Search Results (17)

With the in-depth application of digital transformation in the oil industry, data-driven methods provide a new technical path for drilling engineering safety evaluation. In this paper, a data-driven drilling safety level evaluation system is proposed. By integrating the three-dimensional visualization technology of wellbore trajectory and the prediction model of friction torque, a dynamic and intelligent drilling risk evaluation framework is constructed. The Python platform is used to integrate geomechanical parameters, real-time drilling data, and historical working condition records, and the machine learning algorithm is used to train the friction torque prediction model to improve prediction accuracy. Based on the K-means clustering evaluation method, a three-tier drilling safety classification standard is established: Grade I (low risk) for friction (0–100 kN) and torque (0–10 kN·m), Grade II (medium risk) for friction (100–200 kN) and torque (10–20 kN·m), and Grade III (high risk) for friction (>200 kN) and torque (>20 kN·m). This enables intelligent quantitative evaluation of drilling difficulty. The system not only dynamically optimizes bottom-hole assembly (BHA) and drilling parameters but also continuously refines the evaluation model’s accuracy through a data backtracking mechanism. This provides a reliable theoretical foundation and technical support for risk early warning, parameter optimization, and intelligent decision-making in drilling engineering. Full article

Impact Drilling Technology is one of the most effective methods for enhancing the penetration rate and efficiency in hard rock formations. Downhole axial vibration impact tools can provide a stable impact load, but they also increase the complexity of the Bottom Hole Assembly (BHA) motion. Addressing the problem of vibration fatigue in the lower BHA when subjected to high-frequency impact stresses during impact drilling, this study utilizes finite-element impact modules and Design-Life fatigue analysis software to establish a nonlinear dynamic model of the drill string assembly under axial excitation. It investigates the influence patterns of control parameters, such as the impact energy and impact frequency, on BHA vibration damage and rock-breaking efficiency. The results show that the vibration characteristics of the BHA are significantly affected by the impact tool’s control parameters. Increasing the input impact energy intensifies the amplitude of alternating stress in the drill string system. Meanwhile, the equivalent stress fluctuation of the drill string tends to stabilize at high frequencies above 100 Hz, indicating that high-frequency impacts are beneficial for mitigating vibration damage and prolonging the service life of the BHA. This study provides a theoretical basis for reducing the drill string fatigue damage and optimizing the drilling parameters for an improved performance. Full article

The rate of penetration (ROP) is the key parameter to enhance drilling processes as it is inversely proportional to the overall cost of drilling operations. Maximizing the ROP without any limitation can induce drilling dysfunctions such as downhole vibrations. These vibrations are the main reason for bottom hole assembly (BHA) tool failure or excessive wear. This paper aims to maximize the ROP while managing the torque to keep the depth of cut within an acceptable range during the cutting process. To achieve this, machine learning algorithms are applied to build ROP and drilling torque models. Then, a metaheuristic algorithm is used to determine the optimal technical control parameters, the weight on bit (WOB) and revolutions per minute (RPM), that simultaneously enhance the ROP and mitigate excessive vibrations. This paper introduces a new methodology for mitigating drill string vibrations, improving the rate of penetration (ROP), minimizing BHA failures, and reducing drilling costs. Full article

During the drilling process, stick–slip vibrations are one of the critical causes of bottom-hole assembly (BHA) failure and reduced drilling efficiency. To address this, this study first proposes a drill-string model based on a three-dimensional nonlinear finite beam element, combined with Hamilton’s principle of virtual work, to comprehensively describe the nonlinear behavior of the drill-string system. Next, to improve computational efficiency, the model is reduced using the modal truncation method, which retains the key modes of drill-string vibrations. Based on this, a model predictive control (MPC) method is designed to eliminate stick–slip vibrations. Furthermore, the robustness of the MPC method under parameter uncertainties is also investigated. In particular, the impact of the weight on bit (WOB) on the drill bit’s torsional velocity is further considered, and an MPC angular velocity comprehensive control scheme based on the dynamic WOB (DWOB-MPC) is proposed. This scheme stabilizes the velocity of the drill bit by dynamically adjusting the WOB, thereby eliminating stick–slip vibrations. Simulation results demonstrate that both the proposed MPC and DWOB-MPC methods effectively suppress stick–slip vibrations. Notably, the DWOB-MPC method further reduces the settling time and overshoot, exhibiting superior dynamic performance. Full article

