Search Results (71)

As oil and gas exploration and development advance into deep and ultra-deep areas, kick accidents are becoming more frequent during drilling operations, posing a serious threat to construction safety. Traditional kick monitoring methods are limited in their multivariate coupling modeling. These models rely too heavily on single-feature weights, making them prone to misjudgment. Therefore, this paper proposes a drilling kick risk prediction method based on image modality. First, a sliding window mechanism is used to slice key drilling parameters in time series to extract multivariate data for continuous time periods. Second, data processing is performed to construct joint logging curve image samples. Then, classical CNN models such as VGG16 and ResNet are used to train and classify image samples; finally, the performance of the model on a number of indicators is evaluated and compared with different CNN and temporal neural network models. Finally, the model’s performance is evaluated across multiple metrics and compared with CNN and time series neural network models of different structures. Experimental results show that the image-based VGG16 model outperforms typical convolutional neural network models such as AlexNet, ResNet, and EfficientNet in overall performance, and significantly outperforms LSTM and GRU time series models in classification accuracy and comprehensive discriminative power. Compared to LSTM, the recall rate increased by 23.8% and the precision increased by 5.8%, demonstrating that its convolutional structure possesses stronger perception and discriminative capabilities in extracting local spatiotemporal features and recognizing patterns, enabling more accurate identification of kick risks. Furthermore, the pre-trained VGG16 model achieved an 8.69% improvement in accuracy compared to the custom VGG16 model, fully demonstrating the effectiveness and generalization advantages of transfer learning in small-sample engineering problems and providing feasibility support for model deployment and engineering applications. Full article

It is generally believed that the ancient oceans during the “boring billion” (1.85–0.8 Ga) lacked the capacity to form large-scale iron formations (IFs), though localized small-scale IFs deposition persisted. The Altyn region of China hosts abundant IFs, with the Dimunalike IFs being the largest and most representative, characterized by typical banded iron–silica layers. Detailed fieldwork identified a tuff layer conformably contacting the IFs at the roof rocks of IFs and a ferruginous mudstone layer at the floor rocks of IFs in drill core ZK4312. Geochemical and zircon U-Pb-Hf isotopic analyses were performed. The tuff has a typical tuff structure, mostly made of quartz, and contains a significant amount of natural sulfur. It also has high SiO₂ content (77.90%–80.49%) and sulfur content (0.78%–3.06%). The ferruginous mudstone has a volcanic clastic structure and is mainly composed of quartz and chlorite, with abundant coeval pyrite. It shows lower SiO₂ content (53.83%–60.32%) and higher TFe₂O₃ content (10.29%–16.24%). Both layers share similar rare earth element (REE) distribution patterns and trace element compositions, with light REE enrichment and negative Eu, Nb, and Ti anomalies, consistent with arc volcanic geochemistry. Zircon U-Pb ages indicate crystallization of the tuff at 1102 ± 13 Ma and maximum deposition of the mudstone at 1110 ± 41 Ma. These data suggest formation during different stages of the same volcanic–sedimentary process. The ε_Hf(t) values (3.60–12.35 for tuff, 2.92–8.19 for mudstone) resemble those of Algoma-type IF host rocks, implying derivation from re-melted new crust. The Dimunalike IFs likely formed in a submarine volcanic–sedimentary environment. In conclusion, although the Mesoproterozoic ocean was generally in a low-oxygen state, which was not conducive to large-scale IF deposition, localized submarine volcanic–hydrothermal activity could still lead to IF formation. Full article

Precipitation leaches soil organic matter (SOM), transporting it downward where it accumulates at the soil–bedrock interface. Intensive agriculture, particularly tillage, accelerates this process. Microbial decomposition of SOM generates CO₂, forming a gas-rich soil layer (GRSL)—a phenomenon long hypothesized but never directly confirmed until now. Drilling on the Harbechy Plateau (Moravian Karst) revealed a GRSL with a thickness of ~0.8 m, CO₂ concentrations averaging 1.5–3 vol. % (peaks of 4–6 vol. %), and isotopic signatures (δ¹³C) indicating a mix of biogenic (−25‰) and atmospheric (−8‰) CO₂. These findings necessitate re-evaluation of carbon cycling models in karst agroecosystems. Full article

