Geomechanical Characterization Research Articles

Geomechanical Characteristics and Wellbore Instability for Nahr Umr Oil Field

Geomechanical Characteristics and Wellbore Instability for Nahr Umr Oil Field

Petrographic, Mineralogical and Geo-mechanical Characteristics of Marble from Mohmand District, Pakistan: Implications for its Use in Construction Industry

Petrographic, Mineralogical and Geo-mechanical Characteristics of Marble from Mohmand District, Pakistan: Implications for its Use in Construction Industry

Employing Geomechanical Characteristics for the Sandstone Reservoirs to Mitigate the Hazards Associated with Sand Production

Employing Geomechanical Characteristics for the Sandstone Reservoirs to Mitigate the Hazards Associated with Sand Production

Optimizing Deep Geothermal Drilling for Energy Sustainability in the Appalachian Basin

This study investigates the geological and geomechanical characteristics of the MIP 1S geothermal well in the Appalachian Basin to optimize drilling and address the wellbore stability issues encountered. Data from well logs, sidewall core analysis, and injection tests were used to derive elastic and rock strength properties, as well as stress and pore pressure profiles. A robust 1D-geomechanical model was developed and validated, correlating strongly with wellbore instability observations. This revealed significant wellbore breakout, widening the diameter from 12 ¼ inches to over 16 inches. Advanced technologies like Cerebro Force™ In-Bit Sensing were used to monitor drilling performance with high accuracy. This technology tracks critical metrics such as bit acceleration, vibration in the x, y, and z directions, Gyro RPM, stick-slip indicators, and bending on the bit. Cerebro Force™ readings identified hole drag caused by poor hole conditions, including friction between the drill string and wellbore walls and the presence of cuttings or debris. This led to higher torque and weight on bit (WOB) readings at the surface compared to downhole measurements, affecting drilling efficiency and wellbore stability. Optimal drilling parameters for future deep geothermal wells were determined based on these findings.

Petrography and geomechanical properties of some mine waste rocks from the Tarkwaian Group in Ghana: implications for their use as aggregates in general engineering works

This research investigated some mine waste rocks from the southwestern part of the Tarkwaian Group in Ghana to check their suitability for general engineering works using petrography and geomechanical characteristics of the rocks. The rocks were subjected to a total of ten (10) tests, including Aggregate impact value (AIV), Aggregate crushing value (ACV), Loss Angeles abrasion (LAA), Specific gravity (SG), and Water absorption (WA), as well as flakiness and elongation index, slake durability (SD), chloride test (CT), sulphate test (ST), and ten percent fines (TPF). The tests were carried out per the American Society for Testing and Materials guidelines (ASTM). The following average test results were obtained: AIV (7.12%), ACV (15.60%), LAA (23.16%), TPF (269.7 kN), sulphate (0.0215%), chloride (0.00123%), flakiness (39.5%) and elongation (39.4%). The mine waste rocks are dominated by quartz and sericite, according to the petrographic examination and thus, can be classified as quartzites. Except for the flakiness and elongation index tests, all the results were consistent with the ASTM standards. The analysis carried out on the selected mine waste rocks from the southwestern part of the Tarkwaian Group could be catastrophic for heavy engineering works such as roads, bridges, and heavy buildings but can be used for pavements, lightweight buildings, monuments, and decoratives due to the percentage of quartz present to compensate for the weakness posed by the sericite.

Aproximación geomecánica usando velocidades de onda y su relación con el parámetro “b”

In this research, an exhaustive and rigorous analysis about microseismicity and its importance for the analysis of geological structures will be presented, with special attention on the application of the GSI table and its relationship with safety. Microseismicity is the technique of detection and analysis of small seismic events that occur during the advance and mining exploitation, and its study is fundamental to guarantee a safe and efficient operation. The presence of these seismic events indicates the existence of stresses and deformations in the geological structures, which can have a significant impact on the safety of the operation. Therefore, microseismicity monitoring is an essential tool for geomechanical risk management. In this thesis the relationship between microseismicity and geological structures will be explored, using the GSI table as an important tool for the interpretation of microseismic data. The GSI table is a tool developed by the International Society for Rock Mechanics to assess the geomechanical stability of rock structures. In this work, the microseismic data will be analyzed and correlated with the GSI table, which will allow a better understanding of the geomechanical characteristics of the mine and a more accurate evaluation of geomechanical risk.