Real-time drilling analysis requires knowledge of lithology at the drill bit. However, logging-while-drilling (LWD) sensors in the bottom hole assembly (BHA) are usually positioned 2–50 m (7–164 ft) above the bit (called the sensor offset), leading to a delay in real-time drilling analysis. The current industry solution to overcome this delay involves stopping drilling to perform a bottoms-up circulation for cuttings evaluation—a process that is both time-consuming and costly. To address this issue, our study evaluates three methodologies for real-time lithology prediction at the bit using drilling and petrophysical parameters. The first method employs a petrophysical approach, which involves using bulk density and neutron porosity predicted at the bit. The second method combines unsupervised and supervised machine learning (ML) for prediction. The third method employs classification algorithms on manually labeled lithology data from mud log reports, a novel approach used in this work. Our results show varying degrees of success: the bulk density versus neutron porosity cross-plot method achieved an accuracy of 58% with blind-well test data; the ML approach improved accuracy to 66%; and the Random Forest (RF) classification with manual labeling significantly increased accuracy to 86%. This comparative analysis of three different methodologies for lithology prediction has not been previously explored in the literature. While clustering and classification methods have been regarded as the most effective, our study demonstrates that they do not always yield the best result. These findings demonstrate that ML models, particularly the manual labeling approach, substantially outperform the petrophysical method. This new algorithm, designed for real-time applications, uses selected input parameters to effectively minimize problems associated with the sensor offset of LWD tools. It rapidly adapts to changes, offering a quicker and more cost-effective interpretation of lithology. This eliminates the need for time-consuming bottoms-up circulation to evaluate cuttings. Ultimately, this approach enhances drilling efficiency and significantly improves the accuracy of lithology prediction, notably in identifying interbedded geological layers. Full article

The transient temperature of the wellbore plays an important role in the selection of downhole tools during the drilling of deep shale gas horizontal wells. This study established a transient temperature field model of horizontal wells based on the convection heat transfer between wellbore and formation and the principle of energy conservation. The model verification shows that the root mean squared error (RMSE) between the measured annular temperature neat bit and the predicted value is 0.54 °C, indicating high accuracy. A well in Chongqing, China, is taken as an example to study the effects of bottom hole assembly (BHA), drill pipe size, drilling fluid density, flow rate, inlet temperature of drilling fluid, and drilling fluid circulation time on the temperature distribution in wellbore annulus. It is found that the increase in annular temperature is about 1 °C/100 m in the horizontal section when a positive displacement motor (PDM) is used. A Φ139.7 mm drill pipe is more favorable for cooling than Φ139.7 mm + Φ127 mm drill pipe. Reducing drilling fluid density and flow rate and inlet temperature is beneficial to reduce bottom hole temperature. Bit-breaking rock, bit hydraulic horsepower, and drill pipe rotation will increase the bottom hole temperature. The research results can provide theoretical guidance for temperature prediction, selection of proper drill tools, and adjustment of relevant parameters in deep shale gas horizontal wells. Full article

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling’s stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements. Full article

