Search Results (933)

In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT. Full article

Deep drilling and horizontal wells, as important means of unconventional oil and gas development, face problems with the high energy consumption but low removal efficiency of traditional well washing equipment, the uneven cleaning of horizontal well intervals, and an insufficient degree of automation. This paper proposes a novel hydraulic drive well washing device which consists of two main units. The wellbore cleaning unit comprises a hydraulic drive cutting–flushing module, a well cleaning mode-switching module, and a filter storage module. The unit uses hydraulic and mechanical forces to perform combined cleaning to prevent mud and sand from settling. By controlling the flow direction of the well washing fluid, it can directly switch between normal and reverse washing modes in the downhole area, and at the same time, it can control the working state of corresponding modules. The assembly control unit includes the chain lifting module and the arm assembly module, which can lift and move the device through the chain structure, allow for the rapid assembly of equipment through the use of a mechanical arm, and protect the reliability of equipment through the use of a centering structure. The device converts some of the hydraulic power into mechanical force, effectively improving cleaning and plugging removal efficiency, prolonging the downhole continuous working time of equipment, reducing manual operation requirements, and comprehensively improving cleaning efficiency and energy utilization efficiency. Full article

This study presents the development and validation of an AI-based system for detecting chainsaw sounds, integrated into a fixed-wing VTOL UAV. The system employs a convolutional neural network trained on log-mel spectrograms derived from four sound classes: chainsaw, music, electric drill, and human voices. Initial validation was performed through ground testing. Acoustic data acquisition is optimized during cruise flight, when wing-mounted motors are shut down and the rear motor operates at 40–60% capacity, significantly reducing noise interference. To address residual motor noise, a preprocessing module was developed using reference recordings obtained in an anechoic chamber. Two configurations were tested to capture the motor’s acoustic profile by changing the UAV’s orientation relative to the fixed microphone. The embedded system processes incoming audio in real time, enabling low-latency classification without data transmission. Field experiments confirmed the model’s high precision and robustness under varying flight and environmental conditions. Results validate the feasibility of real-time, onboard acoustic event detection using spectrogram-based deep learning on UAV platforms, and support its applicability for scalable aerial monitoring tasks. Full article

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. 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

Lost circulation is a major challenge in oil and gas drilling operations, severely restricting drilling efficiency and compromising operational safety. Conventional bridging and plugging materials rely on precise particle-to-fracture size matching, resulting in low success rates. Self-healing gels penetrate loss zones as discrete particles that progressively swell, accumulate, and self-repair in integrated gel masses to effectively seal fracture networks. Self-healing gels effectively overcome the shortcomings of traditional bridging agents including poor adaptability to fractures, uncontrollable gel formation of conventional downhole crosslinking gels, and the low strength of conventional pre-crosslinked gels. This work employs stearyl methacrylate (SMA) as a hydrophobic monomer, acrylamide (AM) and acrylic acid (AA) as hydrophilic monomers, and graphene oxide (GO) as an inorganic dopant to develop a GO-based self-healing organic–inorganic hybrid plugging material (SG gel). The results demonstrate that the incorporation of GO significantly enhances the material’s mechanical and rheological properties, with the SG-1.5 gel exhibiting a rheological strength of 3750 Pa and a tensile fracture stress of 27.1 kPa. GO enhances the crosslinking density of the gel network through physical crosslinking interactions, thereby improving thermal stability and reducing the swelling ratio of the gel. Under conditions of 120 °C and 6 MPa, SG-1.5 gel demonstrated a fluid loss volume of only 34.6 mL in 60–80-mesh sand bed tests. This gel achieves self-healing within fractures through dynamic hydrophobic associations and GO-enabled physical crosslinking interactions, forming a compact plugging layer. It provides an efficient solution for lost circulation control in drilling fluids. Full article

