Measurement While Drilling Research Articles

A comparative study on machine learning approaches for rock mass classification using drilling data

Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable metrics for rock engineering. It seeks to link data to specific engineering actions, thus providing critical decision support for geological challenges ahead of the tunnel face. Leveraging a large and geologically diverse dataset of ∼500,000 drillholes from 15 tunnels, the research introduces models for accurate rock mass quality classification in a real-world tunnelling context. Both conventional machine learning and image-based deep learning are explored to classify MWD data into Q-classes and Q-values—examples of metrics describing the stability of the rock mass—using both tabular- and image data. The results indicate that the K-nearest neighbours algorithm in an ensemble with tree-based models using tabular data effectively classifies rock mass quality. It achieves a cross-validated balanced accuracy of 0.86 in classifying rock mass into the Q-classes A, B, C, D, E1, E2, and 0.95 for a binary classification with E versus the rest. Classification using a CNN with MWD-images for each blasting round resulted in a balanced accuracy of 0.82 for binary classification. Regressing the Q-value from tabular MWD-data achieved cross-validated R2 and MSE scores of 0.80 and 0.18 for a similar ensemble model as in classification. High performance in regression and classification boosts confidence in automated rock mass assessment. Applying advanced modelling on a unique dataset demonstrates MWD data's value in improving rock mass classification accuracy and advancing data-driven rock engineering design, reducing manual intervention.

Real-time lithology identification from drilling data with self & cross attention model and wavelet transform

Accurate lithology identification during drilling operations is pivotal for optimizing drilling efficiency and informed decision-making. Traditional methods, such as mud logging, Logging While Drilling (LWD), and Measurement While Drilling (MWD), often face challenges such as delays and inaccuracies, lacking consistent utilization of real-time engineering data across all drilling sites. Consequently, these methods frequently overlook the complex interactions of influencing factors, resulting in underutilized geological information that could significantly enhance operational outcomes. This paper introduces the SelfAttention-CrossWT (SACWT) model, an innovative approach that integrates self-attention and cross-attention mechanisms with Wavelet Transform (WT) to utilize real-time drilling parameters, including Weight on Bit (WOB) and Rate of Penetration (ROP). The SACWT model effectively harnesses both temporal and frequency-domain characteristics of drilling data, facilitating comprehensive extraction of latent patterns and dynamics, which substantially improves lithology identification accuracy. The output of the model is the real-time lithology classification at the drill bit's depth. The performance of the SACWT model is compared with Long Short-Term Memory (LSTM) networks and a Self-Attention (SA) model that does not utilize frequency-domain data. When applied to a dataset from the Volve oil field, the SACWT model achieved identification accuracies of 87% and 91% in two separate test wells, outperforming traditional methods. These results underscore the model's efficacy and the significant potential of utilizing real-time drilling data for enhanced lithological characterization. This breakthrough offers a robust framework for future exploration and development operations, providing a more precise, efficient, and cost-effective approach to geological analysis.

Anti-Vibration Method for the Near-Bit Measurement While Drilling of Pneumatic Down-the-Hole Hammer Drilling

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.

Uniaxial compressive strength prediction based on measurement while drilling data: A laboratory study

The uniaxial compressive strength is one of the most important basic parameters of rock, which is essential for surrounding rock stability analysis and support scheme design of underground engineering. At present, it is time consuming and costly to take a large amount of core samples for laboratory testing, and the mechanical properties of the cores may be affected by mining disturbance, which can easily lead to inaccurate result. The measurement while drilling (MWD) technology provides a new approach to solve the above challenge. The key to implementing this technology is to establish a correlation model between drilling parameters and rock mechanics parameters. Based on the characteristics of polycrystalline diamond compact (PDC) bits in drilling and rock breakage, this paper analyzes the mechanical state of the bit in breaking rock. A theoretical correlation model between the torque, feed force of the bit and the uniaxial compressive strength of the rock has been developed. To verify the accuracy of the theoretical model, the uniaxial compressive strength of five different types of rocks (red sandstone, green sandstone, limestone, marble and shale) was obtained through laboratory mechanical tests. The torque Mb, feed force Fb and other parameters in the drilling process of these five rocks were tested through the newly developed MWD test system. The correlation between the drilling parameters and the uniaxial compressive strength of rock was established. The results showed that the feed force Fb and torque Mb measured at five different types of rocks indicate a linear increasing trend with an increase in depth of cut h. Meanwhile, a strong linear relationship between the feed force Fb and torque Mb is evident. This paper proposes an MWD-based method to rapidly conduct the in-situ measurement of the uniaxial compressive strength of various rocks.

