Complex Geological Environments Research Articles

Experimental Measurements of Explosion Effects Propagating in the Real Geological Environment—Correlation with Small-Scale Model

This research focuses on comparing small-scale and full-scale measurements of wave propagation from explosions by using scaling relationships to find significant correlations between the two. The study investigates how seismic waves generated by explosions behave in the geological environment. The research covers various aspects such as the development of the model, the explosive materials used, measurement methods, evaluation techniques, and relevant software. A scientific approach based on the principle of backward Fourier transform was used to process and evaluate the data, which helps to filter the frequencies. One of the important calculations discussed is the determination of the attenuation coefficient, which helps to describe how waves attenuate as they pass through a material. The research also deals with dynamic scaling, using the dynamic exponent as a scaling factor to provide a better understanding of the behavior of waves at different scales. By comparing real in situ data with results from small-scale models, the study provides a robust framework for predicting the effects of explosions in complex geological environments. The research results show a high correlation coherence of the statistical data files of up to 4.1%. For dynamic tasks and model scaling, an important result can be pointed out, namely the approximately fourfold decrease in the exponents of the dependence on the distance from the excitation source and the amplitudes between P-waves (0.4316) and R-waves (0.1219). Conclusions are targeted at the possibility of correlating three types of results: small-scale simulations, numerical simulations, and a real full-scale experiment.

A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions

This study compares the effectiveness of different methods for coal thickness identification, aiming to identify the most accurate approach and provide a reference for intelligent coalmine development. Focused on the No. 2 coal seam in a mining area in Shanxi, China, the analysis employs well log-constrained impedance inversion and seismic multi-attribute techniques. The results show that the back propagation (BP) neural network model, as part of the seismic multi-attribute approach, delivers prediction accuracy comparable to the well log-constrained inversion method. Specifically, after applying proper static corrections, a four-layer BP neural network was constructed using four optimized sensitive attributes as the input layer, achieving an error range of 0.11% to 1.36%, compared to 0.03% to 6.59% for the logging-based method. The BP neural network demonstrated strong applicability in complex geological environments. Empirical analysis further validated the BP neural network’s geological reliability and practicality in systematic coal thickness determination.

Analysis of Fracture Modes and Acoustic Emission Characteristics of Low‐Frequency Disturbed Water‐Bearing Soft Rock With Different Cyclic Initial Value

ABSTRACTIn the complex geological environment of deep mining area, water‐bearing soft rock is more prone to damage and destruction by low‐frequency disturbance. In this paper, the dynamic–static combination test was conducted on the basis of uniaxial compression test by using creep dynamic disturbance impact loading system and acoustic emission technique. The test results show that with the increase of the initial value of disturbance loading, the fracture morphology of sandstone gradually changes from a single major crack to multiple cracks coexisting, and some saturated sandstones lose the bearing capacity in the process of disturbance, presenting a cone‐shaped fracture surface. The increase of the initial value of the disturbance changes the bearing capacity of the sandstone, and the peak energy of acoustic emission reaches the maximum value when the initial value of the disturbance is 80% UCS. The results of the study can provide some reference for the stability analysis of deep water‐rich soft rock mines.

Machine Learning Approach for Rock Mass Classification with Imbalanced Database of TBM Tunnelling in Himalayan Geology

AbstractThe geological condition of the Himalayan region is very complex and challenging. So far, empirical and analytical approaches for rock mass characterization have been a common practice in the Himalayas. Due to the limitations of input parameters and governing equations in design practices, rock mass characterization in tunnel boring machine (TBM) excavated tunnels is crucial. This research introduces robust machine learning (ML) approaches to predict rock mass quality conditions in complex geological environments, leveraging a large database of TBM parameters and rock mass rating (RMR) values. To do so, a total of 6879 stable phase TBM cycle data were collected from 12 km long tunnel in Nepal. The pre-processed parameters were randomly split into a training set (80%) and a testing set (20%). Seven individual classifiers consisting of logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and bagging, and stacking ensemble classifier were exploited with optimal hyperparameters. The comprehensive assessment carried out has shown that the ensemble classifier gave highest overall accuracy as compared to other individual classifiers. More importantly, the synthetic minority over-sampling technique (SMOTE) performs better to handle the imbalanced database, while the RF and stacking classifier demonstrated the best prediction performance with accuracy of 92%. Moreover, for the minority rock mass class, the RF shows better performance compared to stacking classifier. The authors emphasize that the effective application of ML-based data-driven approach shows substantial potential for rock mass characterization in TBM tunnelling.