The critical factors that affect bottomhole stick–slip vibrations during vertical drilling operations are thoroughly investigated and analyzed in this research. Influential factors, such as rotation speed, weight on bit (WOB), bottom hole assembly (BHA) configuration, and formation properties, were studied in order to understand their part in the stick–slip phenomena. The analysis is based on a thorough review of previous research conducted on stick–slip drilling vibrations. A mathematical model was created that not only explains axial vibrations but also includes the torsional vibrations present in stick–slip occurrences, which helps with understanding the stick–slip phenomena better. This model can be used as an analytical tool to predict and evaluate the behavior of drilling systems under various operational circumstances. Furthermore, two drilling tests using a WellScan simulator were performed to validate the research findings and assess mitigation techniques’ viability. These test scenarios reflect the stick–slip vibration-producing situations, allowing us to test mitigation strategies. The finding of this study shows the effectiveness of two tactics for reducing stick–slip vibrations. First was the reduction of WOB, which successfully lowered the occurrence of stick–slip vibrations. The second was the increase in the rotation speed, which helped to control the stick–slip problem and increased the drilling speed. This study explains the complex dynamics of stick–slip vibrations during vertical drilling and offers practical, tried-and-true methods for reducing their adverse effects on drilling operations. Full article

Well deviation is a prevalent problem in deep oil and gas exploration, leading to a significant increase in drilling costs. The conventional bottom-hole assembly (BHA) anti-deviation design method does not consider the impact of the BHA structure on lateral vibration. This paper proposes an integrated BHA design method that takes into account both anti-deviation and vibration reduction. This method evaluates the BHA’s anti-deviation ability using the drilling trend angle. A negative value of the drilling trend angle indicates that the BHA can correct well deviation. A finite element linearized dynamics method is used to evaluate the lateral vibration intensity of the BHA. This method involves calculating the bending displacement caused by mass imbalance and then determining the magnitude of the bending strain energy based on this displacement. The structural factors affecting the anti-deviation ability and potential lateral vibration intensity of pendulum BHAs and bent-housing mud motor (BHMM) BHAs were studied, and field tests were conducted for verification. The research shows that for pendulum BHAs, the factor that has the greatest impact on anti-deviation ability and vibration intensity is the distance from the stabilizer to the drill bit. For BHMM BHAs, the length of the short drill collar has a significant impact on the vibration intensity. Compared with current design methods, the mechanical specific energy (MSE) of the single stabilizer pendulum BHA decreased by 12%, while the MSE of the BHMM BHA decreased by 26.4%. Both decreases indicate a reduction in vibration intensity. This study will help to further increase drilling speed while preventing well deviation. Full article

In the process of dynamic Measurement While Drilling (MWD), the strong vibration and rapid rotation of the Bottom Hole Assembly (BHA) lead to multi-frequency and high-amplitude noise interference in the attitude measurement signal. The weak original signal and extremely low signal-to-noise ratio (SNR) are always the technical difficulties of dynamic MWD. To solve this problem, this paper uses the chaotic effect of the Duffing system, which takes the expression (−x³ + x⁵) as a nonlinear restoring force to detect the weak characteristic signal of downhole dynamic MWD. First of all, in order to meet the limit condition of the chaotic phase transition of the system output, the frequency value of the characteristic signal is reconstructed and transformed based on the variable scale theory. Then, in order to solve the influence of the initial phase of the characteristic signal on the detection accuracy, a detection model based on the array Duffing system is presented, and a frequency-detection scheme with all-phase coverage is given. Finally, another array Duffing system is designed for the parameter estimation of the characteristic signal. The critical value of chaotic phase transition is determined by adjusting the amplitude of the driving signal of the array Duffing system, and then the amplitude and phase parameters of the characteristic signal are synchronously estimated. The experimental results show that the proposed method can effectively extract the weak characteristic signal within the strong noise, and the SNR of the characteristic signal can be as low as −21 dB. Through the attitude calculation for the extracted characteristic signal, it can be seen that the proposed method can improve the accuracy of the inclination of the drilling tool significantly, which proves the feasibility and effectiveness of the method proposed in this paper. Full article

