Search Results (41)

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

In geotechnical engineering, an accurate prediction is essential for the safety and effectiveness of construction projects. One example is the prediction of over/under-excavation volumes during drill and blast tunneling. Using machine learning (ML) models to predict over-excavation often results in low accuracy, especially in complex geological settings. This study explores how the pre-processing of measurement while drilling (MWD) data impacts the accuracy of ML models. In this work, a correlational analysis of the MWD data is used as the main pre-processing procedure. For each drilling event (single borehole), correlation coefficients are calculated and then supplied as inputs to the ML model. It is shown that the ML model’s accuracy improves when the correlation coefficients are used as inputs to the ML models. It is observed that datasets made from correlation coefficients help ML models to obtain higher generalization skills and robustness. The informational content of datasets after different pre-processing routines is compared, and it is shown that the correlation coefficient dataset retains information from the original MWD data. Full article

The drilling fluid cooling system is a key technology for reducing wellbore temperatures, improving the working environment of downhole equipment, and ensuring safe and efficient drilling in high-temperature wells. Based on the existing drilling fluid cooling system, this article designs and develops a closed drilling fluid cooling system according to the working environment and cooling requirements of the GH-02 dry hot rock trial production well in the Gonghe Basin, Qinghai Province. The system mainly includes a cascade cooling module, a convective heat exchange module, and a monitoring and control module. Based on the formation conditions and drilling design of the GH-02 well, a transient temperature prediction model for wellbore circulation is established to provide a basis for the design of the cooling system. Under the conditions of a drilling fluid displacement of 30 L/s and a bottomhole circulation temperature not exceeding 105 °C, the maximum allowable inlet temperature of the drilling fluid is 55.6 °C, and the outlet temperature of the drilling fluid is 69.2 °C. The heat exchange of the drilling fluid circulation is not less than 1785 kW. Considering the heat transfer efficiency and reserve coefficient, the heat transfer area of the spiral plate heat exchanger calculated using the average temperature difference method is not less than 75 m². By applying this drilling fluid cooling system in the 3055 m~4013 m section of well GH-02, the inlet temperature is controlled at 45 °C~55 °C, and the measured bottomhole circulation temperature remains below 105 °C. After adopting the drilling fluid cooling system, the performance of the drilling fluid is stable during the drilling process, downhole tools such as the drill bits, screws, and MWD work normally, and the failure rate of the mud pump and logging instruments is significantly reduced. The drilling fluid cooling system effectively maintains the safe and efficient operation of the drilling system, which has been promoted and applied in shale oil wells in Dagang Oilfield. Full article

For Measurement While Drilling (MWD), the redundant Micro-Electro-Mechanical Systems Inertial Measurement Unit (MEMS-IMU) navigation system significantly enhances the reliability and accuracy of drill string attitude measurements. Such an enhancement enables precise control of the wellbore trajectory and enhances the overall quality of drilling operations. But installation errors of the redundant MEMS-IMUs still degrade the accuracy of drill string attitude measurements. It is essential to calibrate these errors to ensure measurement precision. Currently, the commonly used calibration method involves mounting the carrier on a horizontal plane and performing calibration through rotation. However, when the carrier rotates on the horizontal plane, the gravity acceleration component sensed by the horizontal axis of the IMU accelerometer in the carrier is very small, which leads to a low signal-to-noise ratio, so that the measured matrix obtained by the solution is dominated by noise. As a result, the accuracy of the installation is insufficient, and, finally, the effectiveness of the installation error compensation is reduced. In order to solve this problem, this study proposes a 45°-inclined six-position calibration method based on the selected hexagonal prism redundant structure for redundant MEMS-IMUs in MWD. Firstly, the compensation matrices and accelerometer measurement errors were analyzed, and the new calibration method was proposed; the carrier of the IMUs should be installed at an inclined position of 45°. Then, six measuring points were identified for the proposed calibration approach. Finally, simulation and laboratory experiments were conducted to verify the effectiveness of the proposed method. The simulation results showed that the proposed method reduced installation errors by 40.4% compared with conventional methods. The experiments’ results demonstrated reductions of 83% and 68% in absolute measurement errors for the x and y axes, respectively. As a result, sensor accuracy after compensation improved by over 25% compared with traditional methods. The calibration method proposed by this study effectively improves the accuracy of redundant systems, providing a new approach for the precise measurement of downhole trajectories. Full article