Data-driven machine learning approaches for precise lithofacies identification in complex geological environments

ABSTRACT Reservoir characterization is a vital task within the oil and gas industry, with the identification of lithofacies in subsurface formations being a fundamental aspect of this process. However, lithofacies identification in complex geological environments with high dimensions, such as the Lower Indus Basin in Pakistan, poses a notable challenge, especially when dealing with limited data. To address this issue, we propose four common data-driven machine learning approaches: multi-resolution graph-based clustering (MRGC), artificial neural networks (ANN), K-nearest neighbors (KNN), and self-organizing map (SOM). We utilized these proposed approaches to assess their performance in scenarios with varying core sample availability, specifically evaluating their effectiveness in identifying lithofacies within the Lower Goru formation of the middle Indus Basin. The study reveals that in scenarios with a limited number of core samples, MRGC is the preferred choice, while KNN or MRGC is more suitable for larger datasets. The results demonstrate the superior performance of MRGC and KNN in lithofacies identification within the specified geological environment, with SOM following closely behind, and ANN exhibiting comparatively lower efficacy. The accurate identification of lithofacies from the selected model is complemented by the application of the truncated Gaussian simulation method for facies modeling. Comparative results confirm the excellent agreement between the model identification of lithofacies from well logs and electro-facies obtained from the truncated Gaussian simulation electro-facies volume. This study highlights the crucial role of selecting the right machine learning approach for precise lithofacies identification and modeling in complex geological environments. The comparative analysis provides practitioners in the petroleum industry with insights into the strengths and limitations of each method, enhancing existing knowledge. In conclusion, this research emphasizes the significance of comprehensive research and method selection for advancing lithofacies identification in diverse formations or study areas, ultimately benefiting the broader field of subsurface characterization in the petroleum industry.

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-to-quantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within the data but also integrates expert knowledge, achieving accurate evaluation of drilling risks.

Improving fluid identification in well logging using Continuous Wavelet Transform and Vision Transformers: An innovative approach

Well logging fluid prediction is one of the key steps in assessing oil and gas reserves. By analyzing downhole logging data, different types of fluids contained in underground rocks, such as crude oil, natural gas, and water, can be determined. This information is crucial for assessing the abundance and recoverable reserves of oil and gas resources and helps guide oil and gas exploration and development work. We have introduced a novel model called CWT (Continuous Wavelet Transform)-ViT (Vision Transformer). CWT can simultaneously provide frequency information at different scales, enabling the model to analyze downhole logging data more comprehensively and accurately at different scales. Underground rock structures often exhibit features at multiple scales, and CWT can effectively capture these features, aiding in better differentiation of different types of fluids. The ViT model utilizes the Transformer architecture, allowing for global attention over input sequences without being limited by sequence length. This enables the model to comprehensively understand the overall information of downhole logging data and extract richer features. For complex geological structures and fluid distributions in geological exploration, the global attention mechanism helps the model better grasp the overall situation, thereby improving the accuracy of fluid prediction. When we used the CWT-ViT method for well logging fluid prediction, we achieved a high accuracy rate of 97.50% in the first dataset, which further improved to 97.77% in the second dataset. These results demonstrate the significant robustness and efficiency of the CWT-ViT method in lithology prediction using well logging data. We also conducted blind well experiments, and our CWT-ViT model outperformed other models, achieving a blind well prediction accuracy of 97.36%. Therefore, the experiments indicate that the key to improving accuracy in well logging fluid prediction with CWT lies in its multiscale analysis capability, effectively capturing different fluid characteristic frequencies. Additionally, CWT enhances signal features and removes noise, increasing the precision of fluid identification. Finally, the integration with ViT further optimizes fluid prediction performance, making it outstanding in complex geological environments. The advantages of ViT in fluid prediction include its excellent sequence modeling capability, effective handling of long-distance dependencies, and enhanced ability to capture fluid characteristics in complex well logging data.

