Permeability Distribution Research Articles

Effects of Glacial Isostatic Adjustment on Fault Reactivation and Its Consequences on Radionuclide Migration in Crystalline Host Rocks

AbstractTo assess the robustness of a safety case for a deep geological repository (DGR), it is necessary to analyze a range of scenarios covering likely, less likely, and hypothetical future developments. Crystalline rock can, under ideal conditions, provide a suitable hydrogeologic barrier due to its extremely low matrix permeability. However, this host rock is often fractured, which can compromise its hydro-mechanical (HM) barrier function. We quantify how faults that are prone to reactivation during glacial events can affect radionuclide migration around a DGR in a crystalline host rock. We extend a previously developed finite element model of coupled fluid flow and radionuclide transport to numerically solve the component transport problem before and after fault reactivation. Assuming that fault reactivation is triggered by changes in mechanical boundary conditions, we derive heterogeneous permeability distributions in the reactivated faults by evaluating the Coulomb failure stress criterion of finite element solutions of a complementary hydro-mechanical problem. Specifically, we evaluate the consequences of glacial isostatic adjustment (GIA) during a glacial cycle. We find that the increased permeability in the reactivated faults accelerates the migration of radionuclides along the fault by channeling the flow, while it is reduced in the direction perpendicular to the fault. The channeling observed is also a result of heterogeneous permeability enhancement, and the flow fields differ from those of the previous model which postulated a homogeneous permeability enhancement. Although the proposed numerical workflow has been applied to the case of GIA, it is adaptable to study hydro-mechanical processes induced by seismic events or by hydrofracking in enhanced geothermal systems.

Assessment of petrophysical properties of reservoirs by well data

The use of traditional research complexes is of great importance in solving such important problems as correlating well sections for the purpose of lithological assessment, determining the reservoir properties of reservoir layers that make up the section, namely, saturation, porosity, permeability and clay content, as well as identifying oil and gas-bearing and aquifer layers. The complexity of solving such important problems depends entirely on the petrophysical properties of reservoir layers. For example, the electrical resistivities of oil-bearing and aquifer reservoirs are similar in magnitude, which leads to high clay content and significant variation in porosity throughout the field, which, as a result, makes it difficult to separate oil-bearing and aquifer reservoirs from each other. On the other hand, it is also known that an increase in the amount of cementing clays in reservoirs leads to a decrease in porosity and an increase in residual water saturation. In this regard, in the article, based on a set of well data, the values of the petrophysical parameters of reservoir layers were determined, models were built that determine the correlation between them, and regression equations based on the statistical distribution of these parameters were established. The article also built models for the distribution of porosity, permeability, clay content and oil and gas saturation along well sections. The constructed models provide the basis for forming a definite opinion about the field and obtaining results of both a scientific and practical nature when studying the filtration and reservoir characteristics of reservoir layers, as well as when determining the relationship between petrophysical parameters. The object of the study was the on the Kirmakinskaya (KS) and Underkirmaky (UK) formations of the productive series (PS) selected from the wells of the Gyurgyan-Deniz field.

Estimation of rock mass permeability using relaxation time and P‐wave velocity

AbstractDue to the inherent unpredictability of geological conditions, tunnelling operations are often at risk of encountering water inrushes. Such incidents can lead to construction delays, impose financial strains and pose significant safety threats to the workers involved. Water‐bearing geological formations are the main triggers for such incidents, with factors such as the positioning, water quantity and permeability distribution of these formations being key to predicting the occurrence and severity of water inrush disasters. By leveraging the complex interplay among relaxation time, P‐wave velocity and permeability within the rock's physical properties, a series of indoor tests were conducted on 40 artificial reef limestone cores to extract the necessary parameters. Through the analysis of the data, the comprehensive permeability prediction model was established, and the correlation coefficient was 0.9420 between the model's predictions and actual measurements. At the same time, through theoretical and mechanism analysis, the relationship between permeability and relaxation time and the relationship between permeability and P‐wave velocity were analysed. Finally, 10 natural reef limestone samples were used to verify the accuracy of the model. The prediction model enables an accurate evaluation of tunnel permeability, thus providing a scientific basis for the mitigation of tunnel water inrush hazards.

Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements

We introduce a novel method for estimating the spatial distribution of absolute permeability in oil reservoirs, consistent with well logging and well test measurements. The primary objective is to create a permeability map, incorporating the well test interpretation results and achieving hydrodynamic similarity to the actual permeability distribution around each well. This enhancement aims to improve the accuracy of reservoir modeling outcomes in reproducing real data. We utilize Nadaraya-Watson kernel regression to parameterize the two-dimensional spatial distribution of rock permeability. The kernel regression parameters are optimized by minimizing the discrepancies between actual and predicted values of permeability at well locations, the integral permeability of the reservoir domain around each well, and skin factors. This inverse optimization problem is addressed by repeatedly solving forward problems, where an artificial neural network (ANN) predicts the integral permeability of the formation surrounding a well and skin factor. The ANN is trained on a physics-based dataset generated through a synthetic well test procedure, which includes the numerical modeling of the bottomhole pressure decline curve in a reservoir simulator and its interpretation using a semi-analytical reservoir model. The proposed method is tested on the “Egg Model”, a synthetic reservoir with significant heterogeneity due to highly permeable channels. The permeability map created by our approach demonstrates hydrodynamic similarity to the original map. Numerical reservoir simulations, corresponding to the constructed and original permeability maps, yield comparable pore pressure and water saturation distributions at the end of the simulation period. Additionally, we observe a notable match in flow rates and total volumes of produced oil, water, and injected water between simulations. The developed approach outperforms kriging in terms of numerical reservoir modeling outcomes. This research advances existing geostatistical interpolation techniques by fusing well logging and well test data to build the reservoir permeability map through an optimization framework coupled with machine learning. Unlike traditional variogram-based geostatistical simulation algorithms, our method provides a permeability distribution that is hydrodynamically similar to the actual one, enhancing initial guess in the history matching process. The novel incorporation of well test interpretation results into the permeability map represents a significant improvement over existing methods, offering an innovative approach that can benefit the petroleum industry. We also provide recommendations for further development of the proposed algorithm to account for geological realism.

An Alternating Direction Multiplier Method for the Inversion of FDEM Data

In this paper, we focus on the numerical solution of nonlinear inverse problems in applied geophysics. Our aim is to reconstruct the structure of the soil, i.e., either its electrical conductivity or the magnetic permeability distribution, by inverting frequency domain electromagnetic data. This is a very challenging task since the problem is nonlinear and severely ill-conditioned. To solve the nonlinear inverse problem, we propose an alternating direction multiplier method (ADMM), we prove its convergence, and propose an automated strategy to determine the parameters involved. Moreover, we present two heuristic variations of the ADMM that either improve the accuracy of the computed solutions or lower the computational cost. The effectiveness of the different proposed methods is illustrated through few numerical examples.

Groundwater flow paths using combined self-potential, electrical resistivity, and induced polarization signals

SUMMARY The dam of Lampy (Black Mountain, Aude, France) is considered as one of the oldest dams in France. A geophysical survey is performed to better understand the pattern of groundwater flow downstream of this dam in the granitic substratum. Induced polarization is first used to image both electrical conductivity and normalized chargeability. Eight core samples of granite from this site are measured and analysed in the laboratory. Their electrical conductivity and normalized chargeability are expressed as a function of the porosity and cation exchange capacity (CEC). The field data and the petrophysical results are used to image the water content, the CEC and the permeability distribution of the substratum. Then, self-potential is used as a complementary passive geophysical technique, which, in absence of metallic bodies, is directly sensitive to groundwater flow through the so-called streaming potential effect. Indeed, the excess of electrical charges in the vicinity of the solid grains, in the so-called double layer, is dragged by the ground water flow generating in turn an electrical (streaming) current and therefore an electrical field. A map of the resulting self-potential signals is done over the area covered by the induced polarization profiles. This map shows a large positive anomaly with an amplitude of ∼80 mV possibly associated with upwelling groundwater in an area where the soil is water-saturated. A groundwater flow simulation is performed to model this anomaly. This is done in two steps. A preliminary groundwater flow model is built using the permeability and water content distributions obtained from the induced polarization data. Then, this groundwater flow model is updated using the information contained in the self-potential data including the electrical conductivity distribution obtained through resistivity tomography. The algorithm for the inversion of the self-potential data is validated through a 2-D numerical test. This analysis yields a groundwater flow model with the flow being focused through a high permeability zone. This study shows how three geoelectrical methods (self-potential, induced polarization and electrical resistivity) can be efficiently combined to image groundwater flow in the vicinity of a dam.

