Rotary Steerable System Research Articles

Improving the accuracy of dynamic inclination measurement by machine learning

With the diminishing availability of oil and gas resources, the Rotary Steerable System has become increasingly important. However, the vibrations and shocks during the drilling process pose challenges to the Measurement While Drilling. In recent years, the application of machine learning in the field of petroleum exploration has gradually expanded, especially in the estimation of geological parameters and lithology discrimination. However, there is still limited research on drilling tool attitude measurement. To address the above-mentioned issues, this study proposes a method that combines drilling tool attitude sensor data with artificial neural networks to improve the accuracy of dynamic inclination measurement. This method utilizes machine learning techniques, combining real-time z-axis acceleration signals and magnetic induction signals, and employs a deep learning model to invert the x and y-axis acceleration signals, thereby achieving high-precision measurement of dynamic inclination angles. Experimental results show that Long Short-Term Memory model, under simulated measurement conditions with different rotational speeds, yields dynamic inclination curve errors ranging from 0.4° to 0.7°, significantly reducing the errors compared to the original measurements. This method not only improves the accuracy of inclination angle measurement but also demonstrates strong adaptability to different rotational speeds, providing more accurate data support for drilling operations.

Polynomial filter-based adaptive fault-tolerant control for a class of nonlinear systems with multiplicative faults

In this article, the problem of fault-tolerant control is investigated for polynomial nonlinear systems (PNSs) subject to multiplicative faults and measurement noise. Firstly, a matrix Taylor expansion technique is applied to transform the PNS into a linear parameter varying system. Then the polynomial filter is constructed to get estimations of states and unmatched disturbances, which reduces the effect of measurement noise. A novel adaptive fault-tolerant controller is proposed to compensate for multiplicative faults and unmatched disturbances. Furthermore, the condition of the parameter-dependent linear matrix inequality (PDLMI) is deduced, which guarantees the boundedness of tracking errors and estimation errors, and the sum of squares (SOS) decomposition technique is utilized to solve PDLMI. Finally, the simulation of a rotary steerable drilling tool system confirms the feasibility of the proposed adaptive fault-tolerant control framework.

A Simultaneous Wireless Power and Data Transfer Method Utilizing a Novel Coupler Design for Rotary Steerable Systems

A Simultaneous Wireless Power and Data Transfer Method Utilizing a Novel Coupler Design for Rotary Steerable Systems

Observer-based adaptive fuzzy control of stabilized platform in rotary steerable system with input saturation and output constraint

This paper explores the control strategy for toolface tracking in the stabilized platform of a rotary steerable system, considering both input saturation and output constraint. To mitigate the impact of unknown friction torque and modeling error on the stabilized platform, we propose an adaptive fuzzy backstepping control approach. A fuzzy state observer is specifically devised to handle the uncertain state arising from unknown friction torque and modeling errors. Introducing an auxiliary system effectively compensates for input saturation, and the resolution of the output error constraint is achieved through the construction of a barrier Lyapunov function. Furthermore, employing the Lyapunov method establishes that all signals in the entire closed-loop control system are semi-globally uniformly ultimately bounded. Simulation results confirm the efficacy of the proposed control methodology.