Application of Experimental Configurations of Seismic and Electric Tomographic Techniques to the Investigation of Complex Geological Structures

The main purpose of this study is the subsurface investigation of two complex geological environments focusing on the improvement of data acquisition and processing parameters regarding electric and seismic tomographic techniques. Two different study areas, in central–east Peloponnese and SE Attica, were selected, where detailed geological mapping and surface geophysical survey were carried out. The applied geophysical survey included the application of electrical resistivity tomography (ERT), seismic refraction tomography (SRT) and ground penetrating radar (GPR). The geoelectrical measurements were acquired with different arrays and electrode configurations. Moreover, various types of seismic sources were used at seventeen shot locations along the seismic arrays. For the processing of geoelectrical data, clustered datasets were created, increasing the depth of investigation and discriminatory capability. The seismic data processing included the following: (a) the creation of synthetic models and seismic records to determine the effectiveness and capabilities of the technique, (b) spectral analysis of the seismic records to determine the optimal seismic source type and (c) inversion of the field data to create representative subsurface velocity models. The results of the two techniques successfully delineated the complex subsurface structure that characterizes these two geological environments. The application of the ERT combined with the SRT are the two dominant, high-resolution techniques for the elucidation of complex subsurface structures.

Advancing Skarn Iron Ore Detection through Multispectral Image Fusion and 3D Convolutional Neural Networks (3D-CNNs)

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image-processing methodologies. Leveraging the capabilities of ASTER image, band ratio (BR) images, and principal component analysis (PCA) alongside the power of 3D convolutional neural networks (3D-CNNs), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture accepting input as a 7 × 7 × C image patch, where C represents the combined number of selected ASTER image bands, principal component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image band combinations, including the proposed band combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside ASTER bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected misclassification rates. Surprisingly, utilizing solely ASTER bands as input parameters yielded higher accuracy rates (OA = 93.13%, AA = 91.96%, kappa = 90.91%) compared with scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, kappa = 82.60%) or the integration of BR images to PC bands (OA = 87.78%, AA = 82.39%, kappa = 83.81%). However, the amalgamation of ASTER bands with selected PC bands showed slight improvements in accuracy (OA = 94.65%, AA = 92.93%, kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of ASTER bands, PC bands, and BR images (proposed band combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA = 94.87%, kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the ASTER bands, PC bands, and BR images, with the ASTER bands proving pivotal for optimal skarn classification, the PC bands enhancing intrusions classification accuracy, and the BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Evaluation of landslides susceptibility in Southeastern Tibet considering seismic sensitivity

