Search Results (55)

Near-bit multi-parameter MWD (measurement while drilling) is a key technology for achieving precise and efficient directional drilling in underground and tunnel engineering. The near-bit multi-parameter MWD method was studied, and a “center + side wall” distributed measurement scheme was proposed, based on an analysis of special application scenarios in underground and tunnel engineering. The transmission characteristics of Bluetooth wireless signals in water were investigated. An analysis of the underwater Bluetooth signal link was conducted. When the transmission distance is 100 mm, the received signal strength is −17.5 dBm, and the link margin is 69.5 dB. Wireless Bluetooth was used to transmit the near-bit data. A Bluetooth wireless communication simulation model was established using ANSYS software, and the influence of transmission power, transmission medium, and transmission distance on the Bluetooth signal strength was analyzed. The results indicate that: (1) the received signal strength increases with transmission power, and appropriately increasing the transmission power can improve the effect of Bluetooth wireless communication and extend the communication distance. (2) When the transmission medium is water, the received signal is unstable, and the echo loss curve shows a high and low oscillation form, presenting a frequency shift feature; when the transmission medium is air, the received signal is relatively stable, and the echo loss curve shows a parabolic form. The echo loss of Bluetooth wireless signal in water transmission is significantly higher than that in air transmission, indicating that the Bluetooth signal attenuates more rapidly when transmitted in water. (3) When the transmission distance increases near the optimal transmission frequency of 2.4 GHz, the echo loss increases accordingly, and the received signal strength of the wireless receiving module gradually decreases. The theoretical analysis, simulation, and indoor test results are in good agreement. The reasonable Bluetooth transmission power is 1 mW, and the transmission distance is 100 mm. After completing the overall scheme design and simulation analysis optimization, the structure, circuit, and program development were carried out, and the near-bit multi-parameter MWD device was developed. A laboratory water supply test was conducted, and the power supply, collection, and wireless transmission were all normal. A drilling test was carried out at an underground engineering of a coal mine in Wuhai City, achieving a drilling depth of 2328 m. A continuous and stable collection of various parameters such as WOB (weight on bit), torque, rotation speed, vibration, and gamma was carried out. A wireless transmission channel for near-bit data was established across the screw drilling tool. It can provide key technical support for the research and development of near-bit MWD in underground and tunnel engineering. Full article

To address the critical challenges of lithology acquisition and low blasting refinement under extreme low temperatures and varying thermal conditions in high-altitude environments, this study develops a real-time dynamic identification method for rock-like interfaces using Measurement While Drilling (MWD) technology. The scope of this research involves the use of a self-developed indoor digital drilling experimental platform to simulate both ambient and freezing (−20 °C) conditions. Procedures included conducting comprehensive comparative drilling experiments on various rock-like materials with distinct strength levels to evaluate their mechanical responses during penetration. The major findings reveal a significant influence of low-temperature hardening effects on MWD parameters; specifically, the frozen state notably increases drilling torque and feed pressure while simultaneously decreasing the stable rotational speed of the drill bit. To resolve the feature parameter drift induced by temperature variations, a novel interface recognition algorithm is proposed that integrates Z-score normalization, change-point detection, and multi-dimensional spatial clustering. Through a dual-detection mechanism involving both single-point and cumulative features, the algorithm effectively captures precise mutation information during rock layer transitions. It further incorporates multi-dimensional indicators, such as consistency, change intensity, and point density, to perform comprehensive weighted scoring. Experimental results demonstrate that the proposed algorithm effectively eliminates the systematic offset of parameters caused by temperature fluctuations. The prediction error at both “strong-weak” and “weak-strong” transition interfaces is maintained within 1.5 mm, which significantly improves the accuracy and robustness of interface recognition under complex and varying working conditions. These key conclusions provide essential technical support for the implementation of differentiated charging and green refined mining operations, ensuring greater energy efficiency and environmental protection in cold-region engineering. Full article

The growing global demand for critical raw materials (CRMs) essential to renewable energy, electromobility, and digital technologies has accelerated the need for advanced exploration methods capable of operating in increasingly challenging geological environments. Traditional drilling systems, designed primarily for shallow mineral and hydrocarbon exploration, face limitations in heterogeneous and consolidated formations where rock heterogeneity, variable mechanical strength, and borehole instability restrict operational efficiency. This review bridges geological science and robotic engineering by analyzing the evolution of next-generation autonomous drilling technologies integrating sensor systems, artificial intelligence (AI), and real-time geotechnical feedback. The current work explores how robotic drilling systems can autonomously adapt to variable lithologies, optimize penetration rates, and ensure borehole stability through intelligent sensing and control. The paper reviews the geological, geomechanical and ore deposit characteristics of CRMs, discusses state-of-the-art drilling optimization strategies, and highlights advances in measurement while drilling (MWD), logging while drilling (LWD), and geochemical analysis techniques. It also suggests a list of sensor techniques for possible future integration in autonomous subsurface robotic systems. It concludes by emphasizing the need for integration between subsurface geological modeling and intelligent drilling robotics as a pathway toward sustainable and efficient CRM exploration. Full article