Assisting Directional Drilling by Calculating a Safe Operating Envelope

Summary Nowadays, complex 3D trajectories are executed with a succession of circular arcs (CAs). Although they have constant curvature, their tool face is not constant. Consequently, directional drillers must adjust the tool face regularly to reach the target entry within its tolerances. This paper investigates the use of the constant curvature and constant tool face (CTC in short) curve as an alternative to the CA to assist the directional drilling work to reach the target entry within its boundaries. The problem is addressed by calculating a safe operating envelope (SOE) to reach the boundaries of the target entry and provide a tolerance window for the curvature and tool face to support directional drilling decisions. The target entry tolerance is discretized as a polygon. From the current bit position and its direction, the possible choices of curvatures and tool faces are obtained to reach the edges of the target entry shape. The SOE can be calculated with the CA or with the CTC curve. It is, therefore, possible to compare the advantages and disadvantages of both types of curves to attain the target entry and stay within its boundaries. The CA is shorter than the CTC curve. However, it requires adjusting the tool face during the navigation, which is not the case with the CTC curve. As a result, the directional driller can control the bottomhole assembly (BHA) direction such that the well lands within the target entry limits by using set points for tool face and curvature inside the calculated SOE. Furthermore, a new way to represent the SOE is introduced. It makes use of a 3D cylindrical representation where the curvature is mapped as the height of a cylinder, while the tool face corresponds to the azimuth in the cylindrical coordinate system, and the length is linked to the radial distance. This provides a visual aid to understand the SOE. Moreover, this visualization helps to appreciate the relationship between the initial bit location and direction in the construction of the SOE and how the margins increase in a particular manner as the bit approaches the target entry polygon. The CTC curve is the natural one followed by directional positive displacement motors (PDMs) or rotary steerable systems (RSS). Potentially, the CTC curve may be a more straightforward solution to automated directional drilling control because it is easier to be followed by both PDM and RSS.

Deep reinforcement learning attitude control of stabilized platform for rotary steerable system based on extended state observer

A Deep Deterministic Policy Gradient with Extended State Observer (DDPG_ESO) method is proposed to address control accuracy and robustness issues in the attitude control of stabilized platforms for rotary steerable systems. A nonlinear extended state observer is developed to estimate the total system disturbance, which is then integrated into a deep reinforcement learning framework to enhance the controller’s adaptive capabilities. The controller is trained using variable amplitude and frequency external disturbances to improve its ability to suppress these disturbances. The superiority of the DDPG_ESO controller is validated through simulation experiments, demonstrating rapid tracking of toolface angles, reduced overshoot, and maintained steady-state errors. Compared to Proportional Integral Derivative (PID), Active Disturbance Rejection Control (ADRC), and Deep Deterministic Policy Gradient (DDPG) controllers, the DDPG_ESO algorithm exhibits higher robustness and adaptability under varying operational conditions, effectively mitigating the impact of external disturbances and modeling errors. The results indicate potential for practical application in the drilling industry

Adaptive fixed-time fault-tolerant tracking control for rotary steerable drilling tool systems

In this paper, the problem of adaptive fixed-time fault-tolerant tracking control is investigated for rotary steerable drilling tool systems (RSDTSs). Markov jump system (MJS) is used to describe the RSDTS with varying parameters which are induced by the changing environment. By employing the smooth projection operator technique, adaptive laws are established to estimate the faults. Based on the fault compensation strategy, a new adaptive fixed-time fault-tolerant tracking control scheme is proposed to ensure that the RSDTS is globally stochastically practically fixed-time stable. In addition, to reduce the computational burden in the backstepping framework, the derivatives of the virtual control are directly derived using command filters. Finally, the experiment performed on the rotary steerable drilling tool systems prototype is exploited to demonstrate the feasibility and effectiveness of the proposed method.

Intermittent fault detection in nonstationary processes via a Wald‐based control chart

SummaryIn this article, the problem of intermittent fault (IF) detection is investigated for nonstationary processes in the multivariate statistics framework. By combining the moving window technique with maximum likelihood estimation (MLE), the moving window Wald‐based control chart is proposed to realize the detection of IFs in nonstationary processes. The computational efficiency and the convergence properties are discussed for the designed iterative algorithm of MLE. Then, necessary and sufficient conditions are presented to guarantee the detectability of IFs with the consideration of window lengths. Moreover, the alarm delays are analyzed for the appearance and disappearance of IFs. In virtue of the above analysis, the optimal window length is derived by minimizing the supremum of alarm delays. In order to estimate the time of IFs' appearance and disappearance, an algorithm is designed with the inspiration of simulated annealing strategy. Finally, a simulation on rotary steerable drilling tool system is provided to verify the effectiveness of the proposed method.