Southeastern Tibet features a complex geological environment and a high incidence of earthquakes. Earthquake-induced chain disasters pose a great threat to engineering construction and public safety in this area, and landslides are among the most frequent postearthquake disasters. To investigate the impact of earthquakes on landslides, this study constructed a comprehensive database for landslide susceptibility analysis based on various factors, including elevation, slope, slope direction, distances from roads and rivers, proximity to faults, land use patterns, rainfall patterns, and seismic parameters. By integrating the frequency ratio (FR) model with the analytic hierarchy process (AHP) model, this work delineated landslide susceptibility zones in southeastern Tibet. Subsequently, the susceptibility zoning layer was overlaid with the magnitude sensitivity layer and validated using ROC curve analysis to identify the earthquake magnitudes that exerted the greatest influence on landslides. Finally, by incorporating the distance from earthquake epicentres into our refined model framework, different monitoring levels for landslide susceptibility zoning were established. The AHP results show the relative importance of the landslide-influencing factors in southeastern Tibet can be ranked as follows: elevation, slope direction, distance from road, land use, distance from river, slope, and rainfall. The ROC values of the landslide models with seismic sensitivities of 1, 2 and 3 are 0.876, 0.883 and 0.877, respectively, indicating that earthquakes of magnitude 4 and above have a great influence on landslides in the study area. Through the overlay of the landslide susceptibility zoning map and the vector map of distance from seismic focal points, a correlation with the distance between landslide-prone areas and seismic focal points is identified. Within the extremely high susceptibility area and within 40 km of the focal point, there are 220 landslide points, accounting for approximately 34 % of the total landslides in the study area. Additionally, 133 landslide points are located in the extremely susceptible area and within 40–80 km of the focal point, representing approximately 20 % of the total landslides in the study area. The susceptible areas were assessed based on grades, resulting in the production of 4 maps depicting different levels of monitoring for landslide-prone areas. These maps are valuable tools for implementing landslide disaster prevention measures within the study area.

Experimental and numerical investigation of water flow in different media models at multiple scales

ABSTRACT The study of the flow behavior in pores, fractures, and pore-fracture dual media plays a crucial role in understanding the mechanisms of sudden water inrush and sand flow. In this study, laboratory experiments were conducted using a custom-made transparent porous model to investigate the flow characteristics of fluids in complex media. Additionally, finite element simulations were employed to explore the flow features at multiple scales. A detailed analysis at the micrometer scale was conducted to examine the variations in the water velocity, volumetric flow rate, Reynolds number, flow field, and permeability under different porosity and pressure drop conditions. The results indicate that within the studied range of flow velocities and pressures, the fluid flow in all the media models adhered to Darcy’s law. The improvement in structural connectivity in the random pore model has made the flow channels of fluids in the random pore network more diverse, resulting in an increase in the peak water flow velocity from 4.89E–5 m/s to 4.19E–4 m/s. The fluid accelerated while bypassing the protruding solid particles, resulting in the formation of high-velocity zones in the vicinity. As the pressure drop increases, the volume flow rate of the random dual-medium model with a porosity of 0.6 can increase to 1.01E–9 m3/s, with the highest growth rate. We also simulate the fluid flow of a regular dual-medium model at different porosities and find that the permeability rapidly increases from 5.65E–13 m2 to 1.01E–11 m2, demonstrating that the presence of fractures can significantly improve the permeability performance of the medium. The research findings deepen our understanding of the migration behavior of water in complex geological environments and provide theoretical guidance for groundwater resource protection and underground engineering.

Intelligent Identification of Surrounding Rock Grades Based on a Self-Developed Rock Drilling Test System

The classification of surrounding rock is crucial for formulating safe tunnel construction plans and support measures. However, the complex geological environment of tunnels presents a challenge in obtaining accurate drilling parameters for rock mass classification. This paper presents the development of a rock drilling testing system, which includes a propulsion speed acquisition system, oil pressure acquisition system, air pressure acquisition system, and an automatic data acquisition system. This system enables real-time, high-precision automatic collection and storage of parameters such as propulsion speed, with data collected twice per second for each parameter. Leveraging the Qingdao Metro Line 6 as a case study, we conducted rock mass drilling and constructed a rock mass classification database. By employing kernel density estimation and Pearson correlation analysis, we quantified the correlation between rock mass classification and the drilling parameters. The results indicated that relying on a single drilling parameter is insufficient for accurately determining rock mass classification. Both impact pressure and rotational pressure showed the strongest correlation with rock mass classification, each with a correlation coefficient below −0.8 (indicating a strong negative correlation). Outlier values of drilling parameters were excluded using the interval method. Based on the remaining data, we established an intelligent rock mass classification model using the random forest algorithm. This model demonstrated good accuracy and generalization performance, with an average accuracy exceeding 0.9. The proposed rock drilling testing system, combined with the intelligent rock mass classification model, forms an integrated system for the intelligent identification of rock mass grades. This system has significant implications for the intelligent and safe construction of drill-and-blast tunnels.

