Geomechanics and Geophysics for Geo-Energy and Geo-Resources最新文献

Over the past few years, the use of machine learning has gained considerable momentum in many industries, including exploration seismic. While supervised machine learning is increasingly being used in seismic data analysis, some obstacles hinder its widespread application. Seismic facies classification—a crucial aspect in this field—particularly faces challenges such as the selection of appropriate input attributes. Plethora of seismic attributes have been created over the years, and new ones are still coming out. Yet, several have been deemed redundant or geologically meaningless. In the context of machine learning, it is crucial to avoid these redundant and irrelevant attributes as they can result in overfitting, building unnecessary complex models, and prolonging computational time. The current study incorporates an attribute selection approach to seismic facies classification and evaluates the importance of several available seismic attributes. Two datasets from the AN Field and the Dangerous Grounds region offshore Malaysia were utilized. Several attribute selection techniques were evaluated, with most of them yielding perfect attribute subsets for the AN dataset. However, only the wrapper and embedded methods could produce optimal subsets for the more complex Dangerous Grounds dataset. In both datasets, distinguishing the targeted seismic facies was mainly dependent on amplitude, spectral, and gray-level co-occurrence matrix attributes. Furthermore, spectral magnitude components played a significant role in classifying the facies of the Dangerous Grounds broadband data. The study demonstrated the importance of attribute selection, established a workflow, and identified significant attributes that could enhance seismic facies classification in Malaysian basins and similar geologic settings.

Understanding the impact of diagenesis on the elastic properties of organic-rich shale reservoirs is essential for evaluating unconventional hydrocarbon reservoirs and interpreting seismic data. Recent advancements in the exploration of the Permian Wujiaping Formation in the eastern Sichuan Basin indicate its potential to become a significant succession within the Sichuan Basin. However, the effect of different lithofacies in the Wujiaping Formation on shale elastic properties under varying diagenetic conditions remains unclear, hindering detailed reservoir interpretation. This study employs X-ray diffraction, thin section analysis, scanning electron microscopy, organic geochemistry, and dynamic elastic property tests to investigate the Wujiaping Formation shale. The results reveal three primary lithofacies types: argillaceous shale, mixed shale, and siliceous shale. Argillaceous shale, subjected to intense compaction, forms a dense rock framework of oriented clay minerals, characterized by low porosity (1.66%), low elastic wave velocity (4122.30 m/s), low elastic modulus (2174.59 m/s), and high Poisson's ratio (32.24 GPa). Mixed shale, dominated by carbonates and quartz, exhibits a rock framework formed through dissolution and cementation, with high elastic wave velocity (5196.54 m/s), relatively high elastic modulus (2975.86 m/s), and moderate Poisson's ratio (58.53 GPa). Siliceous shale, comprising biogenic quartz particles, shows strong resistance to compaction. During hydrocarbon generation, it develops abundant organic matter pores, resulting in the highest porosity (2.36%), high elastic wave velocity (5177.92 m/s), high elastic modulus (2975.86 m/s), and low Poisson's ratio (62.23 GPa). The significant differences in mineral composition and diagenetic processes across the lithofacies lead to distinct elastic properties. This study provides a rock physics framework for the detailed seismic prediction of "sweet spots" in the Wujiaping Formation shale reservoirs and offers new insights into characterizing the diagenesis of unconventional shale reservoirs using geophysical properties.

The complicated geological environment of deep rocks poses new challenges to tunnel and mining engineering. Some thorny disasters such as large deformation of soft rock and rockburst are becoming more and more prominent. However, the classic tunnelling methods represented by the mine tunnelling method and the new Austrian tunnelling method are generally unsatisfactory in addressing these issues due to the limited self-stability of surrounding rock mass. Therefore, the excavation compensation method (ECM) with the core of active stress compensation has been proposed and applied in practical engineering construction to solve the above problems. After extensive engineering practice, the theoretical foundation, key technologies, and construction system of ECM have been established and improved. This article provides a comprehensive overview of this novel tunnelling method. In addition, its controlling effects on surrounding rock are demonstrated by two typical engineering examples. It could provide some new ideas and references for the development of future tunnelling technology.

