clay minerals can be used to perform NMR simulation and interpretation studies conducted on sandstone or shale formations. Careful attention should be given when performing NMR simulation on micro-CT digital images of samples containing clay due to the inadequacy of such images in capturing the complex surface and microporous nature of clays. The outcomes from this study can improve the interpretation of NMR T2 data and the determination of NMR-derived pore size distribution and petrophysical properties. The reported surface relaxivity values for clay can also inform the NMR simulation.

An end-to-end approach to predict physical properties of heterogeneous porous media: Coupling deep learning and physics-based features

Digital rock physics (DRP) has become an effective tool to predict the petrophysical properties of rocks and reveal the mass transport mechanisms in porous media. Accurate prediction of the physical properties of heterogeneous rocks based on DRP requires 3D high-resolution (HR) and large-view images. It is, however, extremely challenging to acquire such images since the current imaging technologies cannot resolve the dilemma between the high resolution and large field of view and we often end up with low-resolution (LR) images but with a large field of view or HR images with a small field of view. Moreover, available HR images are limited and always unpaired with accessible LR images, so it is of great difficulty to train a deep learning model based on the limited unpaired images. To address these issues, we used a fast and stabilized generative adversarial network (FastGAN) to synthesize thousands of plausible LR and HR images based on ∼100 unpaired images. Taking the synthetic images as training images, we then utilized a cycle-consistent GAN (CycleGAN) to reconstruct the 3D HR large-scale digital rocks by assimilating the fine-scale structures from 2D HR images into 3D LR images. The accuracy of the proposed method (FastGAN-CycleGAN) is validated by comparing the porosity, pore size distribution, multiple-point correlation, and permeability of the reconstructed digital rocks of shale and carbonate samples with laboratory measurements. The proposed unsupervised approach does not require prior image processing knowledge. Furthermore, it can be also applied to other types of images such as magnetic resonance and fluorescence microscopy images in the future.

Influences of bulk structure of Calcarenitic rocks on water storage and transfer in order to assess durability and climate change impact.

The main purpose of this study is to understand influences of bulk structure of geomaterials on water storage and transfer, in order to assess durability and climate change impact on the UNESCO world heritage sites in Alexandria, Egypt. This study deals with the responses of water towards the physiochemical and physicomechanical behaviours of Calcarenitic rocks, that are utilized in Greek and Roman monuments at Alexandria. Many vulnerable archaeological sites [2.3–2.5 m above mean sea level (M.S.L)] are identified at Alexandria, specifically at the Eastern Harbor, El-Shatby the Greek necropolis and Moustafa Kamil Roman tombs and addition to the Roman Catacombs of Kom El-Shoqafa which excavated with deepth − 18 m from the land surface. These UNESCO heritage sites suffer climate change impact (heavy rains and sea water rising) as well as multiple geoenvironmental and geophysical hazards. In this study a general outline of the various tests, surveys and analyses is presented, highlighting the most important issues related to the durability and climate change impact. This paper represents the comprehensive in-situ, field and laboratory surveys and tests undertaken in these outstanding world heritage sites. The field testing program comprises various geotechnical and geophysical field and laboratory tests aiming to define the physical, mechanical and dynamic properties of the hard soils/soft rock materials of the archaeological sites where these outstanding monuments are excavated and constracted. By analysis of linear correlations, some essential mechanisms should be underlined, which may connect the macrostructure to the microstructure of the geomaterial. A systematic method of analysis clearly appeared and emphasized the role of the bulk structure (i.e. grain size, grain contact, specific area, pore shape and microporosity) on petrophysical and petromechanical behavior of rock materials. The study revealed that the petrophysical and geomechanical properties of Calcarenitic rocks are influenced by size, shape, packing of grains, porosity, cement and matrix content, all controlled strongly by depositional fabric and postdepositional processes. The accurate analysis of the physiochemical and physicomechanical behaviours of Calcarenitic rocks that are utilized in Greek and Roman monuments at Alexandria allowed us to define the pathology of these monuments and to estimate the durability, climate change impact and ultimate geostatic loads that they can survive under their present geoenvironmental conditions.