Integrated modelling of CO2 plume geothermal energy systems in carbonate reservoirs: Technology, operations, economics and sustainability

The rising CO2 content in the atmosphere calls for urgent decarbonization efforts. Carbon capture and storage (CCS) have proven effective in storing large volumes of CO2 in geological reservoirs. Nevertheless, challenges persist in identifying sustainable energy sources and efficient storage sites, especially in carbonate reservoirs. This study aims to characterize the Miocene carbonate reservoir in the Jintan Megaplatform, for CO2 storage and geothermal energy utilization. By integrating advanced technologies such as CO2 Plume Geothermal (CPG) with 3D seismic techniques, borehole analysis and numerical modelling, the study reveals the distribution of critical reservoir characteristics, including architecture, heterogeneity, karstification, fractures, porosity, permeability, temperature and pressure that are paramount for CO2 plume and energy production. The reservoir is estimated to store over 31 Gt of CO2, produce 3.824 × 1018 J of geothermal energy and ⁓44.3 GW of power. This power output can be utilized for electrification of the CCS operations, industrial and municipals, potentially generating over $1.4 billion in revenue. A flow-through model has been proposed to improve energy efficiency, reservoir management, and risk mitigation. This study advances to achieving net-zero targets and the United Nations Sustainable Development Goals on clean energy, climate action and sustainable communities.

Quantitative characterization and application of deep learning carbonate reservoir

Abstract Quantitative reservoir characterization is an important research task in carbonate reservoir development. At present, the comprehensive evaluation of drilling, logging, seismic, and productivity testing data is usually used, which requires a large number of test data and tedious manual data collation. Deep learning technology can independently learn complex reservoir characteristics, has a strong ability for high-dimensional data structure mining, and has a strong advantage in solving complex nonlinear reservoir description problems closely related to various geological characteristics. Therefore, this paper applies deep learning technology to reservoir characterization. First, a deep neural network model is constructed, and then seven characteristic parameters such as fracture density, fracture opening, fracture porosity, matrix porosity, total porosity, effective thickness, and specific oil production index are taken as input variables. After model training and verification, the effective permeability distribution map of the reservoir in the target area can be automatically generated. The results show that the deep learning method has strong adaptability and practicability for the quantitative characterization of complex reservoirs, and provides a new way of thinking for the quantitative characterization of reservoirs.

An Insight into Perfusion Anisotropy within Solid Murine Lung Cancer Tumors.

Blood vessels are essential for maintaining tumor growth, progression, and metastasis, yet the tumor vasculature is under a constant state of remodeling. Since the tumor vasculature is an attractive therapeutic target, there is a need to predict the dynamic changes in intratumoral fluid pressure and velocity that occur across the tumor microenvironment (TME). The goal of this study was to obtain insight into perfusion anisotropy within lung tumors. To achieve this goal, we used the perfusion marker Hoechst 33342 and vascular endothelial marker CD31 to stain tumor sections from C57BL/6 mice harboring Lewis lung carcinoma tumors on their flank. Vasculature, capillary diameter, and permeability distribution were extracted at different time points along the tumor growth curve. A computational model was generated by applying a unique modeling approach based on the smeared physical fields (Kojic Transport Model, KTM). KTM predicts spatial and temporal changes in intratumoral pressure and fluid velocity within the growing tumor. Anisotropic perfusion occurs within two domains: capillary and extracellular space. Anisotropy in tumor structure causes the nonuniform distribution of pressure and fluid velocity. These results provide insights regarding local vascular distribution for optimal drug dosing and delivery to better predict distribution and duration of retention within the TME.