Smart predictions of petrophysical formation pore pressure via robust data-driven intelligent models

AbstractPredicting pore pressure in the formation is crucial for assessing reservoir geomechanical characteristics, designing drilling schemes/mud programs, and strategies to enhance oil recovery. Accurate predictions are vital for safe and cost-effective exploration and development. Recent research has seen the emergence of intelligent models utilizing machine learning (ML) and deep learning (DL) algorithms, offering promising outcomes. However, there remains a need to identify the most accurate and dependable model among these. This study aims to address this gap by comparing the performance of various ML and DL models, as reported in existing literature, to determine the optimal approach for pore pressure prediction. The sorted machine learning (ML) and deep learning (DL) regression algorithms used for the comparative analysis are decision tree (DT), extreme gradient boosting (XGBoost), random forest (RF), recurrent neural network (RNN), and convolutional neural network (CNN). A total dataset of 22,539 is gathered from five wells (15/9-F-1 A, 15/9-F-1 B, 15/9-F-11 A, 15/9-F-11 T2, and 15/9-F-14) drilled at North-sea Volve oil field, Norway. The first four wells are used to train and test the ML and DL algorithm, and the remaining well (15/9-F-14) is used to evaluate the best-performing algorithm’s universality in predicting pore pressure at the field of study. Seven different petrophysical parameters are used as input parameters to develop the predictive models. Statistical performance metrics are carried out to analyze the applied ML and DL performance. Based on performance indicators, the RF algorithm showed superior results compared to other predictive models with R2 and RMSE values of 0.97 and 2.70 MPa, respectively. Furthermore, the best-performing predictive model with low prediction error RMSE value is applied to the other well dataset from the field of study to access the universality of the RF algorithm to predict pore pressure in the field of study. The results of the universality analysis show a satisfactory prediction accuracy with R2 and RMSE values of 0.905 and 6.48 MPa, respectively.

A Study on Three-Dimensional Multi-Cluster Fracturing Simulation under the Influence of Natural Fractures

Multi-cluster fracturing has emerged as an effective technique for enhancing the productivity of deep shale reservoirs. The presence of natural bedding planes in these reservoirs plays a significant role in shaping the evolution and development of multi-cluster hydraulic fractures. Therefore, conducting detailed research on the propagation mechanisms of multi-cluster hydraulic fractures in deep shale formations is crucial for optimizing reservoir transformation efficiency and achieving effective development outcomes. This study employs the finite discrete element method (FDEM) to construct a comprehensive three-dimensional simulation model of multi-cluster fracturing, considering the number of natural fractures present and the geo-mechanical characteristics of a target block. The propagation of hydraulic fractures is investigated in response to the number of natural fractures and the design of the multi-cluster fracturing operations. The simulation results show that, consistent with previous research on fracturing in shale oil and gas reservoirs, an increase in the number of fracturing clusters and natural fractures leads to a larger total area covered by artificial fractures and the development of more intricate fracture patterns. Furthermore, the present study highlights that an escalation in the number of fracturing clusters results in a notable reduction in the balanced expansion of the double wings of the main fracture within the reservoir. Instead, the effects of natural fractures, geo-stress, and other factors contribute to enhanced phenomena such as single-wing expansion, bifurcation, and the bending of different main fractures, facilitating the creation of complex artificial fracture networks. It is important to note that the presence of natural fractures can also significantly alter the failure mode of artificial fractures, potentially resulting in the formation of small opening shear fractures that necessitate careful evaluation of the overall renovation impact. Moreover, this study demonstrates that even in comparison to single-cluster fracturing, the presence of 40 natural main fractures in the region can lead to the development of multiple branching main fractures. This finding underscores the importance of considering natural fractures in deep reservoir fracturing operations. In conclusion, the findings of this study offer valuable insights for optimizing deep reservoir fracturing processes in scenarios where natural fractures play a vital role in shaping fracture development.