The development of shale bedding and fractures exacerbates the invasion of drilling fluid, leading to significant reservoir damage. This article elucidates the strength degradation behavior of shale with bedding orientations of 0° and 90° under drilling fluid immersion, as determined through triaxial compression experiments. An improved Hooke–Brown anisotropic strength criterion has been established to quantitatively characterize the degradation effects. Additionally, a dynamic mechanism of pore pressure accumulation was simulated. The research findings indicate the following: (1) As the intrusion pressure increases from 6 MPa to 8 MPa, the penetration depth significantly increases. In the horizontal bedding direction (0°), cracks dominate the flow mode, resulting in a sudden drop in strength; (2) An increase in bedding density or opening exacerbates the degree of invasion and strength degradation in the horizontal bedding direction, with a degradation rate exceeding 40%. In contrast, the vertical bedding direction is influenced by permeability anisotropy and crack blockage, leading to limited seepage and minimal degradation. By optimizing the dosage of emulsifiers and other treatment agents through orthogonal experiments, a low-viscosity, high-shear-strength plugging oil-based drilling fluid system was developed, effectively reducing the invasion depth of the drilling fluid by over 30%. The primary innovations of this article include the establishment of a quantitative model for Reynolds number degradation for the first time, which elucidates the mechanism of accelerated crack propagation during turbulent transition (when the Reynolds number exceeds the critical value of 10). Additionally, a novel method for synergistic control between sealing and rheology is introduced, significantly decreasing the degradation rate of horizontal bedding. Furthermore, the development of the Darcy–Forchheimer partitioning algorithm addresses the issue of prediction bias exceeding 15% in high-Reynolds-number regions (Re > 30). The research findings provide a crucial theoretical foundation and data support for the optimized design of drilling fluids. Full article

Used for soil and weathered rocks, soil nails are rigid reinforcements positioned at certain angles on the ground to provide slope stability. A rigid reinforcement element placed in a well filled with cement grout mix after completing drilling will generate adherence stress between the grout-mixed nail bar and soil. Due to this stress, load is transferred to the soil along the soil–grout interaction surface. In the case discussed herein, the slope at the parcel border needed to be made steeper in order to accommodate the construction of a facility in the Taşkısığı region of Sakarya province. Soil-nailed walls, which are inexpensive and suitable for weathered rocks, were needed as a support system because the slope was too steep to support itself. Support system performance was measured using two inclinometers and two soil nail pull-out tests conducted on different sections observed during and after construction. Contrary to the design-phase prediction, it was determined that the stresses started to dampen in the region closer to the slope-facing zone. Field measurement data and numerical analysis revealed that higher parameters than necessary were selected. In this context, sensitivity and parameter analyses were carried out using the Hoek–Brown constitutive model. The GSI value was re-evaluated and found to be compatible with the observation results obtained from the field performance. Since the retaining wall performance observed was higher than expected, geometric parametric analysis of the structural elements was performed; high safety coefficients were found across variations. The effects of the inclination of the slope, nail length, nail spacing, and nail slope design parameters on the safety coefficient and horizontal displacement were examined. The optimal design suggested nail lengths of 4.00 m, a spacing of 1.60 m, and slopes of 20°. It was discovered that the effect of the inclination degree of the slope on the safety coefficient was lower than expected. The results revealed that a more economical design with a similar safety factor can be obtained by shortening the lengths of the nails. Full article

In geoenergy science and engineering, well placement optimization is the process of determining optimal well locations and configurations to maximize economic value while considering geological, engineering, economic, and environmental constraints. This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. This study proposes a hybrid workflow for determining the locations of production wells during primary oil recovery using a multi-modal convolutional neural network (M-CNN) integrated with an evolutionary optimization algorithm. The particle swarm optimization algorithm provides the M-CNN with full-physics reservoir simulation results as learning data correlating an arbitrary well location and its cumulative oil production. The M-CNN learns the correlation between near-wellbore spatial properties (e.g., porosity, permeability, pressure, and saturation) and cumulative oil production as inputs and output, respectively. The learned M-CNN predicts oil productivity at every candidate well location and selects qualified well placement scenarios. The prediction performance of the M-CNN for hydrocarbon-prolific regions is improved by adding qualified scenarios to the learning data and re-training the M-CNN. This iterative learning scheme enhances the suitability of the proxy for solving the problem of maximizing oil productivity. The validity of the proxy is tested with a benchmark model, UNISIM-I-D, in which four oil production wells are sequentially drilled. The M-CNN approach demonstrates remarkable consistency and alignment with full-physics reservoir simulation results. It achieves prediction accuracy within a 3% relative error margin, while significantly reducing computational costs to just 11.18% of those associated with full-physics reservoir simulations. Moreover, the M-CNN-optimized well placement strategy yields a substantial 47.40% improvement in field cumulative oil production compared to the original configuration. These findings underscore the M-CNN’s effectiveness in sequential well placement optimization, striking an optimal balance between predictive accuracy and computational efficiency. The method’s ability to dramatically reduce processing time while maintaining high accuracy makes it a valuable tool for enhancing oil field productivity and streamlining reservoir management decisions. Full article