The Main Controlling Factors of the Cambrian Ultra-Deep Dolomite Reservoir in the Tarim Basin

The genesis of deep-to-ultra-deep dolomite reservoirs in the Tarim Basin is crucial for exploration and development. The Cambrian subsalt dolomite reservoirs in the Tarim Basin are widely distributed, marking significant prospects for ultra-deep reservoir exploration. Based on big data methodologies, this study collects and analyzes porosity and permeability data of carbonate reservoirs in the western Tarim Basin, specifically targeting the Cambrian deep-oil and gas-reservoir research. Through an examination of the sedimentary evolution and distribution of carbonate–evaporite sequences, and considering sedimentary facies, stratigraphic sediment thickness, fault zone distribution, and source-reservoir assemblages as primary reference factors, the study explores the macro-distribution patterns of porosity and permeability, categorizing three favorable reservoir zones. The controlling factors for the development of Cambrian carbonate reservoirs on the western part of the Tarim Basin are analyzed from the perspectives of sedimentary and diagenetic periods. Factors such as tectonic activity, depositional environment, microbial activity, and pressure dissolution are analyzed to understand the main causes of differences in porosity and permeability distribution. Comprehensive analysis reveals that the porosity and permeability of the Series2 carbonate reservoirs are notably high, with extensive distribution areas, particularly in the Bachu–Tazhong and Keping regions. The geological pattern of “Three Paleo-uplifts and Two Depressions” facilitated the formation of inner-ramp and intra-platform shoals, creating conducive conditions for the emergence of high-porosity reservoirs. The characteristics of reservoir development are predominantly influenced by diagenetic and tectonic activities. The Miaolingian is chiefly affected by diagenesis, featuring high permeability but lower porosity and smaller distribution range; dolomitization, dissolution, and filling processes under a dry and hot paleoclimate significantly contribute to the formation and preservation of reservoir spaces. In the Furongian, the Keping and Bachu areas display elevated porosity and permeability levels, along with substantial sedimentary thickness. The conservation and development of porosity within thick dolomite sequences are mainly governed by high-energy-particulate shallow-shoal sedimentary facies and various dissolution actions during diagenesis, potentially indicating larger reserves.

Carbonate reservoir characterization and permeability modeling using Machine Learning ـــ a study from Ras Fanar field, Gulf of Suez, Egypt

Predicting facies and petrophysical properties along and between wells is challenging in carbonate reservoir modeling. In the Nullipore carbonate reservoir, Ras Fanar field, depositional and long-term diagenetic processes result in a high degree of heterogeneity and complex distribution of facies, which in turn affect the reservoir quality. This provides a significant obstacle to building accurate geological models. This study integrates thin sections, routine core analyses, and well logging data to overcome such difficulties and model the Nullipore carbonate facies and permeability. The detailed petrographic analysis revealed the existence of seven microfacies in the reservoir, which are summed up into three facies associations (FAs), each of which represents a specific reservoir rock type (RRT): (1) supratidal FA, (2) intertidal FA, and (3) shallow subtidal FA. The three FAs were correlated with the gamma-ray logs to create facies logs for the studied wells, which were further populated via the Truncated Gaussian Simulation method. Cross-validation was used to evaluate the model's accuracy. The analysis of the available core data infers that the three RRTs are prospective and have a wide permeability distribution. However, RRT3 constitutes the best reservoir quality. The sedimentological analysis revealed that the long-term diagenetic events, involving the dolomitization of limestone and the dissolution of allochems have a major role in improving the pore connectivity and permeability of the reservoir. Fracture characterization discloses that fractures play a significant role in fluid storage and migration. Three Machine Learning (ML) models, including Adaptive boosting (AdaBoost), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were developed to integrate the RRTs, porosity, and permeability to improve permeability prediction. Statistical analysis revealed that the XGB model outperforms other models and exhibits the highest prediction performance. The present study provides further insights into the characterization and modeling of facies and permeability of complex carbonate reservoirs. It can be applied in similar geological settings to better interpretation of depositional and diagenetic controls on reservoir quality assessment and aid in the field development plan.