An Effective Barrier Coating Technology Against Premature Bolt Failures in Underground Mines

AbstractSignificant safety and economic consequences accompany the premature failure of bolts, posing sustainability challenges for mining operations. Previous studies have indicated that hydrogen-induced stress corrosion cracking (HISCC), primarily influenced by microbial activities termed microbiologically influenced stress corrosion cracking (MISCC), stands as a major contributor to the premature failure of bolts in underground mines. Presently, an effective mechanism to mitigate these premature failures is lacking. In this study, multiple commercially available coatings undergo testing to assess their susceptibility and suitability in preventing HISCC and MISCC. Additionally, a purpose-developed coating is examined. The results reveal that the tested commercially available coatings either fail to prevent these types of corrosion or are unsuitable for the intricate conditions within underground mines. The laboratory results show the coating has a significant anti-acidic corrosion and anti-MISCC performance. Conversely, the coating formulated in this study successfully averts both MISCC and HISCC, proving its applicability within the complex geological environments prevalent in mines. This breakthrough offers a promising solution to mitigate premature bolt failures in complex underground geological environments. The developed coating presents a viable way forward for enhancing safety, reducing economic losses, and improving the overall sustainability of mining operations.

Research on Failure Modes and Causes of 100-m-High Core Wall Rockfill Dams

Rockfill dams are the most competitive type of dam in complex geological environments. Identifying the failure modes and causes in high dams over 100 m is critical for better guiding high dam designs and implementing safety prevention and control measures. To this end, this paper investigated numerous cases of earth–rock dam breaches and failure modes in rockfill dams globally, with a particular focus on dams over 100 m in height, encompassing all such dams in China. The study categorized dam failure modes based on whether the dams were built before or after 1980. It also examined the causes of dam failures in terms of dam height, foundation characteristics and thickness, and failure time. Additionally, the paper analyzed a rockfill dam in China, with a height of 136 m and over ten years of operation, as a case study. We analyzed the spatial and temporal characteristics and causes of failures, such as dam crest cracking, high-level seepage, and gallery cracking, using the design situation, monitoring data, and numerical simulation. The paper also addressed issues related to dam design and foundation treatment, providing recommendations for improvement. The study indicated that the overall risk of total failure for dams over 100 m is already low. However, longitudinal cracks on the dam crest, core wall seepage, hydraulic splitting, and seepage damage to the dam foundation are primary issues in the current high core wall rockfill dams. These issues are mainly caused by uneven structural deformation of the dam and its foundation. A reasonable design of rockfill materials and foundations can mitigate these failures.

Chaotic behavior of geophysical logs for stratigraphic hiatuses: A case study from Upper Assam Shelf, India

We propose a novel fractal analysis-assisted approach for stratigraphic characterization to understand the in-situ subsurface geology of hydrocarbon reservoirs. By employing correlation dimension and multifractal detrended fluctuation analysis, we quantify the self-similarity and long-range dependencies in the geophysical logs, reflecting the unique sedimentation processes inherent in the stratigraphic formations of the Bhogpara oil field in the Upper Assam basin. The fractal properties of the logs associated with various stratigraphic units are used to establish a fractal-based stratigraphic signature. The hierarchy of the studied formations, based on the correlation dimension ranging from 1.98 to 1.13, is follows as: Girujan > Tipam > Barail > Kopili > Prang > Narpuh > Lakadong-Therria. Furthermore, the spectrum width values, was found to range from 0.70 to 2.04, reflecting the spatial correlation, geological complexity, and heterogeneity inherent in each formation as: Kopili > Prang > Narpuh > Barail > Girujan > Tipam > Lakadong-Therria. This method identifies common fractal patterns between wellbores and aids in determining the spatial continuity of stratigraphic units. Our approach effectively detects lateral variations in subsurface formations, linking well log fractality with depositional patterns through various transitional environments, such as lacustrine to shallow marine to fluvial, in presence of different fluids. The petroliferous formation exhibits lowest fractal dimension and weak multifractality. The research utilizes fractal analysis to improve stratigraphic correlation, enhancing subsurface characterization and reservoir delineation, particularly in complex geological environments, and enhancing reliability.