Research on Surface Processing Method of Pulse Transmission Signal of Amplitude-Modulated Drilling Fluid in 10,000-m Deep Wells

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.

Production processes optimization through machine learning methods based on geophysical monitoring data

The purpose of the article is to create an effective method for low-delay monitoring of the operating state of a drill string and a drill bit without interfering with the proper drilling process. For the drilling process to be continuously controlled, an experimental setup that operates by utilizing the phase-metric method of control was developed. Any movement of the bit causes a change in the electrical characteristics of the probing signal. To obtain a stable signal from a bit immersion depth of up to 250 m, a frequency of probing electrical signals of 166 Hz and an amplitude of up to 500 V were used; the sampling rate of an analog-to-digital converter (ADC) was 10101 Hz. To identify the state of the drill string and the bit based on graphs of time-dependences of changes in the probing signal electrical characteristics, the present authors investigated a number of deep learning methods. Based on the results of the study, a series of capsular neural network methods ( CapsNet ) was chosen. The authors developed modifications of 2D-CapsNet: windowed Fourier transform (WFT) - 2D-CapsNet and frequency slice wavelet transform (FSWT) - 2D-CapsNet. Both of these methods showed a 99% accuracy in determining the transition between two layers of rocks with different properties, which is 2–3% higher than the currently used measurement-while-drilling (MWD) and logging-while-drilling (LWD) rock surveys. Both of these methods unambiguously reveal self-oscillations in the drill string. When determining a fully serviceable bit in the case of self-oscillations, the (FSWT) - 2D-CapsNet method showed an accuracy of 99%.

Semi-supervised recognition of tunnel surrounding rock discontinuities using drilling jumbo data

Geological information ahead of the tunnel face is vital for ensuring safe and efficient tunnel construction. This paper presents a semi-supervised approach that combines a constrained dense convolutional autoencoder (CDAE) with a hybrid deep neural network (HDNN) to predict rock mass discontinuities. The factors related to rock mass discontinuity are categorized into four main classes: geometry, geology, construction, and measurement while drilling (MWD). The geological similarity between neighbouring blastholes is taken into consideration to constrain the training process of CDAE. These four categories of data serve as input parameters for CDAE-HDNN to predict rock mass discontinuity. Data from the Yangjiawopu tunnel is employed to assess the performance and generalization capability of the proposed method. The results show that CDAE can extract features related to rock mass discontinuity, and the proposed model exhibits superior performance and robustness compared to conventional deep learning and machine learning models.

Estimation of tunnel in-situ stress magnitude and direction using measurement while drilling data and acoustic wave information

The rapid determination and analysis of in-situ stress are crucial for ensuring the stability and design integrity of underground engineering endeavours. This study introduces an innovative approach to swiftly estimate tunnel in-situ stress by leveraging drilling jumbo Measurement While Drilling (MWD) data in conjunction with acoustic wave information. Initially, a quantitative model linking MWD data to the second-order elastic modulus is established through a mechanical model of the drilling jumbo’s percussion and rotation for rock fracturing, coupled with Hertzian elastic contact theory. Subsequently, a field-measurement scheme for acquiring acoustic wave information within the tunnel is devised, and an estimation method is devised to determine the magnitude and direction of in-situ stress by fitting the wave velocity ellipsoid. Twenty comparative tests across various tunnel sections were conducted to validate the approach. The measurement errors for maximum horizontal principal stress, minimum horizontal principal stress, and direction of the maximum horizontal principal stress were found to be 3.81 MPa, 2.82 MPa, and 11°, respectively, with average errors of 2.16 MPa, 1.61 MPa, and 5.6°. These findings affirm the efficacy of the proposed method in accurately characterizing the distribution of in-situ stress magnitude and direction.

Dynamic Numerical Simulation and Transfer Learning-Based Rapid Rock Identification during Measurement While Drilling (MWD)

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.