Sensor fault estimation based on ellipsoid bundles for dynamic point-the-bit rotary steerable systems

The dynamic point-the-bit rotary steerable system (DPRSS) achieves high-performance trajectory control in oil and gas exploitation. However, the sensor faults significantly degrade the reliability of the DPRSS. In this paper, the problem of sensor fault estimation for the DPRSS is investigated. Firstly, the DPRSS is modeled by a descriptor system with unknown but bounded uncertainties, which include measurement noises, model perturbations, and unknown inputs. Next, by integrating the decoupling technique, a sensor fault estimator is put forward based on the ellipsoid bundles. Besides, order reduction and bound calculation methods are proposed to derive intervals of the states and faults. Then, with a defined minimization criterion of the ellipsoid bundle, a sufficient condition is presented to design the parameters of the estimator. Finally, numerical simulations and experiments on a DPRSS prototype are provided to demonstrate the effectiveness of the proposed fault estimation method.

Prescribed-time fault-tolerant tracking control for Markov jump nonlinear systems based on saturation dynamics filters

This paper investigates the problem of prescribed-time fault-tolerant tracking control for a class of Markov jump nonlinear systems affected by actuator faults and input saturation. First, a new time-varying constraint function and an error transformation are invoked to transform the original prescribed-time tracking control problem into a tracking control problem with deferred constraint for tracking error. Furthermore, the parameter correction terms are designed, on which a novel Markov jump saturation dynamic filter is constructed to handle the unfavorable effect of input saturation. Meanwhile, the projection operator technique and the norm estimation methodology are integrated to devise adaptive laws to estimate the unknown actuator faults evolving in accordance with the Markov chain. A new prescribed-time fault-tolerant tracking control scheme is put forward that realizes the deferred constraint on the tracking error, which in turn ensures that the tracking error of the system converges to a prescribed accuracy within a prescribed-time. Finally, two simulation examples, one of which implements a prescribed-time fault-tolerant tracking control for rotary steerable drilling tool system, are presented to illustrate the effectiveness and practicality of the developed scheme.

Effectiveness of the method for selecting rotary-steerable systems based on the machine learning algorithm Random Forest Classifier

Relevance. Urgent need to consider and determine possible ways to use machine learning methods in drilling industry, since artificial intelligence is developing rapidly. Achieving this task will provide industrial enterprises with a huge competitive advantage and make an important contribution to the scientific community for its future research. This is emphasized by such regulations as the Decree of the President of the Russian Federation dated 10.10.2019 G. No. 490 "On the development of Artificial Intelligence in the Russian Federation" and "The National Strategy for the Development of Artificial Intelligence for the period up to 2030". Aim. To study the effectiveness of using the machine learning method Random Forest Classifier, to develop methods for selecting rotary-steerable systems, to consider the efficiency of machine learning to determine target parameters when solving the task assigned to it within the drilling industry and to determine the approximate amount of time that can be spent by the algorithm to work out a possible solution. Object. Random Forest Classifier machine learning method in the conditions of solving a problem from the drilling industry on the selection of an optimal rotary-steerable system for specifically specified conditions. Methods. The authors have performed two computational experiments using two computing and electronic machines, namely a laptop and a remote server, the prerequisite for which was the data collected and analyzed on the basis of the study of the scientific literature in the field of research. This article explores the possibility of using the machine learning method Random Forest Classifier, to optimize well construction, using the example of developing a method for selecting rotary-steerable systems. Computational experiments were performed on two computers using the Python programming language, version 3.8.10, as well as the following libraries: NumPy, Pandas, Scikit-learn. Results. The computational experiments carried out proved the ability of the considered method to solve the problems of choosing suitable drilling equipment, an example of which was rotary-steerable systems. This method is able to independently determine the dependencies necessary to perform the task and spends a small amount of time on this process. The totality of these conclusions makes it possible to unequivocally assert the expediency and necessity of developing new approaches to the use of machine learning methods in the drilling industry, as well as performing multiple scientific studies on the possibilities of using machine learning in well construction and analyzing their effectiveness, since this direction is advanced and can radically change existing ideas about the processes occurring during well drilling.