Measurement While Drilling (MWD) tools are widely employed in deep and ultra-deep well drilling. In the high-temperature and high-pressure (HTHP) environments characteristic of these wells, structural deformation induced by thermal expansion interferes with the bit parameter recorder’s sensor readings, thereby degrading the measurement accuracy of weight on bit (WOB) and working torque (WT). To address this issue, this paper proposes a temperature compensation method based on thermo-mechanical coupling simulation. This method systematically establishes the quantitative relationships between multiple loads—including WT, WOB, temperature, and make-up torque—and the strain at critical locations of the bit parameter recorder through finite element analysis (FEA). Furthermore, surface calibration experiments have verified a strong linear correlation between the strain gauge voltage signals and the simulated strain. Building upon this foundation, an inversion-based compensation algorithm is developed. This algorithm effectively isolates the interference caused by thermally induced deformation and inversely deduces the true WOB and torque values by utilizing downhole-measured sensor voltage and temperature data. The research results demonstrate that the proposed temperature compensation method significantly improves the measurement accuracy of the bit parameter recorder under harsh, high-temperature operating conditions. The relative errors for both WOB and torque measurements are controlled to within 5%, providing a reliable solution for precise parameter measurement in high-temperature deep wells. Full article

Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R² = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments. Full article

During the drilling operations of a shale gas well in Central China, a severe failure occurred in the pressure-resistant cylinder of the measurement while drilling (MWD) tool, with numerous microcracks observed on the outer surface of the cylinder. This significantly compromised the safety of the MWD tool and the reliability of the logging data. To determine the cause of the failure, macroscopic morphology analysis and physicochemical performance tests were conducted on the failed pressure-resistant cylinder, which is made of Cr20Ni11 (UNS 308) austenitic stainless steel. Additionally, scanning electron microscopy, X-ray energy dispersive spectroscopy, white light interferometry, and X-ray photoelectron spectroscopy were employed to analyze the morphology and chemical composition of the corrosion products and cracks, thereby identifying the cause of the corrosion failure. It is demonstrated that the physicochemical properties of the pressure-resistant cylinder comply with the specifications of relevant standards. Nevertheless, the size of non-metallic inclusions in the material reaches 100 μm, which significantly enhances the material’s susceptibility to stress corrosion cracking (SCC). Meanwhile, solid particles and high-concentration Cl⁻ present in the drilling fluid deteriorate the passive film formed on the substrate surface. EDS analysis reveals that the Cl⁻ content is measured to be 4.09 wt%, which induces pitting on the substrate with a maximum pitting depth of 13.5556 μm. Under the synergistic effect of stress and corrosion, the pressure-resistant cylinder experiences SCC failure initiated by Cl⁻; specifically, cracks nucleate at the bottom of the pitting pits and propagate along the radial direction. Full article

Significant thermal dynamics occur during both well construction and injection-production cycles in underground energy storage systems. Accurately determining the wellbore temperature distribution is crucial for optimizing drilling processes, enhancing energy storage efficiency, and evaluating reservoir thermal impacts. Existing measurement-while-drilling (MWD) temperature technologies are mostly limited to single-point measurements at the bottomhole, making it difficult to obtain a full wellbore temperature profile. This study proposes a novel microchip logging technology that achieves breakthroughs in power control and high-temperature resistance through optimized system architecture and workflow, with a maximum operating temperature of 160 °C and the ability to function continuously for 5 h under high-temperature conditions. Field tests successfully captured dynamic temperature data during the microchips’ circulation with the drilling fluid. The study established a temperature field model, applied the temperature measurement data to the model improvement, and analyzed the temperature evolution laws throughout the entire process, including bottomhole circulation, reaming operations, and microchip deployment. The model exhibits excellent consistency with the measured values, which is significantly higher than that of traditional models. The research indicates that this technology can be extended to temperature monitoring during cyclic injection and production processes in underground energy storage systems, supporting the design and operation of underground renewable energy storage (URES) systems. Full article