This study presents an application of Boruta-SHapley Additive ExPlanations (Boruta-SHAP) for geotechnical characterization using Measure-While-Drilling (MWD) data, enabling a more interpretable and statistically rigorous assessment of feature importance. Measure-While-Drilling data collected at the scale of an open-pit mine was used to characterize geotechnical properties using regression-based machine learning models. In contrast to previous studies using MWD data to recognize rock type using Principal Component Analysis (PCA), which only identifies the directions of maximum variance, the Boruta-SHAP method quantifies the individual contribution of each Measure-While-Drilling variable. This method ensures interpretable and reliable geotechnical characterization as well as robust feature selection by comparing predictors against randomized ‘shadow’ features. The Boruta-SHAP analysis revealed that bit air pressure and torque-to-penetration ratio were the most significant predictors of rock strength, contradicting previous assumptions that rate of penetration was the dominant factor. Moreover, feature importance was conducted for fracture frequency and Geological Strength Index (GSI), a rock mass classification system. A comparative analysis of prediction performance was also performed using a range of different machine learning algorithms that resulted in strong coefficient of determinations of actual field or laboratory results versus predicted values. The results are plausible, confirming that MWD data could provide a high-resolution description of geotechnical conditions prior to mining, leading to a more confident prediction of subsurface geotechnical properties. Therefore, the fragmentation from blasting as well as downstream operational phases, such as digging, hauling, and crushing, could be improved effectively. Full article

Bench-scale geotechnical characterization often suffers from high uncertainty, reducing confidence in geotechnical analysis on account of expensive resource development drilling and mapping. The Measure-While-Drilling (MWD) system uses sensors to collect the drilling data from open-pit blast hole drill rigs. Historically, the focus of MWD studies was on penetration rates to identify rock formations during drilling. This study explores the effectiveness of Artificial Intelligence (AI) classification models using MWD data to predict geotechnical categories, including stratigraphic unit, rock/soil strength, rock type, Geological Strength Index, and weathering properties. Feature importance algorithms, Minimum Redundancy Maximum Relevance and ReliefF, identified all MWD responses as influential, leading to their inclusion in Machine Learning (ML) models. ML algorithms tested included Decision Trees, Support Vector Machines (SVMs), Naive Bayes, Random Forests (RFs), K-Nearest Neighbors (KNNs), Linear Discriminant Analysis. KNN, SVMs, and RFs achieved up to 97% accuracy, outperforming other models. Prediction performance varied with class distribution, with balanced datasets showing wider accuracy ranges and skewed datasets achieving higher accuracies. The findings demonstrate a robust framework for applying AI to real-time orebody characterization, offering valuable insights for geotechnical engineers and geologists in improving orebody prediction and analysis Full article

Bench-scale geological modeling is often uncertain due to limited exploration drilling and geophysical wireline measurements, reducing production efficiency. Measure-While-Drilling (MWD) systems collect drilling data to analyze mining blast hole drill rig performance. Early MWD studies focused on penetration rates to identify rock types. This paper investigates Artificial Intelligence (AI)-based regression models to predict geophysical signatures like density, gamma, magnetic susceptibility, resistivity, and hole diameter using MWD data. The machine learning (ML) models evaluated include Linear Regression (LR), Decision Trees (DTs), Support Vector Machines (SVMs), Random Forests (RFs), Gaussian Processes (GP), and Neural Networks (NNs). An analytical method was validated for accuracy, and a three-tier experimental method assessed the importance of MWD features, revealing no performance loss when excluding features with less than 2% importance. RF, DTs, and GPs outperformed other models, achieving R² values up to 0.98 with a low RMSE, while LR and SVMs showed lower accuracy. The NN’s performance improved with larger datasets. This study concludes that the DT, RF, and GP models excel in predicting geophysical signatures. While ML-based methods effectively model relationships in the data, their predictive performance remains inherently constrained by the underlying geological and physical mechanisms. Model selection depends on computational resources and application needs, offering valuable insights for real-time orebody analysis using AI. These findings could be invaluable to geologists who wish to utilize AI techniques for real-time orebody analysis and prediction. Full article

