Search Results (135)

Hydraulic fracturing is a crucial technology for developing unconventional oil and gas resources. However, conventional geophysical methods struggle to efficiently and accurately image proppant-connected channels created by hydraulic fracturing. The borehole-to-surface electromagnetic imaging method (BSEM) overcomes this limitation by utilizing a controlled cased well source. Placing the source close to the target reservoir and deploying multi-component receivers on the surface enable high-precision lateral monitoring, providing an effective approach for dynamic monitoring of hydraulic fracturing operations. This study focuses on key aspects of forward modeling for BSEM. A three-dimensional finite-volume method based on the Yee grid was used to simulate the borehole-to-surface electromagnetic system incorporating metal casings, validating the method of simulating metal casing using multiple line sources. The simulation of the observation system and the frequency-domain electromagnetic monitoring simulation based on actual well data confirm BSEM’s high sensitivity for monitoring deep subsurface formations. Critically, well casing exerts a substantial influence on surface electromagnetic responses, while the electromagnetic contribution from line sources emulating perforation zones necessitates explicit incorporation within data processing workflows. Full article

In the drilling process of ultra-deep wells with large-diameter boreholes, the transport and deposition behavior of cuttings plays a critical role in maintaining wellbore cleanliness and ensuring operational safety. Due to the geometry of enlarged boreholes and their complex annular flow characteristics, conventional single-parameter control methods often fail to achieve effective cuttings transport. This study aims to identify the dominant influencing factors and optimize key parameters by focusing on the cuttings volume fraction as a primary evaluation metric. A numerical simulation approach is employed to systematically investigate the influence of stabilizer geometry and hydraulic parameters. Five variables—drilling fluid velocity, drill pipe rotational speed, number of stabilizers, flow area, and helical angle—are selected for analysis. An initial one-factor sensitivity analysis is conducted to evaluate local impacts and to establish relative sensitivity indices, thereby identifying key variables. A variance-based global sensitivity analysis is further applied to quantify first-order effects, full-order effects, and interaction contributions, revealing nonlinear coupling and synergistic mechanisms. The results indicate that drilling fluid velocity and rotation speed exhibit the most significant first-order influences, while stabilizer-related parameters show strong interaction effects that are often underestimated by traditional methods. Based on these findings, an optimized cuttings transport scheme for large-diameter boreholes is proposed. Additionally, a multi-parameter response model for the cuttings volume fraction is developed using sensitivity-weighted analysis, offering theoretical support and methodological reference for enhancing cuttings transport performance and structural design in large-diameter borehole drilling operations. Full article

Medium-deep borehole heat exchanger (BHE) systems represent an emerging form of ground source heat pump technology. Their heat transfer process is significantly influenced by geothermal gradient and soil stratification, typically simulated using segmented finite line source (SFLS) models. However, this approach involves computationally intensive procedures that hinder practical engineering implementation. Building upon an SFLS model adapted for complex geological conditions, this study proposes a comprehensive simplified algorithm: (1) For soil stratification: A geothermally-weighted thermal conductivity method converts layered heterogeneous media into an equivalent homogeneous medium; (2) For geothermal gradient: A temperature correction method establishes fluid temperatures under geothermal gradient by superimposing correction terms onto uniform-temperature model results (g-function model). Validated through two engineering case studies, this integrated algorithm provides a straightforward technical tool for heat transfer calculations in BHE systems. Full article

To address the low drilling efficiency of the composite rod drill in gas extraction boreholes, key drilling parameters are optimized using coal-seam hardness grading tests and response surface methodology. By conducting mechanical tests on coal samples from the Sangshuping, Zhangcun, and Wangzhuang coal mines, the coal seams are classified into three categories: soft (Pus coefficient 0.87), medium–hard (2.16), and hard (3.47). Multi-factor and multi-level field tests were then performed at different working faces, using Design Expert software to analyze the response surface of three factors: pump pressure, flow rate, and feed pressure. The response surface method was used to determine the influence of drilling factors on drilling time under different coal-seam hardness conditions and the optimal drilling parameters. The results indicate that the technology is not suitable for soft coal seams due to frequent bit jamming. The optimal parameters for medium–hard coal seams are a pump pressure of 4 MPa, a flow rate of 180 L/min, and a feed pressure of 6 MPa (time per 100 m: 62 min 33 s). For hard coal seams, the optimal parameters are a pump pressure of 6 MPa, a flow rate of 200 L/min, and a feed pressure of 8 MPa (time per 100 m: 55 min 27 s). This study provides a theoretical basis for efficient coal seam drilling. Full article