Impact of stress regime change on the permeability of a naturally fractured carbonate buildup (Latemar, the Dolomites, northern Italy)

Abstract. Changing stress regimes control fracture network geometry and influence porosity and permeability in carbonate reservoirs. Using outcrop data analysis and a displacement-based linear elastic finite-element method, we investigate the impact of stress regime change on fracture network permeability. The model is based on fracture networks, specifically fracture substructures. The Latemar, predominantly affected by subsidence deformation and Alpine compression, is taken as an outcrop analogue for an isolated (Mesozoic) carbonate buildup with fracture-dominated permeability. We apply a novel strategy involving two compressive boundary loading conditions constrained by the study area's NW–SE and N–S stress directions. Stress-dependent heterogeneous apertures and effective permeability were computed in the 2D domain by (i) using the local stress state within the fracture substructure and (ii) running a single-phase flow analysis considering the fracture apertures in each fracture substructure. Our results show that the impact of the modelled far-field stresses at (i) subsidence deformation from the NW–SE and (ii) Alpine deformation from N–S increased the overall fracture aperture and permeability. In each case, increasing permeability is associated with open fractures parallel to the orientation of the loading stages and with fracture densities. The anisotropy of permeability is increased by the density and connectedness of the fracture network and affected by shear dilation. The two far-field stresses simultaneously acting within the selected fracture substructure at a different magnitude and orientation do not necessarily cancel each other out in the mechanical deformation modelling. These stresses affect the overall aperture and permeability distributions and the flow patterns. These effects – potentially ignored in simpler stress-dependent permeability – can result in significant inaccuracies in permeability estimation.

Classification of rock types of porous limestone reservoirs: case study of the A oilfield

Rock types with similar lithological components and pore structures form the basic units of porous limestone reservoirs; this influences the reservoir evaluation efficiency and water injection development. As the main oil and gas pay zone in central Iraq, the Cretaceous Khasib Formation reservoirs are influenced by deposition, dissolution, and cementation. There is strong vertical heterogeneity in the most important zone of the Kh2 layer, with diverse rock types and complex pore structures. Based on core observation and casting thin-section identification, the Kh2 layer in the study area was divided into eight lithofacies types as argillaceous bioclastic wackestone, planktic foraminiferium wackestone, lamellar bioclastic wackestone, intraclastic–bioclastic packstone, patchy green algae packstone, green algae and pelletoid packstone, benthic foraminiferium–bioclastic packstone, and intraclastic grainstone. Along with the reservoir void space types of the lithofacies, capillary pressure curves are used to quantitatively analyze the throat and pore features of the different lithofacies. From the porosity–permeability cross-plot characteristics and distribution of pore types, 14 petrophysical facies are obtained. Finally, based on the differences between the lithofacies and petrophysical facies, the Kh2 member is divided into 13 rock types with different geological origins and petrophysical characteristics. Among these, the rock type RT1-8-14 has the best and rock type RT1-1-1 has the worst physical properties among the reservoir rock types. This study provides an optimization method for carbonate reservoir evaluation and is expected to be beneficial for efficient development of similar carbonate reservoirs.

Influence of Magnetic Polarization on Diagrams of Inductive and Electromagnetic Logs

Abstract ––We present the results of the study of the effect of induced magnetic polarization of clay beds under the influence of an external harmonic electromagnetic field (frequencies 70 and 875 kHz). A two-stage numerical modeling procedure is proposed. At the first stage we determine the effective relative magnetic permeability of a synthetic sample with inclusions of clay particles. In this case a 3D heterogeneous mesh sample is generated. Then we numerically model a spatial distribution of an electric field. The electromotive force (EMF) induced in the measuring coil is calculated from this distribution. Relative magnetic permeability is determined by comparison with EMF of homogeneous samples with different values of magnetic permeability. It has been found that during the electric field excitation by an alternating current coil, the effect of induced magnetic polarization appears in the sample with clay particles. Its manifestation is that the effective magnetic permeability becomes complex. At the second stage we calculate the EMF diagram of the three-coil logging probe in the macro-model ‘clay cap – reservoir’. Magnetic permeability of the clay cap is given by a complex value. In the generated logs, extremes appear in the vicinity of the bottom of the clay cap; they do not correspond to the distribution of electrical conductivity and magnetic permeability in the given model. They can be incorrectly interpreted when analyzing real logs into individual formations. Numerical modeling at all stages is performed by the Vector Finite Element Method on a consistent adaptive tetrahedral partition and the first-order Webb vector basis.