Detection of Cliff Top Erosion Drivers through Machine Learning Algorithms between Portonovo and Trave Cliffs (Ancona, Italy)

Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R2 of 0.84), with FS decreasing with slope height.

Geomechanical Characterization of a Brazilian Experimental Site: Testing, Interpretation, and Material Properties

Clarity in monitoring existing foundation structures demands innovative safety analysis methodologies for deep foundations, necessitating advanced models calibrated with real-world field parameters. Understanding controlled conditions, including geotechnical profiles, seismic attributes, and soil mechanics, is crucial. A dedicated research group at the University of São Paulo spent three years refining these conditions, characterizing an experimental field along a canal in São Paulo. This study pioneers geotechnical and geomechanical characterization of the region’s tertiary sediments in São Paulo, offering valuable insights for current and future applications. Standard penetration tests with torque (SPT-Torque), piezocone tests (CPTu), and measurement of wave velocity (Vs) with piezocone tests (S-CPTu) were carried out. The exploration of the subsoil shows that in up to 2 m of excavation, there are clays and silts, and in up to 25 m, there is a significant layer of compact fine sand that has high values of tip resistance and wave velocities more significant than 100 m/s. In the electric cone tests, the abacus used displayed a reasonable classification. All propositions identified the transition from the surface soil to the sandy soil at between 3 and 25 m. The soil classification values were obtained with the data of the field parameters, and the geotechnical and mechanical parameters were estimated. No differences were detected among the values found in the SPT-T and CPTu tests for the values obtained via cone resistance. This demonstrates the reliability of both methods. In addition, using the CPTu test to identify the stratigraphic profile horizons employing the soil’s mechanical behavior when the cone is driven proved appropriate.

Production of Synthetic Carbonate Rocks Using Limestone Mining Waste: Mineralogical, Petrophysical and Geomechanical Characterization

Production of Synthetic Carbonate Rocks Using Limestone Mining Waste: Mineralogical, Petrophysical and Geomechanical Characterization

Rockfall susceptibility analysis of the “San Michele Arcangelo” historic trail (Central Italy) based on virtual outcrops and multiple propagation models

The “San Michele Arcangelo” historic trail (Aurunci Regional Park, central Italy), located along the southern slope of Mt. Altino, which is highly prone to rockfalls, is hiked every year by thousands of faithful on pilgrimage who are exposed to such kinds of instabilities. To contribute to a better understanding of the condition and evolution of such phenomena, providing a susceptibility scenario able to support the adoption of mitigation measures, a specific analysis was completed based on field and literature data. Three-dimensional virtual outcrop models were obtained through both photogrammetric and iPad Pro LiDAR surveys to derive geomechanical features of outcropping rocks and estimate potential detaching block volume. Possible mechanisms of detachment were then analyzed using the Markland test method. Susceptibility to rockfall propagation and block deposition was analyzed using GIS processing and rockfall propagation simulations based on deterministic/stochastic models. Such models consist of a combination of a deterministic algorithm able to simulate the physics of rockfall movement during propagation and a stochastic treatment of input parameters based on random sampling within defined interval. Two different rockfall simulations were compared in this study using the Rockyfor3D™ (ecorisQ Association) software and the newly developed RocFall3© (RocScience) code. The use of iPad Pro LiDAR survey provides accurate and high-resolution point clouds, with high speed of acquisition and real-time processing of data. Discontinuity sets identified from 3D models have represented the input for kinematic analyses of slopes, allowing to identify potential failure mechanisms among wedge and planar sliding or toppling. Rockfall simulations indicate the potential for rock blocks propagation and deposition over the whole study area. Comparing the results of rockfall simulations with the geomorphological map of the area it is evident the control exerted by the hydrographic network on rockfall propagation as demonstrated by the presence of screes and slope deposits along the main channels. Understanding the potential susceptibility to both propagation and deposition of rock blocks along the trail provides useful indication for the assessment of appropriate mitigation measures to realize for the safer touristic fruition of the site.