Rapid profiling rock mass quality underneath tunnel face for Sichuan-Xizang Railway

The Sichuan-Xizang Railway is a global challenge, surpassing other known railway projects in terms of geological and topographical complexity. This paper presents an approach for rapidly profiling rock mass quality underneath tunnel face for the ongoing construction of the Sichuan-Xizang Railway. It adopts the time-series method and carries out the quantitative analysis of the rock mass quality using the depth-series measurement-while-drilling (MWD) data associated with drilling of blastholes. A tunnel face with 15 blastholes is examined for illustration. The results include identification of the boundary of homogeneous geomaterial by plotting the blasthole depth against the net drilling time, as well as quantification of rock mass quality through the recalculation of the new specific energy. The new specific energy profile is compared and highly consistent with laboratory test, manual logging and tunnel seismic prediction results. This consistency can enhance the blasthole pattern design and facilitate the dynamic determination of charge placement and amount. This paper highlights the importance of digital monitoring during blasthole drilling for rapidly profiling rock mass quality underneath and ahead of tunnel face. It upgrades the MWD technique for rapid profiling rock mass quality in drilling and blasting tunnels.

RESEARCH INNOVATIONS IN DIRECTIONAL AND HORIZONTAL DRILLING AND SIDETRACKING TECHNIQUES

Directional and horizontal drilling, and sidetracking techniques have become integral components of the oil and gas industry, which allows access to hydrocarbon reserves in challenging environments. This paper provides an overview of these drilling methods and explores recent trends in the development of technical equipment for horizontal drilling and sidetracking. Directional drilling allows for drilling at various angles, including horizontal and multilateral, providing access to reservoirs beneath the Earth's surface. This technique involves specialized equipment and precise control of the drill bit's direction and deviation, resulting in better reservoir management, increased productivity and reduced environmental effect. Horizontal drilling, on the other hand, involves drilling laterally through subsurface rock formations, particularly beneficial for accessing unconventional reservoirs (shale formations). Horizontal drilling allows operators to access more of the formation, resulting in increased productivity and reduced environmental footprint. Sidetracking techniques are employed to redirect the path of a wellbore, necessary for avoiding geological hazards, accessing untapped reservoirs, or bypassing damaged sections of the well. This process requires careful planning, specialized equipment, and skilled personnel to ensure successful execution while minimizing risks. The paper also discusses modern trends in the development of technical equipment for horizontal drilling and sidetracking technology. These include automation and robotics to improve drilling efficiency and safety, advancements in drilling fluids and measurement-while-drilling (MWD) technologies for better wellbore stability and real-time data acquisition, and innovations in extended reach drilling (ERD) tools and casing and cementing solutions. Additionally, there is ongoing development in sidetracking techniques to improve efficiency and reliability, such as whipstock and casing exit technologies. The Ultrashort-Radius Radial System (URRS) is highlighted as an advanced drilling technology, enabling access to reservoirs with restricted space or challenging geometries. Although challenges such as cost and adaptation to downhole conditions remain, ongoing research and development is aimed at overcoming these barriers and demonstrating the feasibility of innovative drilling technologies. Ultimately, these advances contribute to the industry's goal of maximizing hydrocarbon production while ensuring operational efficiency and environmental sustainability. Keywords: directional drilling, horizontal drilling, sidetracking techniques, technical equipment development, ultrashort-radius radial system, laser drilling.

Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques

Chemical X-ray fluorescence (XRF) analyses of drill cuttings and measurement-while-drilling (MWD) records were jointly collected in two production levels with different mineralogical characteristics of a narrow vein underground mine. More than 840 chemical samples and 23,000 MWD records with geological and geotechnical accompanying data constitute the database used in this study. A classification methodology and a site-specific model were developed following two approaches: a k-means clustering heuristic algorithm to define rock classes based on their physicochemical features, and a machine learning (ML) ensemble model trained with those classes to distinguish ore and waste as function of drilling parameters. Oversampling and under sampling techniques are required due to the unbalanced size of the rock classes. The prediction success rate (F1 score) is about 70% for the ore when associated with a high content of silica, and about 86% for the waste. When continuous drilling chips sampling is carried out, the operator bias is reduced and the F1 scores exceed 80% for all classes using different ML techniques. The study presents a complete analysis of the correlations between rock mineralogical characteristics and their mechanical response during drilling, highlighting the benefits that MWD-based information can bring to the analysis-while-drilling (AWD) for ore grading.