With the increasing use of horizontal wells in oil and gas development, measurement-while-drilling (MWD) systems require higher magnetic azimuth accuracy to ensure precise trajectory control. This study proposes a three-stage magnetic azimuth error compensation method based on multi-station analysis (MSA). First, the OPTICS clustering algorithm is utilized to identify and remove noise points, and ellipse fitting is applied to suppress radial magnetic interference. Second, an improved MSA model incorporating wellbore trajectory constraints is developed to minimize axial interference and enhance correction stability. Finally, a Gaussian Process Regression (GPR) model, using accelerometer and magnetometer data as features, is introduced to model and compensate for residual nonlinear errors. Experimental validation under simulated complex magnetic conditions shows that OPTICS-based preprocessing significantly improves ellipse fitting and reduces hard magnetic interference. The improved MSA lowers the mean azimuth error to approximately 2.5°, while integrating GPR further decreases it to below 0.04°. The proposed GPR model achieves an R² of 0.99972 and an RMSE of 0.02928° on the test set, confirming its strong nonlinear compensation capability. Overall, the proposed framework effectively suppresses magnetic interference and enhances azimuth accuracy, providing a robust solution for high-precision MWD applications in horizontal wells. Full article

This paper presents an integrated downhole risk prevention and control system designed to enhance safety, efficiency and sustainability in deep-well drilling operations. The system incorporates advanced measurement processing, risk evaluation, and intelligent data transmission technologies for real-time monitoring of nine key drilling parameters, such as downhole drilling pressure, bending moment, and torque, etc. Bench tests and field trials demonstrated the system’s reliability in accurately capturing and transmitting data under high-pressure, high-temperature conditions. For instance, it successfully monitored bottom-hole pressure up to 61.4 MPa and temperature to 120.8 °C, allowing for early detection of abnormal events such as pressure kicks and torsional stick-slip. The system was laboratory-tested to withstand bottom-hole pressures up to 61.4 MPa and temperatures of 120.8 °C. During field trials, the tool operated safely under actual downhole conditions of approximately 59.2 MPa and 115 °C, which are within its rated limits. The system also facilitated automated controlled actions, including mud weight and pump rate control, to prevent incidents. These results underscore the system’s potential to significantly improve real-time and intelligent process control, minimize operational risks, and advancing the sustainability of drilling practices. The approach marks a step forward in intelligent drilling technologies, supporting proactive decision-making in energy extraction. Future work will extend this system to ultra-deep and high-temperature wells while integrating advanced AI-based analytics for further optimization. Full article

Measurement While Drilling (MWD) systems require high-precision triaxial magnetometers for real-time downhole attitude sensing, yet conventional fluxgates fail to meet the stringent size, noise, bandwidth, and temperature demands of deep reservoirs (>175 °C). To bridge this gap, we present a miniaturized triaxial fluxgate magnetometer (23 × 23 × 21 mm³) leveraging Co-Fe-Si-B amorphous wire cores—a material selected for its near-zero magnetostriction and tunable magnetic anisotropy. The sensor achieves breakthrough performance: a 300 Hz bandwidth combined with noise levels below 200 pT/√Hz at 1 Hz when operating at 175 °C while maintaining full functionality with the probe surviving temperatures exceeding 200 °C. This advancement paves the way for more accurate wellbore positioning and steering in high-temperature hydrocarbon and geothermal reservoirs. Full article

This study investigates the relationship between anchor cable drilling parameters and roadway roof strata properties. The goal is to enable rapid and accurate rock type identification. Field-measured drilling data were processed using data cleaning and wavelet transform noise reduction. Four recognition models were developed and compared: LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), DBO-LSTM (Dung Beetle Optimizer), and DBO-BiLSTM. The results demonstrate a strong correlation between vibration, pressure signals and rock strength, enabling the effective differentiation of rock types. All models performed exceptionally for coal seams with distinct features, achieving 100% accuracy, precision, recall, and F1 scores. Model performance improved with increased complexity for strata with subtle differences, such as sandstone and mudstone. The DBO-BiLSTM model outperformed others, showing significant improvements in accuracy, recall, and F1 score compared to LSTM, BiLSTM, and DBO-LSTM models. Specifically, accuracy improved by up to 9%, recall by 12.48%, and F1 score by 13.06%. These findings highlight the DBO-BiLSTM model’s superior recognition capability for roof strata drilling signals. This method provides a robust technical foundation for lithology identification in Measurement While Drilling (MWD) systems. It supports more precise and efficient roadway design in complex geological conditions. Full article