Geomechanical characterization and mineralogical correlation of compositionally diverse world-class Kazakhstani source rocks: Insights from nanoindentation testing

Extensive nanoindentation testing over a range of deflection depths of up to 4 μm yielded a large dataset, providing a viable framework for the statistical assessment of the mechanical properties, specifically elastic modulus (E) and hardness (H), of compositionally diverse organic-rich mudstone samples. The data from indentations as shallow as 300–400 nm were clustered using the k-means algorithm to identify three mechanical categories in the samples: a soft pseudophase (e.g., organic matter, gypsum, and clay minerals), a stiff pseudophase (e.g., quartz and feldspar), and a transitional composite-like pseudophase bridging the soft and hard minerals. The initially diverse values of E and H for the mechanical pseudophases were observed to converge to a constant value at indentations beyond 2–2.5 μm (varying between different samples), implying the existence of a minimal probing depth for assessing the bulk E and H of heterogeneous mudstone samples. The obtained bulk E and H values (8–21 GPa and 0.3–0.9 GPa, respectively) demonstrated a strong correlation with the mineralogical composition of the indented samples. Despite containing a notable proportion of mechanically stiff components (>45 vol%), the bulk mechanical parameters determined in this study were significantly lower than those reported for major shale formations such as the Barnett and Longmaxi Shale. This discrepancy is primarily due to the presence of organic matter with low thermal maturity (Ro < 0.6%), which constitutes <36 vol% of the samples, and a significant gypsum content, accounting for <15 vol%.The employed approach not only demonstrated the importance of choosing the proper indentation depths for investigating the mechanical properties of highly heterogeneous mudstone rocks and their constituent minerals, but it also illustrated the capability of examining various volumes of investigation using nanoindentation, approaching macroscopic values, and identifying a representative element volume (REV). The findings also provided crucial insights into the fracability and overall producibility of the investigated formations, thereby enhancing our understanding of their extraction potential.

Enhancing geomechanical characteristics of calcium sulfoaluminate (CSA) cement-treated soil under low confining pressures

This study examines the efficacy of employing calcium sulfoaluminate (CSA) cement, an environmentally friendly binder, for enhancing the geomechanical characteristics of sand, particularly under low confining pressure conditions. A series of triaxial consolidated drained tests were performed on sand samples treated with varying content (5, 7, and 10%) of CSA cement and 10% ordinary Portland cement (OPC) under various low confining pressures (50, 100, 200, and 400 kPa). The test findings demonstrated the importance of cement content and confining pressure on the mode of failure, stress–strain and volumetric behavior, failure characteristics, and shear strength parameters of the treated quartz sand. After a curing period of 14 days, samples treated with 10% CSA cement exhibited a remarkable 212% increase in peak deviator stress and an 89% reduction in axial strain at failure, indicating higher initial stiffness compared to untreated samples under a 400 kPa confining pressure. Furthermore, the samples treated with 10% CSA exhibited higher peak deviator stress, initial stiffness, and strength development compared to those treated with 10% OPC. The scanning electron microscopy analysis provides insights into particle breakage and bond degradation processes, which increase with confining pressure in CSA-treated samples. Also, the mode of failure analysis reveals a transition from ductile to slightly brittle behavior with increasing cement content. Notably, the geomechanical properties of the treated material emphasized the significant impact of CSA cement on soil improvement. Thus offering a sustainable alternative for soil improvement in construction projects.

Petrographical, petrophysical, and geomechanical characterization of the Pliocene sandstone reservoirs of the Scarab Gas Field, deepwater Nile Delta, Egypt – Inferences on reservoir development