The unified strength theory with the two-piecewise linear equations is more convenient and concise to calculate the strength of materials. It can fully explore the potential in the strength of materials and improve the economic benefits of engineering design. This study combines the semi-implicit return mapping algorithm and the Aitken accelerated iteration scheme and develops a plastic constitutive algorithm for isotropic softening materials based on the unified strength theory. The combining method can simplify the stress update and make the calculation of consistent tangent modulus easier. Furthermore, it can avoid solving the partial derivatives of the plastic flow rule and overcome the stress-deviating problem. The self-developed constitutive algorithm is used to simulate the elastic–plastic excavation process of a deep-lying circular tunnel. The numerical simulation results match well with the theoretical solution, verifying the correctness of the self-developed constitutive algorithm. Based on the self-developed constitutive algorithm, the stability of an underground mining stope is comprehensively analyzed, and its structural parameters are optimized. The research reveals the mechanism of stope instability, provides a reliable scientific basis for the mining design and decision-making, ensures the safe and efficient production of the stope, and achieves the expected goal.

A self-made triaxial testing machine with thermal–hydraulic–mechanical–chemical (THMC) coupling and a tubular heating furnace, combined with in situ (IS) micro-computed-tomography technology was utilized in this study. The evolution of pore-fissure (PF) structure parameters (porosity, PF scale distribution, effective PF volume ratio, and permeability) of bituminous coal under stress-free (SF) and IS conditions with temperature was investigated, and then the mechanism of experimental results was analyzed. Results showed that (1) under SF conditions, at 300–550 °C, the coal samples after pyrolysis are dominated by elongated large fissures, with PF structure parameters positively correlating with temperature. After 400 °C, the number of PFs increases, with most PFs having equivalent diameter (R) ≤ 100 μm. (2) Under IS conditions, coal sample fissures are dominated by elongated large fissures at 300–350 °C and by holes at 350–600 °C. (3) Under IS conditions at 300–600 °C, the PF structure parameters of coal samples initially decrease with temperature and subsequently increase. The number of PFs fluctuates within a certain range, and the PF scale distribution dynamically shifts with temperature. (4) After 300 °C, the PF structure parameters of bituminous coal under SF and IS conditions show a bipolar distribution with temperature. Therefore, the weakening effect of stress on the PF structure of coal samples should not be overlooked during IS pyrolysis mining of coal bodies.

Revealing the influence of confining pressure on the propagation and formation mechanism of rock cracks under particle impact is significant to deep rock excavation. In this study, the three-dimensional fracture reconstruction of the rock after particle impact was carried out by CT scanning, and the stress and crack field evolution of the rock under particle impact were analyzed by PFC2D discrete element numerical simulation. The results demonstrate that after particles impact, a fracture zone and intergranular main crack propagation zone are formed in the rock. The shear stress and tensile stress caused by compressive stress are the main reasons for the formation of the fracture zone, while the formation of the intergranular main crack propagation zone is mainly due to tangential derived tensile stress. The confining pressure induces prestress between rock particles such that the derived tensile stress needs to overcome the initial compressive stress between the particles to form tensile fractures. And the increase in the confining pressure leads to increases in the proportion of shear cracks and friction effects between rock particles, resulting in an increase in energy consumption for the same number of cracks. From a macroscopic perspective, the confining pressure can effectively inhibit the generation of cracks.

The Hoek–Brown (H–B) criterion has found widespread application in numerous rock engineering projects. However, its efficacy is compromised by an underestimation of rock strength due to its neglect of the influence of the intermediate principal stress ((sigma_{2})). Experimental evidence underscores the significant impact of (sigma_{2}). Consequently, there exists an imperative to formulate a three-dimensional (3D) criterion. In this study, a new deviatoric function with two additional parameters ((k) and (A)) is developed firstly, which ensure compliance with the prerequisites of smoothness and convexity. In addition, the parameters (k) and (A) are bonded with the weakening effect of the Lode angle ((theta_{sigma })) and the strengthening effect of the mean stress ((sigma_{m})), respectively. Then a new 3D strength criterion for rocks is proposed by combining this new deviatoric function and the triaxial compression meridian function of the original H–B criterion. Four distinct sets of test data encompassing various rock types are employed to validate the proposed criterion. The results demonstrate that the proposed criterion adeptly captures the strength characteristics of the four rock types, providing a good depiction of failure surfaces within the 3D principal stress space. Comparative analyses involve the utilization of several existing 3D H–B criteria for strength predictions. The proposed criterion exhibits superior fitting performance for all the selected rocks.