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in the fields of heat reservoir exploitation and geological exploration owing to its advantages of high efficiency and low pollution. However, the vibration near the bit is up to 40 g while DTH hammer drilling, which significantly affects the performance and longevity of the near-bit measurement while drilling (MWD). To enhance the environmental adaptability of the near-bit MWD in pneumatic DTH operations, a design method for a vibration-damping system based on the parameter optimization of a non-dominated sorting genetic algorithm II (NSGA-II) is proposed in this study. First, the whole structure of the near-bit MWD is designed, including the MWD sub-shell, sensors, measurement circuits, batteries, and connecting structures (the circuit unit). Secondly, this study analyzes the vibration characteristics of the pneumatic DTH hammer near the bit. According to the damping structure, the vibration response model for the circuit unit and the damping model are established. Thirdly, NSGA-II is employed to optimize the parameters of the damping model in terms of the low-frequency, high-intensity vibration characteristics near the bit in pneumatic DTH operations, thereby devising a damping scheme tailored to the unique conditions of DTH hammer drilling. Finally, vibration experiments were conducted to verify the effectiveness of the vibration-damping device. The experimental results indicate that within the vibration frequency range of 5–20 Hz and vibration level of 10–40 g, the peak attenuation rate of the circuit unit is more than 86.446%, and the improvement rate of the vibration stability of the system is more than 75.214%; the anti-vibration performance of the near-bit MWD system in DTH hammer drilling is improved remarkably. This study provides strong technical support for the stability of MWD equipment under such special working conditions. It has broad engineering application prospects. Full article

Conventional Measurement-While-Drilling (MWD) technology is unable to function statically at the predicted temperatures of deep formations exceeding 200 °C in wells reaching depths of 10,000 m. It is limited to measuring downhole engineering parameters through purely mechanical means, such as inclination. However, the accurate long-distance transmission of drilling fluid pulse signals poses a significant bottleneck, restricting the application of these mechanical measurement methods. To address these issues, this paper develops and designs an algorithm to identify and analyze the amplitude characteristics of deep well mud signals. By employing a signal coding algorithm, a signal processing analysis method, and a signal feature recognition algorithm based on grey correlation degree, we construct a signal recognition method capable of decoding mud amplitude encoded signals. Key techniques such as filtering, smoothing, and feature extraction are utilized in the signal processing, and the proposed method’s effectiveness is verified through the analysis of collected signals. Furthermore, long-distance simulation analysis software is developed to evaluate waveform distortion during extended transmission, confirming the feasibility of the recognition algorithm. Laboratory experiments demonstrate that this algorithm can accurately recognize and demodulate signals generated by mechanical inclinometer structures, providing a novel decoding method for signal transmission in deep and ultra-deep wells. Full article

In the environment of oil and gas wells, the shielding effect of metal casing increases the difficulty of applying wireless electromagnetic wave transmission technology in such wells. This paper constructs a theoretical model of downhole electromagnetic helical loop transmission based on the finite element method. The magnetic loop is equated with the helical loop antenna in the model. By means of simulation calculations, this study deeply investigates the impact of various factors, such as working frequency within the cased well, drilling fluid resistivity, formation resistivity, drill string dimensions, and electrical conductivity, on the attenuation pattern of the helical loop antenna. The results show that low-frequency signals experience relatively less attenuation underground, while high-frequency signals demonstrate better transmission effects over shorter distances. Moreover, drilling fluids with low resistivity are more suitable for short-distance transmission, whereas high resistivity can effectively reduce signal attenuation and improve transmission distance. The variation in formation resistivity has a relatively small impact on signal transmission and can be considered negligible. In terms of drill string characteristics, as the electrical conductivity of the drill collars increases, signal attenuation gradually decreases, and the amplitude of the received signal is enhanced. With the inner and outer diameters of the drill collars remaining the same, a finer inner diameter of the casing aids electromagnetic wave short-distance transmission, whereas a thicker casing can reduce electromagnetic wave attenuation. Theoretical and practical results are in good agreement through field trial comparative analysis. Full article