In deep, geologically complex environments characterized by high in situ stress and elevated formation temperatures, the mechanical behavior of rocks often transitions from brittle to ductile, differing significantly from that of shallow formations. Traditional rock failure criteria frequently fail to accurately assess the strength of rocks under such deep conditions. To address this, a novel failure criterion suitable for high-temperature and high-pressure conditions has been developed by modifying the Mohr–Coulomb criterion. This criterion incorporates a quadratic function of confining pressure to account for the attenuation rate of strength increase under high confining pressure and a linear function of temperature to reflect the linear degradation of strength at elevated temperatures. This criterion has been used to predict the strength of granite, shale, and carbonate rocks, yielding results that align well with the experimental data. The average coefficient of determination (R²) reached 97.1%, and the mean relative error (MRE) was 5.25%. Compared with the Hoek–Brown and Bieniawski criteria, the criterion proposed in this study more accurately captures the strength characteristics of rocks under high-temperature and high-pressure conditions, with a prediction accuracy improvement of 1.70–4.09%, showing the best performance in the case of carbonate rock. A sensitivity analysis of the criterion parameters n and B revealed notable differences in how various rock types respond to these parameters. Among the three rock types studied, granite exhibited the lowest sensitivity to both parameters, indicating the highest stability in the prediction results. Additionally, the predictive outcomes were generally more sensitive to changes in parameter B than in n. These findings contribute to a deeper understanding of rock mechanical behavior under extreme conditions and offer valuable theoretical support for drilling, completion, and stimulation operations in deep hydrocarbon reservoirs. Full article

The accurate prediction of static formation temperature (SFT) is essential for ensuring safety and efficiency in ultra-deep well drilling operations. Excessive downhole temperatures (>150 °C) can degrade drilling fluids, damage temperature-sensitive tools, and pose serious operational risks. Conventional methods for SFT determination—including direct measurement, temperature recovery inversion, and artificial intelligence models—are often limited by post-drilling data dependency, insufficient spatial resolution, high computational costs, or a lack of adaptability to complex wellbore geometries. In this study, we propose a new pseudo-3D Kriging interpolation framework that explicitly incorporates real wellbore trajectories to improve the spatial accuracy and applicability of pre-drilling SFT predictions. By systematically optimizing key hyperparameters (θ = [10, 10], lob = [0.1, 0.1], upb = [20, 200]) and applying a grid resolution of 100 × 100, the model demonstrates high predictive fidelity. Validation using over 5.1 million temperature data points from 113 wells in the Shunbei Oilfield reveals a relative error consistently below 5% and spatial interpolation deviations within 5 °C. The proposed approach enables high-resolution, trajectory-integrated SFT forecasting before drilling with practical computational requirements, thereby supporting proactive thermal risk mitigation and significantly enhancing operational decision-making on ultra-deep wells. Full article

This study aims to provide a comprehensive overview of the application of artificial intelligence (AI) methods to solve real-world problems in the oil and gas sector. The methodology involved a two-step process for analyzing AI applications. In the first step, an initial exploration of scientific articles in the Scopus database was conducted using keywords related to AI and computational intelligence, resulting in a total of 11,296 articles. The bibliometric analysis conducted using VOS Viewer version 1.6.15 software revealed an average annual growth of approximately 15% in the number of publications related to AI in the sector between 2015 and 2024, indicating the growing importance of this technology. In the second step, the research focused on the OnePetro database, widely used by the oil industry, selecting articles with terms associated with production and drilling, such as “production system”, “hydrate formation”, “machine learning”, “real-time”, and “neural network”. The results highlight the transformative impact of AI on production operations, with key applications including optimizing operations through real-time data analysis, predictive maintenance to anticipate failures, advanced reservoir management through improved modeling, image and video analysis for continuous equipment monitoring, and enhanced safety through immediate risk detection. The bibliometric analysis identified a significant concentration of publications at Society of Petroleum Engineers (SPE) events, which accounted for approximately 40% of the selected articles. Overall, the integration of AI into production operations has driven significant improvements in efficiency and safety, and its continued evolution is expected to advance industry practices further and address emerging challenges. Full article