This study presents a comprehensive reservoir characterization of the channel sandstones of the Pliocene El Wastani Formation from the deepwater Scarab field, in Egypt. Routine core analysis, wireline logs, petrographic thin sections, X-ray diffraction, and drilling data were integrated to characterize the petrographical, petrophysical and geomechanical properties of the studied gas reservoirs to infer the implications for reservoir development. The petrographic analysis indicates fine to medium-grained glauconitic subfeldspathic wacke with dominantly primary intergranular porosity and minor secondary porosity contributed by feldspar dissolution. The reservoir facies exhibit pore-filling detrital clay (dominantly illite, smectite mixed-layer clay) along with minor carbonate and silica cementation. These reservoirs are megaporous and consist of an isotropic pore system, with 20–35 % porosity, 2400–3400 mD horizontal permeability, and 1300–2900 mD vertical permeability. Wireline log-based petrophysical assessments indicate excellent reservoir qualities with low shale volume (9–23%) and high hydrocarbon saturation of about 65–85%. Based on the geomechanical analysis, rock-mechanical properties, pore pressure, vertical stress, and minimum horizontal stress magnitudes were interpreted. The production and depletion-related risks were analyzed by utilizing geomechanical modeling which provided a quantitative assessment of drawdown and depletion limit to ensure sand-free hydrocarbon production without the risk of shear slippage on pre-existing weak planes. Considering the average unconfined compressive strength of 14 MPa, the Pliocene reservoir will require a 9.2 MPa depletion or 16.9 MPa drawdown to reach the onset of sanding, while the reservoir can be depleted to a pore pressure level of 4 MPa before it induces shear slippage on the optimally oriented fractures or fault planes. Inferences are drawn on reservoir quality, and reservoir development strategies are discussed accordingly.

Feasibility of small-scale multipurpose hydropower projects in Karbi-Anglong District of Assam, India: a geotechnical appraisal

ABSTRACT The potential of small hydropower projects (SHP) in Karbi-Anglong District of Assam has been investigated, and site-specific detailed studies have been done at three potentially feasible sites. Geo-mechanical characterization of rocks at the proposed sites shows higher values for strength parameters and falls in Good rock category in Rock Mass Rating class. Run of the River (RoR) scheme with a small reservoir is recommended in the area. A storage type layout plan for the site located on Kalyani river would have a 2.38 MW power-generation capacity, whereas the RoR type would be able to generate 953.1 kW. The RoR scheme for the Daigurung site will be able to generate 175.8 kW and that for Nambar 53.0 kW. Likewise, the storage type scheme will produce 505.4 kW and 137.7 kW for Daigurung and Nambar sites, respectively. These irrigation-based multipurpose projects will boost agriculture activities in the downstream areas and also meet the captive power requirement.

Statistical comparative analysis between strength parameters of a bauxite mining tailing through laboratory tests and numerical simulation

Tailings dams are the primary global way to solve the problem of low storage availability for tailings resulting from mining. However, the recent ruptures of this reservoir in Brazil, such Mariana and Brumadinho dams, draw attention to the importance of studies determining the geomechanical characteristics and behavior of mining tailings. With the implementation of Law nº 14066/2020, which establishes the National Dam Safety Policy, there was an increase in demands for the monitoring and inspecting dams and the imposition of bills. Given this, triaxial tests were carried out to characterize the mechanical behavior of bauxite mining tailing from a Brazilian deposit. Confining pressure of 25, 50, 100, and 200 kPa was applied. Moreover, data were obtained for applying numerical simulation modeling based on the parameters determined for this material. Therefore, this paper describes a comparison using exploratory data analysis between the results obtained in the laboratory by the triaxial test and the results obtained by the finite element method (FEM) analysis with the aid of the Abaqus software. Statistical analysis showed a greater discrepancy between laboratory and numerical simulation results. The most significant differences observed between laboratory and numerical simulation results occurred for the lowest stress levels. The greatest differences observed between laboratory and numerical simulation results occurred for the lowest stress levels. The difference was significant also in terms of behavior. This fact highlights the need for extensive geotechnical investigation to characterize the material adequately and obtain parameters to simulate the correct tailing behavior and develop a safe design for storage structures.