In constructing rapid rock identification models for measurement while drilling (MWD) via neural network methods, collecting actual drilling data to train the model is extremely time-consuming and labor-intensive. This requires extensive drilling experiments in various rock types, resulting in limited neural network training data for rock identification that covers a limited range of rock types. To suitably address this issue, a dynamic numerical simulation model for rock drilling is established that generates extensive drilling data. The input parameters for the simulations include torque, drill bit rotation speed, and drilling speed. A neural network model is then developed for rock classification using large datasets from dynamic numerical simulations, specifically those of granite, limestone, and sandstone. Building upon this model, transfer learning is appropriately applied to store the knowledge obtained in the rock identification based on the neural network model. Further training through transfer learning is conducted with smaller datasets obtained during actual drilling, making the model suitable for practical rock identification and prediction in the drilling processes. The neural network rock classification model, incorporating dynamic numerical simulation and transfer learning, achieves a prediction accuracy of 99.36% for granite, 99.53% for sandstone, and 99.82% for limestone. This reveals an enhancement in prediction accuracy of up to 22.94% compared to the models without transfer learning. Full article

Measure while drilling (MWD) refers to the acquisition of real-time data associated with the drilling process, including information related to the geological characteristics encountered in hard-rock mining. The availability of large quantities of low-cost MWD data from blast holes compared to expensive and sparsely collected orebody knowledge (OBK) data from exploration drill holes make the former more desirable for characterizing pre-excavation subsurface conditions. Machine learning (ML) plays a critical role in the real-time or near-real-time analysis of MWD data to enable timely enhancement of OBK for operational purposes. Applications can be categorized into three areas, focused on the mechanical properties of the rock mass, the lithology of the rock, as well as, related to that, the estimation of the geochemical species in the rock mass. From a review of the open literature, the following can be concluded: (i) The most important MWD metrics are the rate of penetration (rop), torque (tor), weight on bit (wob), bit air pressure (bap), and drill rotation speed (rpm). (ii) Multilayer perceptron analysis has mostly been used, followed by Gaussian processes and other methods, mainly to identify rock types. (iii) Recent advances in deep learning methods designed to deal with unstructured data, such as borehole images and vibrational signals, have not yet been fully exploited, although this is an emerging trend. (iv) Significant recent developments in explainable artificial intelligence could also be used to better advantage in understanding the association between MWD metrics and the mechanical and geochemical structure and properties of drilled rock. Full article

A typical magnetometer-based measurement-while-drilling (MWD) system determines the azimuth of the bottom hole assembly during the drilling process by employing triaxial accelerometers and magnetometers. The geomagnetic azimuth solution is susceptible to magnetic interference, especially strong magnetic interference and so a rotary norm constraint filtering (RNCF) method for azimuth estimation, designed to support a gyroscope-aided magnetometer-based MWD system, is proposed. First, a new magnetic dynamical system, one whose output is observed by the magnetometers triad, is designed based on the Coriolis equation of the desired geomagnetic vector. Second, given that the norm of the non-interfered geomagnetic vector can be approximated as a constant during a short-term drilling process, a norm constraint procedure is introduced to the Kalman filter. This is achieved by the normalization of the geomagnetic part of the state vector of the dynamical system and is undertaken in order to obtain a precise geomagnetic component. Simulation and actual drilling experiments show that the proposed RNCF method can effectively improve the azimuth measurement precision with 98.5% over the typical geomagnetic solution and 37.1% over the KF in a RMSE sense when being strong magnetic interference environment. Full article

In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data. Full article

MPPM (Multipulse Position Modulation) is a pulse code technology that is widely used in optical communication and deep space communication due to its high energy efficiency. Combination code is a variation of MPPM and is widely used in MWD (Measurement While Drilling) telemetry systems. However, whether it is MPPM or combination code, it is difficult to establish a simple mapping relationship between bit symbols and pulse symbols. Therefore, an encoding table is usually used to realize the mapping between bit symbols and pulse symbols. With an increase in bit width, the storage complexity of the encoding table increases exponentially. This is unacceptable for underground transmitters working in harsh environments. This paper maps the code symbols of MPPM to a hyper-triangular region of a high-dimensional tensor and establishes a simple lookup table and lookup algorithm to achieve low-complexity mapping from bit symbols to MPPM code symbols. At the same time, this paper also provides a method for extending from MPPM code symbols to commonly used combination code symbols in MWD, thereby providing an efficient implementation path for large bit width combination code encoding at the underground end of MWD. Full article