Groundwater resources are changing under the current climate change trajectory. Mitigation and adaptation measures include understanding the inter-working relationships among all climate variables and water resources, specifically groundwater, since it has less direct impacts than surface waters due to its nature. The Breede Gouritz Water Management Area provides an interesting platform to assess these interdependencies, since they have not been assessed before. To assess any underlying dependencies, a multivariate analysis of independent variables including monthly average temperature, summative precipitation, and average evapotranspiration, and a dependent monthly variable, i.e., average groundwater depth, from 14 boreholes was conducted. Moreover, a groundwater depth near-future prediction for each relevant borehole was made. The Multiple Linear Regression model was chosen as the appropriate one since it is cost- and time-effective, entry-level, easy to interpret, and provides a simple and basic understanding of the relationship dependencies. The Kruskal-Wallis test was also performed to elaborate on findings from the Multiple Linear Regression models. Simple linear models incorporating independent and dependent variables can only account for up to 41.7% of the variation in groundwater depth. Groundwater depth is mainly influenced by temperature and evapotranspiration and is expected to be lower for ten dependent variables. The more arid regions in the study area can expect groundwater depth to lower soon and need to use alternative water resources. The temperate west of the study area could expect more favorable outcomes regarding groundwater depth in the near future. Incorporating more variables and using a multi-modal approach to combat non-linear relationships is recommended in future. Full article

To address the challenges in identifying NAPL contamination within low-permeability clay sites, this study innovatively integrates high-density electrical resistivity tomography (ERT) with a UNet deep learning model to establish an intelligent contamination detection system. Taking an industrial site in Shanghai as the research object, we collected apparent resistivity data using the WGMD-9 system, obtained resistivity profiles through inversion imaging, and constructed training sets by generating contamination labels via K-means clustering. A semantic segmentation model with skip connections and multi-scale feature fusion was developed based on the UNet architecture to achieve automatic identification of contaminated areas. Experimental results demonstrate that the model achieves a mean Intersection over Union (mIoU) of 86.58%, an accuracy (Acc) of 99.42%, a precision (Pre) of 75.72%, a recall (Rec) of 76.80%, and an F1 score (f1) of 76.23%, effectively overcoming the noise interference in electrical anomaly interpretation through conventional geophysical methods in low-permeability clay, while outperforming DeepLabV3, DeepLabV3+, PSPNet, and LinkNet models. Time-lapse resistivity imaging verifies the feasibility of dynamic monitoring for contaminant migration, while the integration of the VGG-16 encoder and hyperparameter optimization (learning rate of 0.0001 and batch size of 8) significantly enhances model performance. Case visualization reveals high consistency between segmentation results and actual contamination distribution, enabling precise localization of spatial morphology for contamination plumes. This technological breakthrough overcomes the high-cost and low-efficiency limitations of traditional borehole sampling, providing a high-precision, non-destructive intelligent detection solution for contaminated site remediation. Full article

This study presents a probabilistic geotechnical analysis of the Visonta Keleti-III lignite mining area, focusing on the statistical evaluation of soil parameters and their integration into slope stability modeling. The objective was to provide a more accurate representation of the spatial variability of geological formations and mechanical soil properties in contrast to traditional deterministic approaches. The analysis was based on over 3300 laboratory samples from 28 boreholes, processed through multi-stage outlier filtering and regression techniques. Strong correlations were identified between physical soil parameters—such as wet and dry bulk density, void ratio, and plasticity index—particularly in cohesive soils. The probabilistic slope stability analysis applied the Bishop simplified method in combination with Latin Hypercube simulation. Results demonstrate that traditional methods tend to underestimate slope failure risk, whereas the probabilistic approach reveals failure probabilities ranging from 0% to 46.7% across different sections. The use of tailored statistical tools—such as Python-based filtering algorithms and distribution fitting via MATLAB—enabled more realistic modeling of geotechnical behavior. The findings emphasize the necessity of statistical methodologies in mine design, particularly in geologically heterogeneous, multilayered environments, where spatial uncertainty plays a critical role in slope stability assessments. Full article