Research on the mechanical model of anchorage resistance in deeply filled jointed rock masses

The calculation model of anchorage resistance is the core issue in the study of anchorage theory in jointed rock masses. Due to the complex geological environment of deep rock masses, the anchorage mechanism of soft rock filled in joints is even more complicated and has not yet been clearly defined. Based on the theory of structural mechanics, considering the restraining stress in the plastic extrusion deformation zone of the bolt as a rectangular distribution pattern is more in line with the interaction state between the bolt and the surrounding medium when the rod is at its limit load. Taking into account the influence of the joint filling part, the value of restraining stress is revised to include a formula expression that factors in the strength of the filling material(σcj) and the degree of filling(Δ).Develop a mechanical model of anchorage resistance for filled joint soft rock considering large deformation conditions due to turning angles. Further, conduct shear tests on anchored filled joint rock bodies under various σcj−Δ combination patterns with constant normal stiffness (CNS) boundary conditions to validate and analyze the model. The research results indicate that under the influence of deep rock mass mechanical boundaries, the ratio of the shear deformation section length to the bolt diameter differs from existing research results, with specimens l/D under different σcj−Δ combinations typically ranging between 0.7 and 1.5 times.The displacement of the bolt at turning angle θ increases with the increase of Δ and decreases with the decrease of σcj, where Δ is the main factor affecting the degree of tensile-bending deformation of the bolt. As Δ increases, the calculated contribution rates of bolt shear resistance under three different σcj conditions increase from 9.8% to 34.8%, 40.3%, and 39.4%, respectively. This indicates that an increase in Δ can reduce the increment of normal stress, further decrease the overall stress and compressive deformation zone’s normal constraint stress in the rock mass, leading to a gradual increase in the degree of tensile-bending deformation of the bolt and an increase in resistance contribution.For the specimens with three different values of σcj, the ratio of shear force to axial force providing resistance on the joint surface decreased from a maximum of 2.83 to 0.72, 1.04, and 2.01, respectively. This indicates that when σcj is relatively large, tensile-bending deformation is more likely to occur, mobilizing the axial force of the bolt to provide the main resistance. Further systematic analysis was conducted on the length of the shear deformation section of the bolt and the distribution characteristics of the anchor resistance under condition β=30°∼90°. The reasonableness of the model was verified through the comparison of model calculation results with experimental data. Finally, a series of recommendations were proposed for preventing failure of the anchored system in filled jointed rock masses in actual engineering projects.

Advanced AI approach for enhanced predictive modeling in reservoir characterization within complex geological environments

Advanced AI approach for enhanced predictive modeling in reservoir characterization within complex geological environments