This review paper provides a critical examination of underground hydrogen storage (UHS) as a viable solution for large-scale energy storage, surpassing 10 GWh capacities, and contrasts it with aboveground methods. It exploes into the challenges posed by hydrogen injection, such as the potential for hydrogen loss and alterations in the petrophysical and petrographic characteristics of rock structures, which could compromise the efficiency of UHS systems. Central to our analysis is a detailed overview of hydrogen solubility across various solvents, an extensive database of potential mineralogical reactions within underground storage environments, and their implications for hydrogen retention. We particularly focus on the effects of these reactions on the porosity of reservoir and cap rocks, the role of diffusion in hydrogen loss, and the consequences of multiphase flow induced by hydrogen injection. Our findings highlight the critical mineralogical reactions—specifically, goethite reduction and calcite dissolution—and their pronounced impact on increasing cap rock porosity. We underscore a notable discovery: hydrogen's solubility in non-aqueous phases is significantly higher than in aqueous phases, nearly an order of magnitude greater. The paper not only presents quantitative insights into the mechanisms of hydrogen loss but also pinpoints areas in need of further research to deepen our understanding of UHS dynamics. By identifying these research gaps, we aim to guide future studies towards enhancing the operational efficiency and safety of UHS facilities, thereby supporting the transition towards sustainable energy systems. This work is pivotal for industry stakeholders seeking to optimize UHS practices, ensuring both the effective utilization of hydrogen as a clean energy carrier and the advancement of global sustainable energy goals.

During the process of rock failure, the characteristics of crack propagation affect the fracture characteristics and macroscopic mechanical behavior of rocks, indirectly affecting the safety and stability of rock engineering. In order to study the evolution characteristics of cracks during rock failure under different lateral pressure, based on an improved digital image correlation (DIC) and acoustic emission (AE) signal recognition method, a visual biaxial servo loading device was developed to conduct biaxial compression tests on mudstone with prefabricated cracks of the same inclination angle. The research results indicate that the stages of crack propagation include microcracks propagation, crack tip formation, stable macroscopic cracks propagation, and unstable macroscopic cracks propagation. As the lateral pressure increased, the initiation frequency of cracks decreased, the quantity of propagation decreased, and the propagation path shortened, indirectly increasing the bearing strength of rocks. The initiation stress, peak stress, and elastic modulus of pre-cracked rocks with lateral pressure ≤ 2 MPa were lower than those of pre-cracked rocks with lateral pressure > 3 MPa, with the minimum reduction amplitude of 14.1%, 21.2%, and 12.6%, respectively. As the lateral pressure decreased, the dispersion of the AE main frequency distribution increased and accelerated its downward expansion. The surface temperature curves of rocks were prone to fluctuations and rapid upward evolution characteristics corresponding to crack tip formation and crack propagation, respectively. The research results provide theoretical and engineering references for the mining of weak coal seams.

The oil and gas industry relies on accurately predicting profitable clusters in subsurface formations for geophysical reservoir analysis. It is challenging to predict payable clusters in complicated geological settings like the Lower Indus Basin, Pakistan. In complex, high-dimensional heterogeneous geological settings, traditional statistical methods seldom provide correct results. Therefore, this paper introduces a robust unsupervised AI strategy designed to identify and classify profitable zones using self-organizing maps (SOM) and K-means clustering techniques. Results of SOM and K-means clustering provided the reservoir potentials of six depositional facies types (MBSD, DCSD, MBSMD, SSiCL, SMDFM, MBSh) based on cluster distributions. The depositional facies MBSD and DCSD exhibited high similarity and achieved a maximum effective porosity (PHIE) value of ≥ 15%, indicating good reservoir rock typing (RRT) features. The density-based spatial clustering of applications with noise (DBSCAN) showed minimum outliers through meta cluster attributes and confirmed the reliability of the generated cluster results. Shapley Additive Explanations (SHAP) model identified PHIE as the most significant parameter and was beneficial in identifying payable and non-payable clustering zones. Additionally, this strategy highlights the importance of unsupervised AI in managing profitable cluster distribution across various geological formations, going beyond simple reservoir characterization.