Research on the high temperature calibration of the electromagnetic damping generator applied to the rotary steerable system

The Electromagnetic Damping Generator (EDG) used for petroleum exploration is faced with the problem of high local temperature during its downhole work. To avoid the damage to its internal circuit caused by high temperature, the key EDG circuit is required to be heat-resistant based on the electromagnetic induction principle and the current heating effect. First of all, the working condition with the maximum heating power was figured out after studying the heating power characteristics of EDG. Then the multi-field coupling model of the temperature field, electromagnetic field and structure of the damper, as well as its iterative analytic model were established to identify the relationship between the temperature rise of the key location of the damper part and its working time in the downhole environment and the room-temperature experimental environment, which provided data support for EDG optimization design. Finally, a EDG prototype was developed, and the test bench was set up by replacing the load with resistors to verify the accuracy of the iterative analytical model. The results showed that the temperature value calculated by the model well fit the experimental value. Therefore, the downhole operation reliability of the EDG and the safety of corresponding bench tests are guaranteed, which has certain guiding significance for EDG optimization design and its experimental study.

Dynamic Event-Triggered Intermittent Fault Detection for Time-Varying Stochastic Systems

In this paper, the problem of intermittent fault (IF) detection is investigated for stochastic linear time-varying (LTV) systems using dynamic event-triggered methods. Using the nonuniform sampling approach, the event-triggered system is transformed into a time-varying system with varying sampling periods. Using the moving horizon estimation strategy, a new IF detection filter is designed to generate residual signals which are decoupled from event-triggered transmission errors and estimation errors. Moreover, an event-triggered IF detection algorithm is proposed such that the appearance time and disappearance time of IFs can be detected quickly for stochastic LTV systems. In order to analyze the detectability of IFs for systems with/without event-triggered cases, the concept of distinguishability is introduced for IFs. Sufficient conditions are derived to guarantee the detectability of IFs for LTV systems. Finally, an experiment concerning the rotary steerable drilling tool system is provided to illustrate the effectiveness of the proposed IF detection method.

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

AbstractIn this paper, the problem of fault prognosis is investigated for linear stochastic systems with intermittent faults (IFs) and strong noise. Different from permanent faults, the degradation state of IFs are related to the fault duration time, leading to difficulties in the first step of fault prognosis, that is, the health indicator (HI) extraction. Generally, HI extraction of IFs is achieved by fault detection techniques. Therefore, the proposed fault prognosis approach mainly covers the following steps. First, the HI and degradation model are introduced for linear stochastic systems with IFs. Then, considering dynamic characteristic of systems and influence of strong noise, an IF detection algorithm under strong noise is proposed using weighted moving average methods to extract the HI. Moreover, an alternative HI extraction approach is given by an HI estimation way, which avoids using fault detection techniques. Subsequently, the degradation model is fitted by virtue of HIs and the least square method, and the remaining useful life is predicted with the aid of the obtained model. Finally, an experiment concerning the rotary steerable drilling tool system is provided to verify the effectiveness of the derived results.