ANALYSIS OF THE EFFICIENT COMPOSITION OF DRILLING FLUIDS USED IN WELLS DRILLED IN THE ABSHERON AND BAKU ARCHIPELAGOS

The article shows that the process of drilling oil and gas wells in the Absheron and Baku archipelagos is often accompanied by a number of complications. This is due to the physicochemical and lithological characteristics of the rocks in the geological section of the territory, as well as hydrogeological conditions, tectonics, and a number of other factors. A key feature of this area is the sharp change in reservoir pressure gradients along the section, along with the presence of abnormally high pore pressures in thick clayey layers. These conditions necessitate the use of carefully formulated drilling fluids to manage wellbore stability and minimize operational risks. In this study, the efficient composition of drilling fluids used in these wells is analyzed to address the challenges posed by the unique geological and geophysical conditions of the region. The investigation includes a detailed examination of the rheological properties, density, and filtration characteristics of various drilling fluid formulations. Laboratory tests and field data were utilized to determine the most effective drilling fluid composition. The aim is to identify an optimal composition that enhances drilling performance, mitigates the risks of wellbore instability, and ensures safe and effective drilling operations. The findings contribute to improving the efficiency and safety of drilling activities in the Absheron and Baku archipelagos. This research provides valuable insights into the development of customized drilling fluid solutions, highlighting the importance of adapting fluid formulations to specific geological settings. Additionally, the study emphasizes the economic benefits of optimizing drilling fluid composition, such as reducing non-productive time and lowering overall drilling costs. These insights are crucial for future exploration and production endeavors, potentially enhancing the success rate of drilling operations in similar complex geological environments. Keywords: Drilling complications, Physicochemical characteristics, Lithological characteristics, Hydrogeological conditions, Tectonics, Reservoir pressure gradients, Economic benefits, Non-productive time, Drilling costs.

Experimental study on the damage and fracture mechanism of deep coal beds impacted by water jets at different temperatures

The development of abundant deep coalbed methane (CBM) resources has attracted significant attention due to their commercial development potential. However, developing such resources presents many challenges due to their generation in complex geological environments governed by high temperatures and stresses. Thus, this work investigated coal fragmentation using water jets at different reservoir temperatures. The results show that compared with room temperature conditions, under water jet impact, the threshold fracturing pressure of high-temperature coal is lower, the rock-breaking volume is larger, and thermal stress leads to intensified dynamic damage. Indeed, when the temperature exceeds 75.0 °C, the coal undergoes volume fragmentation, and the depth of water jet-breaking coal increases by 81.0 %, while the specific energy consumption decreases by 52.4 %. The threshold fracturing pressure rapidly decreases as temperature increases and stabilizes, presenting an exponential trend. When the temperature exceeds 75.0 °C, the threshold fracturing pressure is maintained at around 10.0 MPa and no longer decreases with the temperature increase. In addition, three-dimensional (3D) damage field analysis highlights that the coal breaking mode shifts from a “water wedge” dominated mode to a “water wedge-thermal fracture” mode as temperature increases. Finally, the dynamic fracturing mechanism of coal under high-temperature conditions of water jet impact has been revealed. The research results of this paper provide a theoretical basis for the hydraulic permeability enhancement design of deep coal seams.

Dynamic compressive characteristics of a green sandstone under coupled hydraulic-mechanical loading: Experiments and theoretical modeling

Deep rock is under a complex geological environment with high geo-stress, high pore pressure, and strong dynamic disturbance. Understanding the dynamic response of rocks under coupled hydraulic-mechanical loading is thus essential in evaluating the stability and safety of subterranean engineering structures. Nevertheless, the constraints in experimental techniques have led to limited prior investigations into the dynamic compression behavior of rocks subjected to simultaneous high in-situ stress and pore pressure conditions. This study utilizes a triaxial split Hopkinson pressure bar (SHPB) system in conjunction with a pore pressure loading cell to conduct dynamic experiments on rocks subjected to hydraulic-mechanical loading. A porous green sandstone (GS) was adopted as the testing rock material. The findings reveal that the dynamic behavior of rock specimens is significantly influenced by multiple factors, including the loading rate, confining stress, and pore pressure. Specifically, the dynamic compressive strength of GS exhibits an increase with higher loading rates and greater confining pressures, while it decreases with elevated pore pressure. Moreover, the classical Ashby-Sammis micromechanical model was augmented to account for dynamic loading and pore pressure considerations. By deducing the connection between crack length and damage evolution, the resulting law of crack expansion rate is related to the strain rate. In addition, the influence of hydraulic factors on the stress intensity factor at the crack tip is introduced. Thereby, a dynamic constitutive model for deep rocks under coupled hydraulic-mechanical loading was established and then validated against the experimental results. Subsequently, the characteristics of introduced parameter for quantifying the water-induced effects were carefully discussed.