Summary: The economic value of real-time optimization of the drilling process has attracted extensive attention in the period of crude oil price fluctuation. Because of the complicated bottomhole conditions and coupling relationship among subsystems, it is difficult for the driller to continuously adjust the primary manipulation variables—weight on bit (WOB), revolutions per minute (RPM), and managed pressure drilling (MPD) equipment—which leads to unsatisfactory drilling effect and increased nonproductive time. Here, we propose an integrated control strategy based on economic model predictive control with zone tracking (ZoneEMPC), nonlinear model predictive control (NMPC), and Lyapunov-based model predictive control (LMPC) to optimize hydraulic mechanical specific energy (HMSE) while stabilizing bottomhole pressure (BHP). During normal drilling, ZoneEMPC is used to minimize HMSE through operating RPM, WOB, mud pump flow, and choke-valve opening as well as its tracking performance, which ensures that the BHP is maintained within the pressure window. Moreover, the controller is switched to NMPC, which adjusts the BHP to quickly reach the new setting zone. The reason for controller switching is that the tracking accuracy of ZoneEMPC could decline when encountering high-pressure formation with gas invasion. As the BHP reaches the new pressure range, the controller is switched to ZoneEMPC again and the HMSE is reoptimized. In addition, the transmission rate of mud pulse telemetry is not enough to meet the real-time control in practical engineering. Therefore, the LMPC controller is used to ensure the closed-loop stability of the system when the downhole measurement data (e.g., BHP) delayed. This study uses the drilling data of a vertical well located in Tarim, China, to verify the control strategy. The results indicated that the proposed integrated control strategy could improve the rate of penetration (ROP), especially in a formation with high rock strength. Meanwhile, the BHP could be adjusted efficiently and stably under different conditions. This work provides a unified framework integrating multiple system models for practical engineering, which has certain theoretical guiding significance for the automation and intelligent development of the oil and gas industry.

The occurrence of downhole high-frequency torsional oscillations (HFTO) can lead to the significant damage of drilling tools and can adversely affect drilling efficiency. Therefore, establishing a reliable HFTO identification model is crucial. This paper proposes an improved whale algorithm optimization support vector machine (TSWOA-SVM) for accurate HFTO identification. Initially, the population is initialized using Fuch chaotic mapping and a reverse learning strategy to enhance population quality and accelerate the whale optimization algorithm (WOA) convergence. Subsequently, the hyperbolic tangent function is introduced to dynamically adjust the inertia weight coefficient, balancing the global search and local exploration capabilities of WOA. A simulated annealing strategy is incorporated to guide the population in accepting suboptimal solutions with a certain probability, based on the Metropolis criterion and temperature, ensuring the algorithm can escape local optima. Finally, the optimized whale optimization algorithm is applied to enhance the support vector machine, leading to the establishment of the HFTO identification model. Experimental results demonstrate that the TSWOA-SVM model significantly outperforms the genetic algorithm-SVM (GA-SVM), gray wolf algorithm-SVM (GWO-SVM), and whale optimization algorithm-SVM (WOA-SVM) models in HFTO identification, achieving a classification accuracy exceeding 97%. And the 5-fold crossover experiment showed that the TSWOA-SVM model had the highest average accuracy and the smallest accuracy variance. Overall, the non-parametric TSWOA-SVM algorithm effectively mitigates uncertainties introduced by modeling errors and enhances the accuracy and speed of HFTO identification. By integrating advanced optimization techniques, this method minimizes the influence of initial parameter values and balances global exploration with local exploitation. The findings of this study can serve as a practical guide for managing near-bit states and optimizing drilling parameters.

During drilling, the driller cannot continuously and reliably regulate manipulated variables such as surface weight on bit (WOBs), surface revolutions per minute (RPMs), and mud pump flow rate. This results in optimal drilling parameters that cannot be obtained in real time, which will affect drilling safety and efficiency. Therefore, we consider an integrated coupling model to simultaneously describe the response of rate of penetration (ROP) and bottom hole pressure (BHP). With this model, we design an economic model predictive control with zone tracking (ZEMPC) algorithm combining the optimization and control layers to reduce the hydraulic mechanical specific energy (HMSE) while accurately regulating the BHP and ensuring efficient hole cleaning during operation. This algorithm is compared with another hierarchical control structure. The upper layer of this structure implements steady-state optimization for HMSE, and the optimal set value of BHP and ROP is dynamically tracked in the lower layer using nonlinear model predictive control (NMPC). A performance comparison reveals that both control algorithms can track formation pressure with acceptable accuracy, and the concentration of bottom hole cuttings is less than 5% during improving ROP. ZEMPC based on $l_{1}$ norm constraint can better improve the economic performance of key variables by increasing control redundancy and is not affected by the decision cycle. The robustness of the two algorithms is studied in case of errors in mud density and friction parameters. It is found that the ZEMPC based on the $l_{1}$ norm constraint is less sensitive to error when the model is mismatched.

Within the spectrum of complex downhole operational challenges, pipe sticking incidents emerge as one of the most prevalent and costly drilling complications. These incidents characteristically develop through progressive deterioration rather than abrupt failure, with discernible precursor signals typically manifesting as anomalous patterns in critical drilling parameters (torque fluctuations, drag anomalies, deviations in standpipe pressure). Consequently, early detection of these signals plays a pivotal role in mitigating pipe sticking occurrences. To systematically investigate the characteristic signatures pipe sticking events, this study employs two modal decomposition methods to extract salient features from near-bit downhole data. Conventional pipe sticking prediction methodologies exhibit three predominant limitations: rule-based systems suffer from poor generalizability, physics-based models demonstrate low computational efficiency, and data-driven techniques lack physical interpretability. To overcome these constraints, this study innovatively proposes a physically constrained prediction framework that integrates Variational Mode Decomposition (VMD) with near-bit measurement data. Experimental results demonstrate the superior predictive capability of the proposed VMD-based, near-bit data-physical constraint model. Based on a comprehensive evaluation using six benchmark models, the proposed approach achieves optimal performance, with an R² metric of approximately 0.9, significantly outperforming existing algorithms. When deployed in actual drilling operations, this model exhibits robust early detection of pipe sticking precursors, enabling proactive intervention. The practical implementation of this framework facilitates timely corrective actions, thereby substantially reducing the incidence of downhole pipe sticking events and enhancing operational safety.

Considering the stability and safety of industrial production, the open-loop system cannot meet the requirements of industrial production, so more and more attention has been paid to multivariable closed-loop system identification. This study focuses on the identification of a multivariate closed-loop system with spike noise in which the model order of the feedback channel is lower than that of the forward channel. Combining principal component analysis (PCA), which is used to eliminate the correlation between the data matrix elements, and the derivable approximate least absolute criterion function, partial approximate least absolute deviation (PALAD) for multivariable closed-loop system identification is proposed. By introducing a deterministic function to replace the absolute value in partial approximate least absolute deviation, the non-differentiable problem of the least absolute deviation function can be solved, and the identifiability of the multivariable closed-loop system in the case mentioned above is theoretically verified. Simulation experiments show the validity of the PALAD algorithm. Compared with the partial least squares (PLS) method, PALAD can effectively restrain the spike noise that follows an $S\alpha S$ distribution and shows stronger robustness when white and spike noises exist simultaneously.