Investigation on fatigue life of push-the-bit rotary steerable bottom hole assembly with random bit-rock interaction

Rotary steerable systems are critical for challenging boreholes in deep-sea oil and gas drilling, where fatigue failure is the normal form of damage. A procedure with deterministic excitation and random excitation is presented in this paper to reliably predict the fatigue life of the bottom hole assembly. The initial defects, the Coulomb friction model, frictional contact between the drill bit and rock, and the stochastic rock-cutting process are included in the model. Monte Carlo approach is used to obtain the Statistical characteristics of the stochastic model. Based on the program mentioned above, influences of some parameters, such as rotational speed, weight on bit, steering force, and random excitation on torsional vibration characteristics, are investigated. Moreover, the rain-flow counting method is applied to reveal the dependency relationship between fatigue life and torsional vibration. It is found that random excitation can exacerbate the torsional vibration phenomenon and even significantly reduce the fatigue life. Finally, compared with the deterministic system, the sensitivity index coefficient of the fatigue life in the random excitation model is about 0.8 % - 2.2 % higher. The research results can provide a theoretical basis for reducing tool failure in on-site deep-sea oil and gas drilling.

Backstepping sliding mode control of stabilized platform for fully rotary steerable drilling system based on nonlinear disturbance observer

AbstractTo handle the disturbances and uncertainties issue in the control of the stabilized platform toolface angle, a composite control approach based on a backstepping sliding mode control (BSMC) method with a nonlinear disturbance observer (NDO) is proposed. First, a nonlinear mathematical model of the stabilized platform is established, and the NDO is employed to observe and compensate for the external disturbance. Then, the BSMC control approach based on the nonlinear mathematical model is designed to improve the robustness of the stabilized platform control system. The asymptotic stability of the designed controller and NDO convergence are mathematically verified using the Lyapunov theory. Furthermore, to address the dead zone torque caused by mechanical friction on the stabilized platform, which could lead to instability in the toolface angle, a novel dead zone torque estimation algorithm is presented. Finally, in comparison with other advanced control methods, simulation results show the superiority and robustness of the proposed scheme under complex disturbances from the downhole environment. And drilling simulation experiment results are provided to validate the robustness and adaptivity of the proposed method.

Autonomous Drilling Approach Uses Rotary Steerable System in Middle East Wells

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 22975, “Novel Approach to Autonomous Drilling Using Rotary Steerable System in Middle Eastern Oil and Gas Wells,” by Victor Oliveira, Saudi Aramco, and Mohammed Elsadig and Ayman Al-Ghazzawi, SPE, SLB, et al. The paper has not been peer reviewed. Copyright 2023 International Petroleum Technology Conference. Reproduced by permission. _ An operator focused on evaluating and validating a novel approach from its major directional-drilling-service provider using the autocurve-drilling mode to drill curved sections automatically without human intervention. The autonomous curve-drilling technology helped minimize human error, enhance accuracy of well positioning, and improve hole quality for drilling and workover operations. Autocurve-Drilling System Rotary-steerable-system (RSS) intelligence has been improving since its introduction. Around 1990, when the RSS method was introduced, communication with RSS tools was only one-way; downlink commands were sent through flow variations, and the response was monitored. For corrections, human intervention was required by sending new downlink commands. Between 2003 and 2013, downhole automation design and analysis for RSS began with the introduction of closed-loop features in which either verticality or target inclination and azimuth in tangent sections can be maintained. The technology evolved to closed-loop responses in these vertical or tangent modes to adjust the trajectory automatically to the target directions within 0.5° in inclination and 2° in azimuth. The next step for automating the downhole trajectory control of RSS was to close-loop the curve section. This once took increased levels of focus from directional drillers to ensure that they continued reviewing directional responses of the RSS in different intervals to update steering requirements, the steering ratio, and toolface commands. This was a good driver for further improvement of RSS intelligence and updates to desired steering-requirement capabilities to automate curve-section directional control. An autocurve autonomous downhole-automation-control system was developed to address the need to automate curved-section directional control. The intelligent algorithm was developed to change the communication process from including surface intervention to use of only a downhole closed-loop system once directional requirements were established. The system moved away from toolface and steering-ratio downlinking commands to dogleg and toolface commands. As a result, the RSS is no longer dependent on surface intervention for steering requirements but adapts to deliver required dogleg-severity (DLS) commands. This process solved the need for surface dependency. Upon acquiring this information, the tool uses the latest-known inclination and azimuth, and programmed rate of penetration, to create pseudochanges in direction and inclination to follow and automatically calculates a steering ratio to follow the required path. This closed-loop design is performed on a continuous basis within 1 second compared with the average 20-minute process cycle that was used conventionally. Furthermore, the design minimizes the command-downlink requirements to the tool, which further enables the automation path.

