In the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain the accurate attitude parameters of the steering drilling tool, a new method for multi-sensor dynamic attitude combined measurement is presented. Firstly, by using a triaxial accelerometer and triaxial magnetometer measurement system, the nonlinear model based on the quaternion is established. Then, an improved adaptive fading square root unscented Kalman filter is proposed for eliminating the vibration disturbance signal. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical unscented Kalman filter (UKF) to avoid the filter divergence caused by the negative definite state covariance matrix. The fading factor is introduced into UKF to adjust the filter gain in real-time and improve the adaptive ability of the algorithm to mutation state. Finally, the computational method of the fading factor is optimized to ensure the self-adaptability of the algorithm and reduce the computational complexity. The results of the laboratory test and the field-drilling data show that the proposed method can filter out the interference noise in the attitude measurement sensor effectively, improve the solution accuracy of attitude parameters of drilling tools in the case of abrupt changes in the measuring environment, and thus ensuring the dynamic stability of the well trajectory.

The high-speed vibration rotation of the drill bit during drilling causes the logging tool to be damaged or distorted, resulting in inaccurate or lost data collection. Traditional prediction methods such as dynamic modeling and geological modeling have problems such as incomplete data and difficult modeling, which cannot meet the accuracy and stability requirements of wellbore trajectory prediction. The long short-term memory neural network (LSTM) for predicting time series can achieve accurate prediction, but there are problems such as difficulty in adjusting the hyperparameters of the LSTM model, slow convergence speed, and easy overfitting. This paper absorbs the advantages of the LSTM algorithm, ridge regression (L2 regularization), and sparrow optimization algorithm (SSA) in machine learning and proposes a well trajectory prediction model of steerable drilling based on L2 regularization and SSA optimized LSTM (L2-SSA-LSTM). The model takes the LSTM hyperparameter as the parameter optimization goal of SSA and adds L2 regularization to the model to prevent model overfitting to complete modeling and prediction. The experiment was conducted using measured data sets from directional drilling in two different oilfields. The results show that compared with the back propagation algorithm (BP), consolidated memory gated recurrent unit (CMGRU), dual-thread gated recurrent unit (DTGRU), Attention-based Spatiotemporal Graph Recurrent Neural Network (ASTG-RNN), LSTM, and the L2-SSA-LSTM prediction model has significantly higher accuracy in predicting directional drilling trajectories than other models and has the better predictive ability.

During drilling, a rotary steerable system (RSS) is affected by vibration, rotation, and other random noises. This paper presents a random weighting adaptive estimation of model errors on attitude measurement for RSS drilling tools. The algorithm is used to estimate the covariance matrix of the dynamic model errors and the state prediction vectors for RSS drilling tools, to reduce the effect of abnormal noises of the dynamic model on the estimation of state parameters. The random weighting estimation of observation’s systematic error and covariance matrices of observation error vectors to control the effect of observation model noises anomaly on state parameter estimation, enhance the use efficiency of the latest observation information, indirectly weaken the impact of model error on state parameter vector, and improve the accuracy of attitude calculation of RSS drilling tools. Experimental results and comparison analysis demonstrate that the proposed algorithm achieves better outcomes than the Kalman filtering and Extended Kalman filtering. The maximum solution error is controlled at 0.15°, and the tool face angle error is less than 3°. It cannot only control the covariance matrix of the observation error vector and predicted residual vector, but it can also effectively resist the disturbances of kinematic model error. It also can improve the dynamic measurement accuracy of attitude parameters, and solve the problem of uncertainty in dynamic attitude measurement of RSS drilling tools.

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.

The jet pipe servo (JPS) valve is one key component, whose dynamic performance directly influences the aircraft’s maneuverability. In this paper, a more accurate mathematical model and a novel multiobjective hierarchical encourage particle swarm optimization genetic algorithm (HEPGA) are proposed to improve the dynamic performance of the jet pipe servo valve. By optimizing the main structure parameters of the jet pipe servo valve, the adjustment and overshoot in the dynamic performance are reduced by 24.28% and 51.39%, respectively, compared with the prototype before optimization. To obtain a more accurate mathematical model, the computational fluid dynamics (CFD) is introduced to modify the analytical model considering the turbulent submerged free jet. Different from conventional numerical simulation, the dynamic mesh technique is used to analyze the flow field distribution by considering the force interaction of various parts of the jet pipe servo valve under actual working condition. Then, the HEPGA with better convergence is utilized because of the conflict of adjustment and overshoot. This proposed hybrid algorithm introduces the concept of staff welfare system to divide the population into elite individuals and excellent individuals of particle swarm optimization and general individuals of genetic algorithm. Meanwhile, the convergency performance of the HEPGA is evaluated through the Rosenbrock function by comparing with other particle swarm genetic hybrid methods. Subsequently, the experimental platform is constructed and the dynamic performance tests are conducted on the prototype after optimization. The experimental results verify the accuracy of the established mathematical model and the significant improvement of dynamic performance of the jet pipe servo valve.

Due to the federal Kalman filter is used to directly fuse the measurement information into the main filter without processing, resulting in the problem of reduced filtering accuracy. An adaptive weighted federated Kalman filtering based on Mahalanobis distance was proposed in this paper. By calculating the Mahalanobis distance between the predicted value and the measurements of the system, the random fluctuation of the measurements is detected. The statistical characteristics of the system measurement noise are adjusted at any time according to random fluctuations in the measurements. And then by using a adaptive amplification factor to dynamically adjust the measurement noise in the subsystems, and reduce the impact of measurement information contamination in subfilters on the main filter. The adaptive federated information distribution coefficient is used to realize the global information fusion of the federal Kalman filter method, to reduce the influence of inaccurate estimation of subfilters on the main filter.Simulation results and comparison analysis prove that the filtering performance of the proposed is better than the traditional federated Kalman filter (FKF) and adaptive FKF, which can improve the accuracy of the integrated navigation system.