With the intensive development of oil and gas resources leading to a rapid increase in abandoned wells, sealing failures may cause oil and gas leakage and environmental pollution. Systematically investigating the temperature distribution patterns of thermite melting in open-hole abandoned wells under various factors is critical for effective plugging. This study overcomes the limitations of traditional single heat conduction models by integrating thermite reaction kinetics, phase change latent heat, and thermal–fluid–solid multi-field coupling effects, establishing a thermal–fluid–solid coupling model for thermite melting in open-hole abandoned wells. This model provides theoretical guidance for the effectiveness of plugging operations and temperature control during operations. The model was validated through thermite melting experiments: the simulated expansion of the sandstone borehole diameter was 9.8 mm, with a 5.5% error compared to the experimental value of 9.29 mm; and the simulated axial extension at the well bottom was 18.9 mm, with a 4.7% error compared to the experimental value of 17.19 mm, confirming the model’s accuracy. The influence of different lithologies and initial downhole temperatures on the temperature distribution in the open-hole section of abandoned wells under identical conditions was analyzed. The results show that the ultimate melting thicknesses of dolomite, limestone, and granite are 0.0354 m, 0.0350 m, and 0.0234 m, respectively, indicating superior plugging effects in dolomite and limestone. In the initial reaction stage (stage a), the phase change thickness of limestone exceeded that of dolomite by 59.78%, demonstrating better thermite melting and sealing efficacy in limestone. Additionally, model analysis reveals that the initial downhole temperature has a minimal impact on the temperature distribution of thermite melting in open-hole abandoned wells. Full article

Electrical imaging logging technology precisely characterizes the features of the formation on the borehole wall through high-resolution resistivity images. However, the problem of blank strips caused by the mismatch between the instrument pads and the borehole diameter seriously affects the accuracy of fracture identification and formation continuity interpretation in marine oil and gas reservoirs. Existing inpainting methods struggle to reconstruct complex geological textures while maintaining structural continuity, particularly in balancing low-frequency formation morphology with high-frequency fracture details. To address this issue, this paper proposes an inpainting method using a dual-stream network based on the collaborative optimization of wavelet and spatial-channel convolution. By designing a texture-aware data prior algorithm, a high-quality training dataset with geological rationality is generated. A dual-stream encoder–decoder network architecture is adopted, and the wavelet transform convolution (WTConv) module is utilized to enhance the multi-scale perception ability of the generator, achieving a collaborative analysis of the low-frequency formation structure and high-frequency fracture details. Combined with the spatial channel convolution (SCConv) to enhance the feature fusion module, the cross-modal interaction between texture and structural features is optimized through a dynamic gating mechanism. Furthermore, a multi-objective loss function is introduced to constrain the semantic coherence and visual authenticity of image reconstruction. Experiments show that, in the inpainting indexes for Block X in the South China Sea, the mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) of this method are 6.893, 0.779, and 19.087, respectively, which are significantly better than the improved filtersim, U-Net, and AOT-GAN methods. The correlation degree of the pixel distribution between the inpainted area and the original image reaches 0.921~0.997, verifying the precise matching of the low-frequency morphology and high-frequency details. In the inpainting of electrical imaging logging images across blocks, the applicability of the method is confirmed, effectively solving the interference of blank strips on the interpretation accuracy of marine oil and gas reservoirs. It provides an intelligent inpainting tool with geological interpretability for the electrical imaging logging interpretation of complex reservoirs, and has important engineering value for improving the efficiency of oil and gas exploration and development. Full article

Lithology identification is fundamental for the logging evaluation of natural gas hydrate reservoirs. The Sanlutian field, located in the permafrost zones of the Qilian Mountains (PZQM), presents unique challenges for lithology identification due to its complex geological features, including fault development, missing and duplicated stratigraphy, and a diverse array of rock types. Conventional methods frequently encounter difficulties in precisely discerning these rock types. This study employs well logging and core data from hydrate boreholes in the region to evaluate the performance of four data-driven machine learning (ML) algorithms for lithological classification: random forest (RF), multi-layer perceptron (MLP), logistic regression (LR), and decision tree (DT). The results indicate that seven principal lithologies—sandstone, siltstone, argillaceous siltstone, silty mudstone, mudstone, oil shale, and coal—can be effectively distinguished through the analysis of logging data. Among the tested models, the random forest algorithm demonstrated superior performance, achieving optimal precision, recall, F1-score, and Jaccard coefficient values of 0.941, 0.941, 0.940, and 0.889, respectively. The models were ranked in the following order based on evaluation criteria: RF > MLP > DT > LR. This research highlights the potential of integrating artificial intelligence with logging data to enhance lithological classification in complex geological settings, providing valuable technical support for the exploration and development of gas hydrate resources. Full article