A New Workflow for Estimating Reservoir Properties With Gradient Boosting Model and Joint Inversion Using MWD Measurements

Triple-combo logs are important measurements for estimating geological, petrophysical, and geomechanical properties. Unfortunately, wireline and advanced logging-while-drilling (LWD) logs are typically dropped from the formation evaluation plan for unconventional wells due to economic constraints or borehole instability risks. Available measurements are typically measurement-while-drilling (MWD) gamma ray (GR) logs, along with surface measurements such as weight on bit (WOB), rate of penetration (ROP), torque, rotation per minute (RPM), and differential pressure. The development of a robust and rapid model for predicting reservoir properties using this limited data set would be of high value for geological evaluation. Estimating such properties is a challenging task due to the nonlinear relationship between the available log data and unknown reservoir properties. A novel workflow that combines two sequential models is presented. First is a machine-learning (ML) algorithm to predict triple-combo logs from drilling dynamics and GR logs. To train the ML algorithm, well logs obtained from multiple wells located in the Eagle Ford and Permian Basins are scrutinized to identify important features. This process includes depth shifting, outlier detection, and feature selection, which allows for strategic hyperparameter tuning. Several regression algorithms are investigated, and it is found that gradient boosting algorithms yield superior prediction performance. Unlike random forest methods, boosting algorithms train predictors sequentially, each trying to correct its predecessor. After triple-combo logs are predicted from MWD logs, a physics-based joint inversion model is applied to estimate various reservoir properties. The trained model is deployed on a blind test well, and the predicted logs show excellent agreement compared to the corresponding triple-combo measurements. The multimineral inversion using predicted triple-combo logs yields a geologic model that is validated with elemental capture spectroscopy (ECS) measurements. Additionally, reconstructed logs from the forward model closely match measured logs by minimizing the cost function. Therefore, real-time estimated geological, petrophysical, and geomechanical properties can reveal complex geologic information and be used to mitigate uncertainty related to drilling optimization, reservoir characterization, development planning, and reserve estimation. Using the MWD logs to predict triple-combo logs followed by a joint inversion is an innovative approach for geological evaluation with a limited data set. The developed workflow can successfully provide (1) geologic lithofacies identification and rock typing, (2) more confidence in real-time drilling operation, (3) reservoir properties prediction, (4) missing log imputations and pseudo-log generation with forward modeling, (5) guidance for future logging and perforation, (6) reference for seismic quantitative interpretation (QI) and well tie, and (7) potentially massive computation time saving from days to minutes.

Profiling of weathered argillaceous limestone rock with MWD data from advanced drilling for tunnelling along Wu-Kai expressway in Chongqing, China

Advanced drilling was employed for the ongoing excavation of a tunnel on Wu-Kai Expressway in Chongqing, aiming to characterize the ground condition ahead of the tunnel face and avoid potential geohazards. Measurement While Drilling (MWD) system and the hydraulic rotary crawler drilling machine are used for the advanced drillhole in the weathered argillaceous limestone rock mass. However, the MWD data exhibits random variations in penetration rate and cannot be correlated with ground conditions. In addition, there is little MWD data on the weathered argillaceous limestone with time-series algorithm in relevant open literature. This paper presents further analysis of the MWD data, resolves the random variation issue in penetration rate, and re-interprets the MWD data along three advanced drillholes in this tunnel project. The re-calculated penetration rate with the time-series algorithm for each drillhole is a stepped function of the drillhole depth. The constant penetration rates for the different homogeneous zones can be further correlated with the weathering degrees of the rock and compared with those from other five ground sites with different ground conditions in China and London. The findings of the paper can enhance the general applicability of MWD data for accurate profiling of weathered argillaceous limestone rock mass for safe tunnelling.