Current advances and trends in the fields of mechanical, material, and software engineering have allowed mining technology to undergo a significant transformation. Aiming to maximize the efficiency and safety of the mining process, several enabling technologies, such as the recent advances in artificial intelligence, IoT, sensor fusion, computational modeling, and advanced robotics, are being progressively adopted in mining machine manufacturing while replacing conventional parts and approaches that used to be the norm in the rock ore extraction industry. This article aims to provide an overview of research trends and state-of-the-art technologies in face exploration and drilling operations in order to define the vision toward the realization of fully autonomous mining exploration machines of the future, capable of operating without any external infrastructure. As the trend of mining at large depths is increasing and as the re-opening of abandoned mines is gaining more interest, near-to-face mining exploration approaches for identifying new ore bodies need to undergo significant revision. This article aims to contribute to future developments in the use of fully autonomous and cooperative smaller mining exploration machines. Full article

The furniture manufacturing sector faces significant challenges in machining composite materials, where quality issues such as delamination can lead to substandard products. This study aims to improve the classification of drilled holes in melamine-faced chipboard using Explainable AI (XAI) techniques to better understand and interpret Convolutional Neural Network (CNN) models’ decisions. We evaluated three CNN architectures (VGG16, VGG19, and ResNet101) pretrained on the ImageNet dataset and fine-tuned on our dataset of drilled holes. The data consisted of 8526 images, divided into three categories (Green, Yellow, Red) based on the drill’s condition. We used 5-fold cross-validation for model evaluation and applied LIME and Grad-CAM as XAI techniques to interpret the model decisions. The VGG19 model achieved the highest accuracy of 67.03% and the lowest critical error rate among the evaluated models. LIME and Grad-CAM provided complementary insights into the decision-making process of the model, emphasizing the significance of certain features and regions in the images that influenced the classifications. The integration of XAI techniques with CNN models significantly enhances the interpretability and reliability of automated systems for tool condition monitoring in the wood industry. The VGG19 model, combined with LIME and Grad-CAM, offers a robust solution for classifying drilled holes, ensuring better quality control in manufacturing processes. Full article

Investigating the seeding mechanism of precision seeders is of great significance for improving the quality of cotton sowing operations. This paper designs a rotary type-hole cotton precision mulching dibbler. The main factors influencing the entry of cotton seeds into the seed wheel holes during the seeding process are then theoretically analyzed. Following this, an accurate discrete element model of coated cotton seeds is established and combined with a discrete element method (DEM) and multi-body dynamics (MBD)-coupled simulation model of the seed drill for seed picking and planting. Simulation experiments on the seeding performance of the precision dibbler were performed to study the influence of the seed wheel structure and motion parameters on the picking and planting performance under different speeds. The optimal parameter combination for the seed wheel is obtained through optimization experiments, and a precision dibbler is manufactured for bench testing. The bench test results are consistent with the simulation test results. At the precision dibbler rotation speed of 16 r/min, the qualified index reaches a maximum value of 93.28%, the skip sowing index increases with the precision dibbler rotation speed, and the re-sowing index decreases as the speed increases. These optimization results significantly improved seeding precision and efficiency and are of great significance for the reliability and effectiveness of cotton sowing operations. Full article