Energy transition is a challenge for remote northern communities mainly relying on diesel for electricity generation and space heating. Solar-assisted ground-coupled heat pump (SAGCHP) systems represent an alternative that was investigated in this study for the Kuujjuaq Forum, a multi-activity facility in Nunavik, Canada. The energy requirements of community buildings facing a subarctic climate are poorly known. Based on energy bills, technical documents, and site visits, this study provided an opportunity to better document the energy consumption of such building, especially considering the recent solar photovoltaic (PV) system installed on part of the roof. A comprehensive model was developed to analyze the building’s heating demand and simulate the performance of a ground-source heat pump (GSHP) coupled with PV panels. The air preheating load, accounting for 268,200 kWh and 47% of the total heating demand, was identified as an interesting and realistic load that could be met by SAGCHP. The GSHP system would require a total length of at least 8000 m, with boreholes at depths between 170 and 200 m to meet this demand. Additional PV panels covering the entire roof could supply 30% of the heat pump’s annual energy demand on average, with seasonal variations from 22% in winter to 53% in spring. Economic and environmental analysis suggest potential annual savings of CAD 164,960 and 176.7 tCO₂eq emissions reduction, including benefits from exporting solar energy surplus to the local grid. This study provides valuable insights on non-residential building energy consumption in subarctic conditions and demonstrates the technical viability of SAGCHP systems for large-scale applications in remote communities. Full article

Understanding the climate–weathering coupling mechanisms remains pivotal for interpreting global glacial–interglacial cycles, yet advancements have been constrained by the limited high-resolution sedimentary archives. The newly acquired BXZK2017-2 borehole (30.5 m core) from Bohai Bay provides an exceptional sedimentary sequence to investigate the Late Quaternary climate–weathering interactions. Through an integrated high-resolution chronostratigraphic framework (AMS ¹⁴C and OSL dating) coupled with multi-proxy sedimentological analyses (major element geochemistry and granulometric parameters), we reconstructed the chemical–weathering dynamics in the Bohai coastal region since the Late Pleistocene. Our findings revealed four distinct climate-weathering phases that correlate with the regional paleoenvironmental evolution and global climate perturbations: (1) enhanced weathering during mid-MIS3 to ~37.5 cal kyr BP (Chemical Index of Alteration (CIA): 55.9–62.2), corresponding to regional warming and strengthened summer monsoon circulation; (2) weathering minimum in late MIS3 through early–mid-MIS2 (37.5–14.8 cal kyr BP, CIA < 55), marking the peak aridity before the Last Glacial Maximum; (3) maximum weathering intensity from mid-MIS2 to early MIS1 (14.8–3.34 cal kyr BP, CIA: 65–68), documenting the postglacial humidification driven by the intensified East Asian Summer Monsoon; (4) renewed weathering decline during the Neoglacial (3.34 cal kyr BP-present, CIA: 59–63), coinciding with the late Holocene cooling events. Remarkably, this study identifies a striking synchronicity between the CIA in marine drill cores and δ¹⁸O records derived from Greenland ice cores. Our results indicate that chemical weathering proxies from marginal sea sediments can serve as robust recorders of post-Late Pleistocene climate variability, establishing a new proxy framework for global paleoclimate comparative research. Full article