Accurate pre-drilling mud weight window (MWW) prediction is crucial for drilling fluid design and wellbore stability in complex geological formations. Traditional physics-based approaches suffer from subjective parameter selection and inadequate handling of multi-mechanism over-pressured formations, while machine learning methods lack physical constraints and interpretability. This study develops a novel physics-guided deep learning framework integrating rock mechanics theory with deep neural networks for enhanced MWW prediction. The framework incorporates three key components: first, a physics-driven layer synthesizing intermediate variables from rock physics calculations to embed domain knowledge while preserving interpretability; second, a geological sequence-matching algorithm enabling precise stratigraphic correlation between offset and target wells, compensating for lateral geological heterogeneity; third, a long short-term memory network capturing sequential drilling characteristics and geological structure continuity. Case study results from 12 wells in northwestern China demonstrate significant improvements over traditional methods: collapse pressure prediction error reduced by 40.96%, pore pressure error decreased by 30.43%, and fracture pressure error diminished by 39.02%. The proposed method successfully captures meter-scale pressure variations undetectable by conventional approaches, providing critical technical support for wellbore design optimization, drilling fluid formulation, and operational safety enhancement in challenging geological environments. Full article

The torsional, lateral, and axial vibrations that occur during drilling operations have negative effects on the drilling equipment. These negative effects can cause huge economic impacts, as the failure of drilling tools results in wasted materials, non-productive time, and substantial expenses for equipment repairs. Many researchers have tried to reduce these vibrations and have tested several models in their studies. In most of these models, the drill string used in oil wells behaves like a rotating torsion pendulum (mass spring), represented by different discs. The top drive (with the rotary table) and the BHA (with the drill pipes) have been considered together as a linear spring with constant torsional stiffness and torsional damping coefficients. In this article, three models with different degrees of freedom are considered, with the aim of analyzing the effect of variations in the stiffness and damping coefficients on the severity of torsional vibrations. A comparative study has been conducted between the three models for dynamic responses to parametric variation effects. To ensure the relevance of the considered models, the field data of torsional vibrations while drilling were used to support the modeling assumption and the designed simulation scenarios. The main novelty of this work is its rigorous comparative analysis of how the stiffness and damping coefficients influence the severity of torsional vibrations based on field measurements, which has a direct application in operational energy efficiency and equipment reliability. The results demonstrated that the variation of the damping coefficient does not significantly affect the severity of the torsional vibrations. However, it is highly recommended to consider all existing frictions in the tool string to obtain a reliable torsional vibration model that can reproduce the physical phenomenon of stick–slip. Furthermore, this study contributes to the improvement of operational energy efficiency and equipment reliability in fossil energy extraction processes. Full article

Partially guided implant surgery has emerged as a technique that enhances the precision of implant placement while maintaining surgical flexibility. This in vitro experimental study evaluated the influence of the milling cutter drill on the angular and linear deviations of implant placement in synthetic polyurethane bone models using a partially guided surgical protocol. Additionally, the effects of bone density and implant macrogeometry were assessed. A total of 120 Straumann^® implants (BL, BLT, and BLX) were placed in polyurethane blocks simulating four bone densities (D1–D4). Implant positions were virtually planned with coDiagnostiX^® (version 10.9) software and executed with or without the use of the milling cutter drill. Deviations between planned and final implant positions were measured at the neck and apex using the software’s “Treatment Evaluation” tool. The use of the milling cutter drill significantly reduced angular deviation (p = 0.007), while linear deviations showed no statistically significant differences. Bone density and implant macrogeometry did not significantly affect angular deviation but influenced linear and 3D deviations. Given that angular deviation may compromise prosthetic fit and biomechanical function, the observed reduction is of potential clinical relevance. These findings indicate that the milling cutter drill enhances angular accuracy in partially guided implant surgery and may improve outcomes in anatomically challenging cases. However, the results should be interpreted within the limitations of this in vitro model, including the absence of soft tissue simulation, intraoral constraints, and inter-operator variability. Full article