Low-carbon electricity and heat production is essential for keeping the decarbonization targets and climate mitigation goals. Thus, an accurate understanding of the potential environmental impacts constitutes a key aspect not only for the reduction in greenhouse gas emissions but also for other environmental categories. Life cycle assessment allows us to conduct an overall evaluation of a given process or system through its whole lifetime across various environmental indicators. This study focused on construction, operation and maintenance, and end-of-life phases, which were analyzed based on the ReCiPe 2016 method. Within this work, authors assessed the environmental performance of one of the renewable energy sources—Enhanced Geothermal Systems, which utilize supercritical carbon dioxide as a working fluid to produce electricity and heat. Heat for the process is extracted from hot, dry rocks, typically located at depths of approximately 4–5 km, and requires appropriate stimulation to enable fluid flow. Consequently, drilling and site preparation entail significant energy and material inputs. This stage, based on conducted calculations, exhibits the highest global warming potential, with values between 5.2 and 30.1 kgCO₂eq/MWh_el, corresponding to approximately 65%, 86%, and 94% in terms of overall impacts for ecosystems, human health, and resources categories, respectively. Moreover, the study authors compared the EGS impacts for the Polish and Norwegian conditions. Obtained results indicated that due to much higher electricity output from the Norwegian plant, which is sited offshore, the environmental influence remains the lowest, at a level of 11.9 kgCO₂eq/MWh_el. Polish cases range between 38.7 and 54.1 kgCO₂eq/MWh_el of global warming potential in terms of electricity production. Regarding power generation only, the impacts in the case of the Norwegian facility are two to five times lower than for the installation in the Polish conditions. Full article

Wood borers, such as the emerald ash borer and holcocerus insularis staudinger, pose a significant threat to forest ecosystems, causing damage to trees and impacting biodiversity. This paper proposes a neural network for detecting and classifying wood borers based on their feeding vibration signals. We utilize piezoelectric ceramic sensors to collect drilling vibration signals and introduce a novel convolutional neural network (CNN) architecture named Residual Mixed Domain Attention Module Network (RMAMNet).The RMAMNet employs both channel-domain attention and time-domain attention mechanisms to enhance the network’s capability to learn meaningful features. The proposed system outperforms established networks, such as ResNet and VGG, achieving a recognition accuracy of 95.34% and an F1 score of 0.95. Our findings demonstrate that RMAMNet significantly improves the accuracy of wood borer classification, indicating its potential for effective pest monitoring and classification tasks. This study provides a new perspective and technical support for the automatic detection, classification, and early warning of wood-boring pests in forestry. Full article

Currently, in some domestic and foreign mines, the backfill drilling pipeline experiences a rupture phenomenon even when the wear degree is low. This results in a delay in production due to the filling becoming ‘sick’. This paper presents, for the first time, the damage mechanism from a mechanical perspective and re-derives the anti-extrusion strength model of the backfill drilling pipeline. We investigate the influence of the law on the anti-extrusion strength of pipelines from the perspective of strata and cement rings. We then verify the theoretical and simulation results through engineering examples. The results demonstrate that the Mises stress criterion is a suitable modification principle for the anti-extrusion strength model of the backfill drilling pipeline. The anti-extrusion strength of the pipeline is related to the elastic modulus and Poisson’s ratio of the stratum, and the thickness of the cement ring. It is negatively affected by the depth of the stratum. For hard strata, a cement ring with a smaller elastic modulus is suitable, while for soft stratum, a cement ring with a larger elastic modulus is recommended. When the missing angle of the cement ring is less than 60°, the stress concentration factor increases up to 2.2. The stress unloading capacity of the cement ring ranges from 32.7% to 37.8%, and optimal performance of the cement ring is achieved when it has high strength and low rigidity. The backfill filling pipeline of a copper mine abroad was destroyed due to external extrusion force exceeding its anti-extrusion strength value. The modified pipeline anti-extrusion strength model is 18.2% higher than the pipeline API strength value. This finding can inform the design of the backfill filling pipeline for China’s kilometer-deep wells in the future. Full article