This article is devoted to the development of technology for improving the efficiency of directional well drilling by predicting and adjusting the system of static and dynamic components of the actual weight on the bit, based on the real-time data interpretation from telemetry sensors of the bottom hole assembly (BHA). Studies of the petrophysical and geomechanical properties of rock samples were carried out. Based on fourth strength theory and the Palmgren–Miner fatigue stress theory, the mathematical model for prediction of effective distribution of mechanical specific energy, using machine learning methods while drilling, was developed. An algorithm was set for evaluation and estimation of effective destruction of rock by comparing petrophysical data in the well section and predicting the shock impulse of the bit. Based on the theory provided, it is assumed that the given shock impulse is an actual representation of an excessive energy, conveyed to BHA. This excessive energy was quantitively determined and expressed as an adjusting coefficient for optimal weight on bit. The developed mathematical and predictive model helps to identify the presence of ineffective rock destruction and adjust drilling regime accordingly. Several well drilling datasets from the North Sea were analyzed. The effectiveness of the developed mathematical model and algorithms was confirmed by testing well drilling data. Full article

The strengths and torque of well-boiling hydrocarbons are of utmost significance. Boiling a well is one of the most critical steps in the discovery and production of oil and gas. The well’s boiling process is expensive because the drilling depth can be as much as 7000 meters. Any delay (breakdown time) in boiling costs a lot of money for hydrocarbon firms. Various boiler parameters are continuously tracked and regulated to avoid drilling delays. This paper focuses on the vibrations occurring at the bottom hole assembly (BHA) stick-slip. Two modelling methods, the lumped parameter model and the combination of the distributed–lumped (D–L) parameter model, were used and compared to the actual measurement performance. The D–L model was found to be more precise, particularly for long strings. Using the simulations, the most comprehensive modelling methodology is introduced. Full article

The RSS has been widely used in directional drilling. In order to enhance wellpath control accuracy and efficiency of the static push-the-bit RSS, a powerful BUR prediction method is reconstructed by coupling a rotary steerable bottom-hole assembly (RSBHA) mechanical model and a drill bit–rock interaction model. This article showed that when establishing the RSBHA mechanical model by using the continuous beam column method, the steering rib should be treated as an eccentric stabilizer to consider the contact effect between the steering rib and wellbore wall. For the beam column containing the flexible sub and between two stabilizers, it should be rearranged into three beam columns, and the lower and upper steps of the flexible sub should be considered as virtual supports. The equilibrium tendency method (ETM) to predict the BUR can enhance wellpath prediction accuracy than those of traditional methods. Under 3D conditions, the total drilling tendency angle should be denoted by inclination tendency angle and azimuth tendency angle to enhance the solution efficiency. Case analyses have verified that the average forecast error of the BUR prediction model in this article is less than 1°/30 m. Full article

Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical. Full article

Motion states of bottom hole assembly (BHA) have a great effect on the trajectory control and drilling efficiency while rotary drilling. In order to study the motion states of BHA in horizontal wells, a BHA dynamic model with the finite element method was established. Meanwhile, an indoor experimental setup based on similarity criterion was designed and built to verify the numerical simulation results. Finally, the effects of measuring positions, rotate speeds, weight on bit (WOB), and friction coefficients on the motion states were analyzed in numerical simulation and experiment. The results show that the experimental results can match well with the numerical simulation results. The motion states of BHA in horizontal wells can be divided into three kinds, including circular arc swing, "8" shape swing, and dot-like circular motion. The circular arc swing mainly appears at middle section of BHA and occurs through the collective result of gravity and tangential friction. The dot-like circular motion mainly appears at near-bit or near-stabilizer area because drill bit and stabilizer can steady the BHA at the center part of the wellbore. The "8" shape swing mainly appears at the crossed area and occurs through collective disturbance of the other two motions. Moreover, rotate speed and friction coefficient have promotions on the lateral vibration while WOB have a much smaller effect. Through analyses, related suggestions are given for the drilling engineering. The related conclusions and suggestions in this paper can help to further understand the lateral dynamic characteristics of BHA in horizontal wells and select suitable parameters for drilling engineering. Full article