Some additives currently used to enhance drilling mud’s rheological qualities have a substantial economic impact on society. Carboxymethyl cellulose (CMC) and calcium carbonate (CaCO₃) are currently imported. Food crops have influences on food security; hence, this research explored the potential of utilizing cow bone powder (CBP), a bio-waste product and a renewable resource, as an environmentally friendly fluid-loss additive for drilling applications, in comparison with CaCO₃. Both samples (CBP and CaCO₃) were evaluated to determine the most efficient powder sizes (coarse, medium, and fine powder), concentrations (5–15 g), and aging conditions (before or after aging) that would offer improved rheological and fluid-loss control. The results obtained showed that CBP had a significant impact on mud rheology when compared to CaCO₃. Decreasing the particle size (coarse to fine particles) and increasing the concentration from 5 to 15 g positively impacted mud rheology. Among all the conditions analyzed, fine-particle CBP with a 15 g concentration produced the best characteristics, including in the apparent viscosity (37 cP), plastic viscosity (29 cP), and yield point (25.5 lb/100 ft²), and a gel strength of 16 lb/100 ft² (10 s) and 28 lb/100 ft² (10 min). The filtration control ability of CaCO₃ was observed to be better than that of the coarse and medium CBP particle sizes; however, fine-particle-size CBP demonstrated a 6.1% and 34.6% fluid-loss reduction at 10 g and 15 g concentrations when compared to respective amounts of CaCO₃. The thermal behavior of the Mud Samples demonstrated that it positively impacted rheology before aging. In contrast, after aging, it exhibited a negative effect where samples grew more viscous and exceeded the API standard range for mud properties. Therefore, CBP’s excellent rheological and fluid-loss control ability makes it a potential, sustainable, and economically viable alternative to conventional materials. This superior performance enhances the thinning properties of drilling muds in stationary and circulating conditions. Full article

This paper presents the procedures and results of the numerical modelling and simulations performed to analyse an innovative method of advanced methane drainage employing underground long-reach directional drilling (LRDD) technology. The analysis involved the implementation of geomechanical and dynamic reservoir models to simulate processes in coal seams and the surrounding rocks during coal mining and concurrent methane drainage, in accordance with the proposed technology. The analysis aimed to quantitatively assess the effectiveness of the technology, evaluate its sensitivity to the geological and geomechanical properties of the rocks, and identify the potential for optimisation of its technological and operational parameters in the proposed strategy. The works presented in this paper include the following key tasks: the construction of a system of geological, geomechanical, and dynamic simulation models; the analysis of the geomechanical effects of various types and regions of occurrence; the implementation of the correlation between the geomechanical states of the rocks and their transport properties; and the performance of the effectively coupled geomechanical and reservoir fluid flow simulations. The proposed approach was applied to the specific conditions of the multi-seam Murcki–Staszic Coal Mine operated by Jastrzębska Spółka Węglowa, Poland. Full article

Wellbore strengthening is a widely applied technique to mitigate wellbore leakage during drilling operations in complex formations characterized by narrow mud weight windows. This method enhances the wellbore’s pressure-bearing capacity by using lost circulation materials (LCMs) to bridge natural or induced fractures. In recent years, advanced sealing technologies such as wellbore reinforcement have gradually been applied and developed, but their related influencing factors and mechanisms have not been deeply revealed. This article uses the Cohesive module of ABAQUS to establish a wellbore fracture sealing model. By establishing a porous elastic finite element model, the elastic mechanics theory of porous media is combined with finite element theory. Under the influence of factors such as anisotropy of geostress, reservoir elastic modulus, Poisson’s ratio, and fracturing fluid viscosity, the circumferential stress distribution of the wellbore after fracture sealing is simulated. The simulation results show that stress anisotropy has a significant impact on Mises stress. The greater the stress anisotropy, the more likely the wellbore sealing is to cause wellbore rupture or instability. Therefore, it is necessary to choose a suitable wellbore direction to avoid high stress concentration areas. The elastic modulus of the reservoir is an important parameter that affects wellbore stability and fracturing response, especially in high modulus reservoirs where the effect is more pronounced. Poisson’s ratio has a relatively minor impact. In fracturing and plugging design, the viscosity of fracturing fluid should be reasonably selected to balance the relationship between plugging efficiency and wellbore mechanical stability. In the actual drilling process, priority should be given to choosing the wellbore direction that avoids high stress concentration areas to reduce the risk of wellbore rupture or instability induced by plugging, specify targeted wellbore reinforcement strategies for high elastic modulus reservoirs; using models to predict fracture response characteristics can guide the use of sealing materials, achieve efficient bridging and stable sealing, and enhance the maximum pressure bearing capacity of the wellbore. By simulating the changes in circumferential stress distribution of the wellbore after fracture sealing, the mechanism of wellbore reinforcement was explored to provide guidance for mechanism analysis and on-site application. Full article