Single-well imaging makes use of reflected waves to image geological structures outside a borehole, with a detection distance expected to reach tens of meters. However, in the received full wave signal, reflected waves have much smaller amplitudes than borehole-guided waves, which travel directly through the borehole. To obtain clear reflected waves, we use a deep neural network, the multi-channel convolutional time-domain audio separation network (MC-ConvTasNet), to extract reflected waves. In the signal channels of the common-source gather, there exists a notable arrival time difference between direct waves and reflected waves. Leveraging this characteristic, we train MC-ConvTasNet on the common-source gathers, ultimately achieving satisfactory results in wave separation. For the hard-to-hard single-interface, soft-to-hard single-interface and double-interface models, the reflected waves extracted by MC-ConvTasNet are closer to the theoretical reflected waves in both phase and shape (the average scale-invariant signal-to-distortion ratio exceeds 32 dB) than those extracted by parameter estimation, a median filter and an F-K filter. Meanwhile, MC-ConvTasNet naturally fits in the scenarios of various inclined interfaces and interfaces parallel to the borehole axis. As an application, our method is employed on field logging data and its ability to separate waves is verified. Full article

With the rapid development of big data and artificial intelligence technologies, the era of Industry 4.0 has driven large open-pit mines towards digital and intelligent transformation. This is particularly true in mature mining areas such as the Yanshan Iron Mine, where the depletion of shallow proven reserves and the increasing issues of mixed surrounding rocks with shallow ore bodies make it increasingly important to build intelligent mines and implement green and sustainable development strategies. However, previous mineralization predictions for the Yanshan Iron Mine largely relied on traditional geological data (such as blasting rock powder, borehole profiles, etc.) exploration reports or three-dimensional explicit ore body models, which lacked precision and were insufficient to meet the requirements for intelligent mine construction. Therefore, this study, based on artificial intelligence technology, focuses on geoscience big data mining and quantitative prediction, with the goal of achieving multi-scale, multi-dimensional, and multi-modal precise positioning of the Yanshan Iron Mine and establishing its intelligent mine technology system. The specific research contents and results are as follows: (1) This study collected and organized multi-source geoscience data for the Yanshan Iron Mine, including geological, geophysical, and remote sensing data, such as mine drilling data, centimeter-level drone image data, and high-spectral data of rocks and minerals, establishing a rich mine big data set. (2) SOM clustering analysis was performed on the elemental data of rock and mineral samples, identifying key elements positively correlated with iron as Mg, Al, Si, S, K, Ca, and Mn. TSG was used to interpret shortwave and thermal infrared hyperspectral data of the samples, identifying the main alteration mineral types in the mining area. Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. This model was then applied to centimeter-level drone images, achieving high-precision intelligent identification of magnetite in the mining area. Combined with LiDAR image elevation data, a real-time three-dimensional surface mineral monitoring model for the mining area was built. (4) The Bagged Positive Label Unlabeled Learning (BPUL) method was adopted to integrate five evidence maps—carbonate alteration, chloritization, mixed rockization, fault zones, and magnetic anomalies—to conduct three-dimensional mineralization prediction analysis for the mining area. The locations of key target areas were delineated. The SHAP index and three-dimensional explicit geological models were used to conduct an in-depth analysis of the contributions of different feature variables in the mineralization process of the Yanshan Iron Mine. In conclusion, this study successfully constructed the technical framework for intelligent mine construction at the Yanshan Iron Mine, providing important theoretical and practical support for mineralization prediction and intelligent exploration in the mining area. Full article

Geothermal resources are crucial for sustainable energy development, yet accurately detecting geothermal anomalies in complex terrains remains a significant challenge. This study develops a multi-source thermal infrared approach to enhance geothermal anomaly detection using Landsat 8 and ASTER land surface temperature (LST) data. The Yangbajing–Yangyi Basin in Tibet, characterized by high altitude and rugged topography, serves as the study area. Landsat 8 winter time-series data from 2013 to 2023 were processed on the Google Earth Engine (GEE) platform to generate multi-year average LST images. After water body removal and altitude correction, a local block thresholding method was applied to extract daytime geothermal anomalies. For nighttime data, ASTER LST products were analyzed using global, local block, elevation zoning, and fault buffer strategies to extract anomalies, which were then fused using Dempster–Shafer (D–S) evidence theory. A joint daytime–nighttime analysis identified stable geothermal anomaly regions, with results closely aligning with known geothermal fields and borehole distributions while predicting new potential anomaly zones. Additionally, a 21-year time-series analysis of MODIS nighttime LST data identified four significant thermal anomaly areas, interpreted as potential magma chambers, whose spatial distributions align with the identified anomalies. This multi-source approach highlights the potential of integrating thermal infrared data for geothermal anomaly detection, providing valuable insights for exploration in geologically complex regions. Full article