Calibration of accelerometer and magnetometer sensors of MWD systems in directional drilling application

Measurement while drilling (MWD) tools include accelerometers, magnetometers and temperature sensors. The accuracy of these sensors are highly important in measuring inclination and Azimuth angle parameters in a directional well. These factors can increase costs when drilling a directional well. During the past years, many efforts have been made to increase the accuracy of these tools. Accuracy of accelerometer and magnetometer sensors are influenced by scale, bias and non-orthogonal errors. Considering that MWD systems work in a wide range of temperatures (for example 0 °C–150 °C), the temperature parameter should also be added to the factors affecting the errors. Here, after making a sample of MWD tool, a method to calibrate the tool using mathematical model based on total field calibration algorithm, least square method and the transfer matrix is presented. Calibrated parameters at ambient temperature are calculated with acceptable speed. Then, a method for calculating temperature coefficients in the desired range is provided. Compared to the case where temperature coefficients are not considered, the obtained results show a significantly increased accuracy of Inclination and Azimuth parameters through the use of these coefficients.

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database.

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.

Enhancing Orebody Knowledge using Measure-While-Drilling Data: A Machine Learning Approach

Due to limited resource definition and grade control drilling, conventional bench-scale geochemical exploration is frequently plagued by uncertainty, resulting in diminished processing plant efficiencies. The Measure-While-Drilling (MWD) system comprises a set of sensors designed to gather data pertaining to the performance of mining blast hole drill rigs. Previous MWD research primarily relied on penetration rate to identify rock type and estimate grade. This investigation uses Data Fusion and Machine Learning (ML) algorithms to characterize geochemical orebody quality values, such as iron percentage, phosphorous, sulfur, alumina, and silica content, based on MWD data collected at the field-scale, thereby facilitating efficient mine planning and excavation. Feature importance algorithms identified novel significant MWD variables, such as force on bit and ratio of bit air pressure to penetration rate, which provide valuable insights into subsurface geochemical properties. The performance of various ML algorithms was compared, with the Random Forest algorithm demonstrating the highest coefficients of determination (up to 0.96), indicating accurate field or laboratory results prediction. As a result, this work demonstrates that MWD data can be used to obtain high-resolution orebody knowledge prior to mining, improving subsurface geochemistry prediction. This knowledge can optimize the excavation of high-grade material by minimizing dilution from lower-grade or waste rock entering the processing plant.

A Target-Aware Well Path Control Method Based on Transfer Reinforcement Learning

Summary In the actual construction process, well path control is a challenging task mainly due to the inevitable well deflection caused by geological factors, drilling tools as well as borehole enlargement. Most conventional well path control methods focus on elaborate mechanism model construction. The methods are typically constructed on the basis of certain constraints or assumptions, which reflect their limited ability to accurately capture the actual drilling process, low level of intelligence, poor anti-interference performance, and weak adaptive capacity. To address these challenges, this paper proposes a target-aware well path control method that integrates reinforcement learning and transfer learning. The proposed method employs a deep deterministic policy gradient model based on the prioritized experience replay mechanism and leverages transfer learning to accelerate model learning. This enables the construction of a target-aware well path adaptive control system with strong anti-interference capability. The proposed target-aware control method of well path based on reinforcement learning and transfer learning can accurately track the preset trajectory in diverse geological environments, reach the target area with high precision, and make reasonable trajectory optimization decisions with measurement while drilling (MWD) even when the target trajectory does not match the actual distribution of the reservoir. This approach exhibits excellent anti-interference and adaptive abilities.

Efficient Encoding Method for Combined Codes in the MWD Telemetry System

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.

High Temperature Inertial Navigation Sensors, Electronics and Packaging for reliable 300°C Operation

Conventional oil and gas drilling measurement while drilling (MWD) systems are typically designed to operate at temperatures <200°C. To increase the efficiency of drilling unconventional geothermal wells, downhole MWD tools are needed that can operate in the unique thermal challenges encountered in enhanced geothermal systems (EGS). GE demonstrated a 300°C downhole navigation system. The system utilizes GE’s internally developed state-of-the-art MEMS multiple ring Gyroscope (MRG), and a high temperature gyroscope control and readout application specific integrated circuit (ASIC). The MRG, ASIC and associated high temperature, high reliability packaging were integrated to demonstrate the performance and functionality of the gyroscope across the temperature range from room temperature to 300°C. Specialized long-term capable high temperature test platforms were developed to enable the characterization and testing of the gyroscope system and utilized to validate application-relevant lifetime capability.