Compaction Creep and Evolution of Transport Properties of Carbonate Fault Gouges During the Percolation of CO2‐Rich Fluids

AbstractTo investigate the impact of CO2‐rich fluids on compaction behaviors and transport properties in carbonate fault zones, we conducted compaction‐coupled fluid flow experiments with CO2‐rich fluids percolating precompacted calcite aggregates. Our findings reveal distinct responses among samples subjected to different fluid conditions. Specifically, samples exposed to dry conditions exhibited negligible compaction strain, while those under wet‐closed conditions displayed relatively minor strain. In contrast, samples subjected to flow‐through conditions demonstrated significant compaction strain, with strain rates higher by 2–3 orders of magnitude than closed conditions due to enhanced pressure solution, subcritical cracking, and chemical dissolution. Strain rate, permeability, and grain size distribution exhibited spontaneous variations in response to fluid flow and compaction. Microstructures and mechanical and transport data suggest that deformation during the initial infiltration of CO2‐rich fluids was dominated by subcritical cracking, followed by pressure solution as grain size evolved, which resulted in compaction and reduced permeability. The persistent infiltration of CO2‐rich fluids further enhanced inhomogeneous dissolution‐precipitation with preferred dissolution channels serving as fluid pathways. The re‐precipitations may cement fault rocks and form low permeability seals, resulting in anisotropic fluid flow and localized fluid pressure in fault zones. As applied to nature, our results provide experimental evidence for the evolution of internal structures and transport behaviors, shedding important light on the mechanisms and sealing potential of carbonate faults in response to the infiltration of CO2‐rich fluids during the post‐seismic and inter‐seismic processes.

Detailed effects of reservoir permeability distribution differences on enhanced geothermal systems performance

In enhanced geothermal systems, fractured reservoir permeability significantly affects geothermal exploitation efficiency. However, the detailed effects are not fully understood while most previous literature ignored the spatial differences of reservoir permeability because of the complexity and heterogeneity of fracture distribution. This study aims to reveal the quantitative relationship between the geothermal system’s heat extraction performance and the distributed permeability, through investigating heat extraction ratio and flow impedance by building a 3D thermal-hydraulic coupled model with discrete fracture network. The current study also compared the effects of twenty-nine kinds of fracture network distributions at various depths. It is found that higher fracture permeability around production well (800×10−12 m2) more significantly improves heat extraction rate by 30% rather than higher permeability in other areas in the first year. It is also found that fracture permeability changes on both sides of predominant flow regions cause a lower heat extraction rate. Lower fracture permeability at bottom and top layers (25×10−12 m2) increases the heat extraction rate by 1.17 MW in the 30th year. The proppant distribution affects the pressure distribution in fractured reservoirs. Permeability distribution variation has small effects on the heat extraction ratio. These results provided theoretical basis for fractured reservoir construction, optimal proppant pumping scheme and reservoir thermal output prediction.

Study on Optimization of Stimulation Technology of Heterogeneous Porous Carbonate Reservoir

Mishrif (M) reservoir of Faihaa (F) oilfield in Iraq is a heterogeneous porous carbonate reservoir. The reservoir properties of each reservoir unit differ greatly, and the distribution of porosity and permeability is non-uniform. Some reservoir units have the problem that the expected production cannot be achieved or the production decline rate is too fast after matrix acidification. This work optimized and compared the process of acid fracturing and hydraulic fracturing techniques. The Mishrif B (MB) and Mishrif C (MC) layers are selected as the target units for fracturing and the perforation intervals are optimized. The acid fracturing process adopted the acid fracturing technology of guar gum pad fluid and gelled acid multi-stage injection. According to the wellhead pressure limit and fracture propagation geometry, the pumping rate is optimized. The recommended maximum pumping rate of acid fracturing is 5.0 m3/min, and the optimized acid volume is 256.4 m3. The pressure changes during hydraulic fracturing and acid fracturing are different. It is recommended that the maximum hydraulic fracturing pump rate is 4.5 m3/min for MB and MC layers, and the amount of proppant in MB and MC layers is 37.5 m3 and 43.7 m3, respectively. The production prediction of two optimized processes is carried out. The results showed that the effect of acid fracturing in MB and MC layers is better than hydraulic fracturing, and it is recommended to adopt acid fracturing technology to stimulate MB and MC layers. Acid fracturing operation is carried out in the X-13 well, and better application results are achieved. The results of this study provide optimized reference ideas for reservoir stimulation in heterogeneous porous reservoirs.