Following the worldwide increasing demand for Critical Raw Materials (CRMs), the Hellenic Geological Survey (HSGME) implemented a national project focused on the re-evaluation of certain Public Mining Areas in Greece. In this framework, exploration activities, including geological mapping, and mineralogical, geochemical, and geophysical studies, revealed significant mineralization targets which possibly host elevated contents of certain CRMs in the Kimmeria Fe skarn deposit. The mineralization is related to the contact metamorphic aureole of the Oligocene Xanthi pluton. Various skarn minerals form the following paragenetic zones in order of decreasing temperature: (i) garnet–wollastonite, (ii) garnet–clinopyroxene, (iii) garnet–epidote, and (iv) vesuvianite–scapolite. The skarn deposit consists of magnetite-rich ore occurring along with sulfides (chalcopyrite, pyrite, bismuthinite, and molybdenite), scheelite, minor sulfosalts (aikinite, wittichenite, and cubanite) and native elements (Au and Bi). Bulk-rock geochemical analyses yielded significant values, as follows: Fe₂O₃, up to 58 wt%; Cu, up to 6.6 wt%; Bi, up to 1100 ppm; W, up to 670 ppm; V, up to 200 ppm; Mo, up to 200 ppm; and Au, up to 2.1 g/t. Soil and stream sediment geochemistry reveals spatial and linear trends for certain groups of associated elements (i.e., Fe₂O₃-Cu-Bi-W and Mo-W-Zn). These trends reflect the surficial distribution of mineralized zones and imply the existence of partially unexposed mineralization in the western part of the study area, a fact also supported by geophysical evidence. A preliminary drilling project has been proposed to evaluate the qualitative characteristics of the deeper parts of the mineralization, investigate buried ore zones in the western part, and overall, reassess the economic potential of the deposit. Full article

A geophysical method, electrical resistivity tomography, was applied to identify potential groundwater-bearing zones around Nubutautau village on Viti Levu island, Fiji. Apparent resistivity data of the subsurface were collected through an electrode assembly along survey lines by injecting current into the subsurface using an ABEM Terrameter LS2. The apparent resistivity data were inverted using Res2DINVx64 software to produce the final electrical resistivity through an iterative process to compare the resistivity of layers and draw analogical hydrogeological results. Analysis revealed the presence of two potential groundwater-bearing zones as potential targets for future drilling. The two targets indicated the presence of potentially saturated vertical fractures through which infiltrating rainwater percolates through the volcanic rock towards a deeper basal aquifer. The identification of the two potential targets demonstrated great potential of this geophysical technique to effectively inform drilling operations. A scientific approach can increase the successful delivery of water security interventions in remote, drought-prone communities of the Pacific. Full article

The Sheyang estuary is located on the northern Jiangsu muddy coast, in the western South Yellow Sea, and in the transition area between the eroded coast of the abandoned Yellow River delta and the silted coast of the central Jiangsu. This area is also one of the key areas of interactions between the paleo-Yellow River and paleo-Changjiang River during the late Quaternary. In order to investigate deeply the late Quaternary sedimentary sequence models of coasts and continental shelves under the interactions of the above two large rivers, the sedimentary sequence and age of the core NTCJ1 drilled at the Sheyang estuary were re-examined and re-interpreted recently, based on the existing data on lithology, grain size, ostracods, foraminifera, clay minerals, geochemical elements, and Electron Spin Resonance (ESR) dating, together with other adjacent key cores and shallow seismic profiles. The three new perspectives were summarized as follows: Firstly, the 22.00 m-long core NTCJ1 recorded the evolution of the sedimentary environments since Marine Isotope Stage 5 (MIS 5), and the first continental facies layer formed in MIS 4-2 is supposed to be missing; therefore, the MIS 1 marine facies layer directly overlays on the MIS 5 marine facies layer. Furthermore, the second continental facies layer formed in MIS 6 and/or the stage of the relatively low sea-level of MIS 5 has not been drilled yet. Secondarily, the middle-upper part of the NTCJ1 core sediments (0.00–17.95 m) are characterized by a finer grain, with a predominantly silty texture and dark yellow tone, and from bottom to top it shows a change from fine to coarse and then to fine in grain size, which could be substantially interpreted as the abandoned Yellow River deltaic deposits mainly formed in 1128–1855 CE, and may contain a small amount of Holocene coastal-shallow marine deposits at the bottom; however, it is difficult to identify them currently. Thirdly, the lower part of the NTCJ1 core sediments (17.95–22.00 m) have not yet been drilled through and are characterized by a coarser grain, with a predominantly fine sandy texture and dark grey tone, which could be interpreted as a delta front deposit in the MIS 5 tidal estuary and were obviously influenced by the paleo-Yellow River. Full article