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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.

Discontinuous structures in coal mine roadway roofs, such as rock interfaces and joint fractures, are critical factors leading to surrounding rock instability. The use of Measurement While Drilling (MWD) technology to identify geological formations has become a growing trend. However, there is still a lack of rock structure recognition methods that offer high accuracy, efficiency, and strong generalizability. Therefore, this study acquired four drilling parameters including thrust, torque, modulation specific energy (SEM), and rock drillability assessment (RDA) through drilling experiments. By leveraging the Bayes algorithm, which has high precision, efficiency, and low cost, a change point detection model for drilling parameters was established, and a multi-parameter fusion criterion was proposed for identifying rock structures. The results show that for single rock interface identification, the errors of thrust, torque, SEM, and RDA were 13.3 mm, 4.6 mm, 4.4 mm, and 18.3 mm, respectively. For multiple rock interface identification, the recognition rates were 83.3%, 100.0%, 66.7%, and 83.3%, respectively. Moreover, the absolute value of the magnitude index (SLP) at the interface location was generally the highest among all change points. In multi-change-point detection, the SLP threshold should be set at ± 0.2. It is worth noting that the SLP value is correlated with data fluctuation intensity; greater fluctuation leads to higher SLP values at change points. This study contributes significantly to enabling intelligent perception of rock structures and improving the quality of rock mass control.

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.

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 = Tp|(dF/dT) = 0 ± σ and CIM = xli ± 0.5∆xi. 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.

A tunnelling project is normally initiated with a site investigation to determine the in situ rock mass conditions and to generate the basis for the tunnel design and rock support. However, since site investigations often are based on limited information (surface mapping, geophysical profiles, few bore holes, etc.), the estimation of the rock mass conditions may contain inaccuracies, resulting in underestimating the required rock support. The study hypothesised that these inaccuracies could be reduced using Measurement While Drilling (MWD) technology to assist in the decision-making process. A case study of two tunnels in the Stockholm bypass found the rock mass quality was severely overestimated by the site investigation; more than 45% of the investigated sections had a lower rock mass quality than expected. MWD data were recorded in 25 m grout holes and 6 m blast holes. The MWD data were normalised so that the long grout holes with larger hole diameters and the shorter blast holes with smaller hole diameters gave similar results. With normalised MWD data, it was possible to mimic the tunnel contour mapping; results showed good correlation with mapped Q-value and installed rock support. MWD technology can improve the accuracy of forecasting the rock mass ahead of the face. It can bridge the information gap between the early, somewhat uncertain geotechnical site investigation and the geological mapping done after excavation to optimise rock support.

In this paper, the response characteristics of parameters along the borehole while drilling to rock mass interface are experimentally studied by drilling method. Using the developed measurement while drilling (MWD) system, drilling experiments were carried out on granite, limestone, and sandstone composite rock masses, and real-time monitoring of parameters while drilling, i.e., rotary speed, drilling depth, feed force, and drilling torque, were carried out during the drilling process. The results show that the developed measurement system operates stably and monitors well, and the response characteristics of drilling speed, feed force, and drilling torque at the rock layer interface are obvious, which lays a foundation for the identification of lithology while drilling. There is no obvious change in the rotary speed while drilling, which can be set as a constant. The rate of penetration is the most sensitive parameter while the rock structure changes, followed by the feed force. With the increase of the rotary speed, the sensitivity of the feed force to the response of the rock structure interface will increase accordingly. The drilling torque fluctuates greatly in the rock structure, and the bit torque at different rotary speeds shows different variation laws in the drilling process of different rock structures, but it still has a clear response to the rock stratum interface. The comprehensive analysis of multiple sets of parameters shows that the use of parameters along the borehole while drilling can effectively realize the accurate identification of the layered rock mass interface.

Blasting performance is influenced by mechanical and structural properties of the rock, on one side, and blast design parameters on the other. This paper describes a new methodology to assess rock mass quality from drill-monitoring data to guide blasting in open pit operations. Principal component analysis has been used to combine measurement while drilling (MWD) information from two drill rigs; corrections of the MWD parameters to minimize external influences other than the rock mass have been applied. First, a Structural factor has been developed to classify the rock condition in three classes (massive, fractured and heavily fractured). From it, a structural block model has been developed to simplify the recognition of rock classes. Video recording of the inner wall of 256 blastholes has been used to calibrate the results obtained. Secondly, a combined strength-grade factor has been obtained based on the analysis of the rock type description and strength properties from geology reports, assaying of drilling chips (ore/waste identification) and 3D unmanned aerial vehicle reconstructions of the post-blast bench face. Data from 302 blastholes, comprised of 26 blasts, have been used for this analysis. From the results, four categories have been identified: soft-waste, hard-waste, transition zone and hard-ore. The model determines zones of soft and hard waste rock (schisted sandstone and limestone, respectively), and hard ore zones (siderite rock type). Finally, the structural block model has been combined with the strength-grade factor in an overall rock factor. This factor, exclusively obtained from drill monitoring data, can provide an automatic assessment of rock structure, strength, and waste/ore identification.

In tunneling, rock mass grouting is a method applied to reduce water ingress. Grouting is influenced by rock mass conditions, especially apertures, frequency, and continuation of fracturing. These rock mass conditions can partly be determined by rock mass classification systems. At the Stockholm bypass, the Measurement While Drilling (MWD) Fracturing Index was applied to characterize the rock mass for grouting purposes, with a focus on adjusting the grout hole drill plan to minimize environmental impact. This study divided the rock mass in a 1.9 km tunnel into six categories based on rock mass conditions, identifying rock mass quality, apparent fracturing, and grout consumption. These categories were then compared with the mean fracturing index based on the coefficients of penetration rate and rotation pressure variations, as well as grout consumption at each grout umbrella. The fracturing index was 93% successful in assessing favorable and unfavorable rock mass conditions in the studied tunnel and 85% successful in determining grout consumption. Finally, a conceptual method was developed to reduce the grouting activities using the MWD fracturing index and water loss tests. The introduction of this conceptual method for grouting decisions could potentially reduce 59% of the umbrellas found in the case study.

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.