Experimental investigation on macroscopic fracture and microstructural evolution of granite under high-temperature steam action

This study conducted simulated experiments on the thermal fracturing of granite under non-uniform stress conditions using the method of injecting high-temperature and high-pressure steam. Additionally, a comprehensive investigation was carried out on the thermal fracturing characteristics of granite after steam treatment, as well as the distribution patterns of cracks. The results show that: Firstly, the process of fracturing granite using high-temperature and high-pressure steam as a medium can be divided into five stages. The threshold temperature for thermal fracturing of granite is 260 ℃, at which point a large number of cracks develop or form interconnected networks. Secondly, under high-temperature conditions, the tensile strength of granite is greatly reduced. The temperature at which brittle fracture occurs in granite during the experiment was found to be 427 ℃ at a steam pressure of 10.6 MPa. Thirdly, under the influence of high-temperature and high-pressure steam, granite not only generates primary cracks along the direction perpendicular to the minimum principal stress but also produces a certain number of secondary cracks in other directions. The crack propagation direction can be determined from the velocity and permeability distribution maps of fractured granite. Finally, the fracture surface of granite exhibits significant brittle characteristics. The fracture morphology mainly includes transgranular and intergranular fractures. In the experiment, the fracture surface also showed fragmented and fatigue-induced fracture features. The fractal dimension of micro-morphological features can be used to assess the extent of thermal fracturing at different locations on the fracture surface. Through macroscopic fracturing experiments and comprehensive analysis of theory, wave velocity, and micro-morphology, the study obtained relevant crack propagation patterns and the formation of cracks in different directions. This is of practical significance in the formation of interconnected spatial fracture networks, thereby helping to increase the thermal exchange surface area of artificial reservoirs and improve the efficiency of geothermal exploitation.

Porosity and surface curvature effects on the permeability and wall shear stress of trabecular bone: Guidelines for biomimetic scaffolds for bone repair

Scaffolds are an essential component of bone tissue engineering to provide support and create a physiological environment for cells. Biomimetic scaffolds are a promising approach to fulfill the requirements. Bone allografts are widely used scaffolds due to their mechanical and structural characteristics. The scaffold geometry is well known to be an important determinant of induced mechanical stimulation felt by the cells. However, the impact of allograft geometry on permeability and wall shear stress distribution is not well understood. This information is essential for designing biomimetic scaffolds that provide a suitable environment for cells to proliferate and differentiate. The present study investigates the effect of geometry on the permeability and wall shear stress of bone allografts at both macroscopic and microscopic scales. Our results concluded that the wall shear stress was strongly correlated with the porosity of the allograft. The level of wall shear stress at a local scale was also determined by the surface curvature characteristics. The results of this study can serve as a guideline for future biomimetic scaffold designs that provide a mechanical environment favorable for osteogenesis and bone repair.

Freeze-Casting of Alumina and Permeability Analysis Based on a 3D Microstructure Reconstructed Using Generative Adversarial Networks.

In this study, alumina ceramics with hierarchical pores were successfully fabricated using freeze casting. Experimental studies show that both the solid loading of the slurry and the thermal insulation layer at the interface of the slurry and cooling plate can influence the pore characteristics of cast samples. In order to examine the pore characteristics and evaluate the permeability of the freeze-cast samples fabricated under different conditions, a generative adversarial network (GAN) method was employed to reconstruct the three-dimensional (3D) microstructure from two-dimensional (2D) scanning electron microscopy (SEM) images of the samples. Furthermore, GAN 3D reconstruction was validated against X-ray tomography 3D reconstruction results. Based on the GAN reconstructed microstructures, the permeability and pore distribution of the various samples were analyzed. The sample cast with 35 wt.% solid loading shows an optimal permeability.