Machine learning classification approaches to optimize ROP and TOB using drilling and geomechanical parameters in a carbonate reservoir

Drilling optimization has been broadly developed in terms of influential parameters. The assessment time and the effects of both geomechanical and drilling parameters were vital challenges of investigations. Drilling factors are applied force or rotation of drilling agents such as weight on bit (WOB), and geomechanical features represent mechanical indexes of rocks including unconfined compressive strength (UCS). Optimization efforts have been demonstrated on complex prediction methods whereas the simplicity of classification can offer some optimal ranges utilizing machine learning classifications in an accelerated process. In this study, a novel procedure using the supervised and semi-supervised learning approaches was conducted to classify and optimize the rate of penetration (ROP) and torque on bit (TOB). Firstly, in the case well, user-defined classes were assigned based on geomechanical units (GMU) and the ranges of high ROP and low TOB, thus classes divided drilling factors as GMUs of the case. Secondly, the feature selection was carried out by neural pattern recognition with three multi-objective optimization methods for classification. The inputs of classifications were WOB, hook load, pump pressure, flow rate, UCS, and internal friction angle. Classification approaches were decision trees, support vector machine (SVM), and ensemble learning. Finally, the bagged trees permutation and Laplacian SVM (LapSVM) algorithm separately revealed the significance of parameters and predicted the optimal ROP and TOB regions. Findings showed (1) in supervised classification of the case well, the cubic SVM and bagged trees had the highest area under the curve (AUC) and accuracy, on average 0.97 and 0.96, respectively. (2) The average accuracy of the supervised classifications in a test well was 91% except for the fine SVM, which makes them reliable for the fields with the least information. (3) The permutation outcomes for significant features, flow rate and UCS, exposed influential parameters for ROP and TOB optimization. (4) The semi-supervised method, LapSVM, not only acquired both ROP and TOB labels with an accuracy of 88% but also presented their optimal ranges in 95% of the assessed zones. (5) LapSVM deals with a limited training section perfectly opposed to the supervised version, which is vital for drilling investigation. (6) Implementing machine learning classification approaches with rock properties is a key factor in achieving effective drilling parameters in less time. More importantly, the recommended drilling factors concerning geomechanical properties can ameliorate both drilling performance and perception of upcoming collapse.

An Explicit Data-Driven Model for Estimation of Free and Absorbed Gas Volumes in Shale Gas Reservoirs.

Shale gas reservoir fluid flow is characterized by complex interactions between a number of variables, including stress sensitivity, matrix shrinkage, and critical desorption pressure. The behavior and productivity of shale gas reservoirs are significantly influenced by these variables. Therefore, in this study, an in-house finite element poro-elastic simulator was integrated with an in-house Fortran-coded artificial neural network model for accurate estimation of free and absorbed gas volumes in shale gas reservoirs, taking into account the combined effects of stress sensitivity, matrix shrinkage, and critical desorption pressure. The in-house finite element simulator was used to create a database of free and absorbed gas volumes under different reservoir, fluid, and geo-mechanical characteristics of shale gas, while the in-house neural network code was utilized using the outcomes of the poro-elastic simulator in order to propose an explicit data-driven mathematical formula of free and absorbed volumes of shale gas. The study findings indicate that the neural network model achieved high accuracy with an R-squared value exceeding 99% during both the training and blind-testing phases. The newly developed ANN-based correlations can be used as a quick and easy-to-use tool to forecast the volume of shale gas in place both free and adsorbed without any prior experience with utilizing or implementing machine learning models.

Research Progress on the Geomechanical Properties of Block-in-Matrix Rocks.

The differences in geomechanical properties and the uncertainty in the spatial distribution of Bimrock pose significant challenges to the construction and disaster prediction of geotechnical engineering. To clarify the geomechanical characteristics of Bimrock, this paper summarizes the basic concepts and classification methods of Bimrock at home and abroad. It discusses the methods and characteristics of determining the geometric features of Bimrock blocks and explores the influencing factors and laws of failure modes and strength under different stress states of Bimrock. The study finds that the failure mode of Bimrock is mainly influenced by factors such as block proportion, degree of welding between blocks and matrix, strength ratio between blocks and matrix, and geometric properties of blocks. Among these factors, block proportion is the most significant, and the degree of welding is a controlling factor. However, due to the complexity of Bimrock structures, there is a lack of applicable methods and mechanical models for the evaluation of geomechanical characteristics of Bimrock in engineering practice. This article also explores the influence and research methods of the geological characteristics of Bimrock in slope and tunnel engineering and, finally, provides prospects for the future research trends relating to Bimrock.