Measurement while drilling technology (MWD) has emerged as a pivotal approach for geological exploration. However, the accuracy of existing geological recognition models remains limited, primarily due to data fluctuations that result in high overlap rates and reduced reliability of drilling parameters. This study takes torque data as an example and analyzes the frequency distribution laws of torque responses across rock with varying strengths. A quantitative model of the frequency distribution characteristic interval is established, and a rock information prediction approach based on frequency distribution characteristics is proposed. The results indicate that torque frequency distributions for homogeneous rock exhibit a unimodal pattern, whereas those for composite rocks display multimodal characteristics. The boundaries of the frequency distribution characteristic intervals are mathematically defined as CIS = T_p|_{(dF/dT) = 0} ± σ and CIM = x_li ± 0.5∆x_i. The strength prediction model constructed using torque within the characteristic interval achieves an average accuracy of 85.3%. Furthermore, the frequency of torque within the characteristic interval enables the estimation of rock stratum thickness. This research contributes to enhancing the accuracy of rock information identification. Full article

To address the challenges of signal recognition and prediction in intelligent sliding sleeve downlink communication systems, this paper proposes a dual-model framework based on Long Short-Term Memory (LSTM) networks. The system comprises a classifier for identifying pressure wave edge types and a generator for predicting pressure waveforms. High-quality training data are generated by simulating pressure wave propagation caused by throttle valve modulations. A sliding window strategy and Z-score normalization are applied to enhance temporal modeling. The classifier achieves a high accuracy in identifying rising and falling edges under noise-free conditions. The generator, trained on down-sampled waveform segments, accurately reconstructs pressure dynamics using a dual-input strategy based on historical segments and hypothetical labels. A residual-based decision mechanism is employed to complete the full sequence label prediction. To evaluate robustness, noise intensities of 30 dB and 40 dB are introduced. The proposed system maintains high performance under both conditions, achieving label prediction accuracies of 100%. Error metrics such as MAE and RMSE remain within acceptable bounds, even in noisy environments. The results demonstrate that the proposed LSTM-based method has been validated on simulated data, showing its potential to approximate performance in real-world conditions. It provides a promising solution for cable-free measurement-while-drilling (MWD) and remote control of intelligent sliding sleeves in complex downhole environments. Full article

Measuring-while-drilling (MWD) techniques have great potential for use in geotechnical engineering research. This study first addresses the current use of MWD, which consists of recording data using sensors in a drilling machine operating on site. It then addresses the currently unsolved problems of quality control in drilled piles and assessments of their interaction with the soil under load. Next, an original method of drilling displacement piles using a special EGP auger (Electro-Geo-Probe) is presented. The innovation of this new drilling system lies in the placement of the sensors inside the EGP auger in the soil. These innovative sensors form an integrated measurement system, enabling improved real-time control during pile drilling. The most original idea is the use of a Cone Penetration Test (CPT) probe that can be periodically and remotely inserted at a specific depth below the pile base being drilled. This new MWD-EGP system with cutting-edge sensors to monitor the soil’s impact on piles during drilling revolutionizes pile drilling quality control. Furthermore, implementing this in-auger sensor system is a step towards the development of digital drilling rigs, which will provide better pile quality thanks to solutions based on the results of real-time, on-site soil testing. Finally, examples of measurements taken with the new sensor-equipped auger and a preliminary interpretation of the results in non-cohesive soils are presented. The obtained data confirm the usefulness of the new drilling system for improving the quality of piles and advancing research in geotechnical engineering. Full article

This paper presents a comprehensive study of torsional stick–slip vibrations in rotary drilling systems through a comparison between two lumped parameter models with differing complexity: a simple two-degree-of-freedom (2-DOF) model and a complex high-degree-of-freedom (high-DOF) model. The two models are developed under identical boundary conditions and consider an identical nonlinear friction torque dynamic involving the Stribeck effect and dry friction phenomena. The high-DOF model is calculated with the Finite Element Method (FEM) to enable accurate simulation of the dynamic behavior of the drill string and accurate representation of wave propagation, energy build-up, and torque response. Field data obtained from an Algerian oil well with Measurement While Drilling (MWD) equipment are used to guide modeling and determine simulations. According to the findings, the FEM-based high-DOF model demonstrates better performance in simulating basic stick–slip dynamics, such as drill bit velocity oscillation, nonlinear friction torque formation, and transient bit-to-surface contacts. On the other hand, the 2-DOF model is not able to represent these effects accurately and can lead to inappropriate control actions and mitigation of vibration severity. This study highlights the importance of robust model fidelity in building reliable real-time rotary drilling control systems. From the performance difference measurement between low-resolution and high-resolution models, the findings offer valuable insights to optimize drilling efficiency further, minimize non-productive time (NPT), and improve the rate of penetration (ROP). This contribution points to the need for using high-fidelity models, such as FEM-based models, in facilitating smart and adaptive well control strategies in modern petroleum drilling engineering. Full article