Inclination Build Rate of a Point-The-Bit Rotary-Steerable Tool

The combination of a rotary-steerable system (RSS) and power screws has become the main drilling tool for the efficient exploration and development of unconventional oil and gas. However, the application of point-the-bit (POB) rotary-steerable tools in directional drilling subjects spindle bearings to continuous alternating stress, which can lead to fatigue failure. In this study, numerical analysis and simulation are employed to guide the engineering application of rotary-steerable tools, particularly in the field of oil and gas exploitation. The structural composition and steering principles of rotary-steerable tools are analyzed. The influence of the structural parameters of the spindle on the deflection capacity of the tool is analyzed, and the optimal structural parameters are selected. Based on the results, the recommended distances between the various components of the tool could be obtained. Moreover, the maximum deflection capacity increases with torque when the WOB (weight-on-bit) is constant. Similarly, at constant torque, the maximum deflection capacity increases with the WOB. Overall, the WOB has a greater influence on the maximum deflection capacity than torque. To ensure the safe operation of the spindle, the WOB and torque must be within 200 kN and 20 kN·m, respectively. The findings of this study provide valuable insights into the safe application of POB rotary-steerable tools.

Attitude Control of Stabilized Platform Based on Deep Deterministic Policy Gradient with Disturbance Observer

A rotary steerable drilling system is an advanced drilling technology, with stabilized platform tool face attitude control being a critical component. Due to a multitude of downhole interference factors, coupled with nonlinearities and uncertainties, challenges arise in model establishment and attitude control. Furthermore, considering that stabilized platform tool face attitude determines the drilling direction of the entire drill bit, the effectiveness of tool face attitude control and nonlinear disturbances, such as friction interference, will directly impact the precision and success of drilling tool guidance. In this study, a mathematical model and a friction model of the stabilized platform are established, and a Disturbance-Observer-Based Deep Deterministic Policy Gradient (DDPG_DOB) control algorithm is proposed to address the friction nonlinearity problem existing in the rotary steering drilling stabilized platform. The numerical simulation results illustrate that the stabilized platform attitude control system based on DDPG_DOB can effectively suppress friction interference, improve non-linear hysteresis, and demonstrate strong anti-interference capability and good robustness.

Attitude Control of Rotary Steering Drilling Stabilized Platform Based on Improved Deep Deterministic Policy Gradient

Summary The rotary steerable drilling system is an advanced drilling technology, with stabilized platform toolface attitude control being a critical component. Due to a multitude of downhole interference factors, coupled with nonlinearities and uncertainties, challenges arise in model establishment and attitude control. Furthermore, considering that stabilized platform toolface attitude determines the drilling direction of the entire drill bit, the effectiveness of toolface attitude control will directly impact the precision and success of drilling tool guidance. In this paper, a mathematical model and a friction model of the stabilized platform are established, and an improved deep deterministic policy gradient (I_DDPG) attitude control method is proposed to address the friction nonlinearity problem existing in the rotary steering drilling stabilized platform. A prioritized experience replay based on temporal difference (TD) error and policy gradient is introduced to improve sample usage, and high similarity samples are pruned to prevent overfitting. Furthermore, SumTree structure is adopted to sort samples for reducing computational effort, and a double critic network is used to alleviate the overestimated value. Numerical simulation results illustrate that the stabilized platform attitude control system based on I_DDPG can achieve high control accuracy with both strong anti-interference capability and good robustness.