Reliable front-end pressure-pulse generation is critical to mud-pulse telemetry because waveform distortion introduced at the rotary valve propagates through the telemetry chain and reduces downstream recoverability. This paper targets accurate and computationally tractable control of an intelligent drill-bit rotary valve under actuator limits, parameter drift, and downhole-like disturbances. A control-oriented electromechanical–hydraulic grey-box model is established, and a real-time lexicographic model predictive control (MPC) framework with candidate pre-screening, move blocking, and online correction/compensation is developed and compared with proportional–integral–derivative (PID) control and conventional MPC. Under a sampling period of $T_s=20\mathrm{ms}$, the proposed controller reduces the step-tracking rise time from $2.18\mathrm{s}$ to $1.76\mathrm{s}$ and the steady-state pressure error from $0.1208\mathrm{MPa}$ to $0.0292\mathrm{MPa}$ relative to conventional MPC. In the pulse-output and mismatch–disturbance scenarios, it further maintains lower steady-state pressure error while reducing the cumulative input variation from $51.0$ to $11.5$ and from $121.5$ to $19.5$, respectively. The observed 99th-percentile and worst-case MATLAB workstation execution times remain below one sampling period, while supplementary mismatch–disturbance sensitivity maps indicate a favorable accuracy–timing compromise within the tested numerical envelope. These results support the proposed method as a simulation-validated candidate for low-complexity rotary-valve control and motivate subsequent bench/hardware-in-the-loop (HIL) validation rather than field-qualified deployment claims.

Archived sensor records often lack synchronized reference pressure, making it necessary to distinguish retained device ordering from numerical agreement and response-shape reproduction. Aging-stage and first-retest records from 22 miniature temperature–pressure sensors were evaluated using marginal features, Spearman rank correlation, Lin’s concordance correlation coefficient (CCC), device-level bootstrap intervals, and 56 equally weighted common-temperature bins. A disjoint 16-device same-mode cohort tested recurrence of marginal-feature ordering. The analysis demonstrates the following. The fifth percentile retained device order (0.886) and identity-line agreement (CCC 0.968); standard deviation retained order (0.648) without agreement (CCC 0.032), which the decomposition attributes to systematic displacement (accuracy factor 0.077), whereas the 95th percentile (CCC 0.379) is precision-limited (Pearson r 0.510). Common-temperature alignment reversed the raw-median difference for every device. Relative temperature-slope retention was negligible (rank correlation 0.013), and complete centered-profile correlation was heterogeneous (median 0.356). Lower-tail, upper-tail, and dispersion ordering recurred in the disjoint cohort. The article proposes a multilevel retention signature, an ordinal pattern read across evidence levels, together with candidate retest endpoints. Calibration accuracy, sensor life, sensitivity degradation, retention of metrological characteristics, and causal aging lie beyond this evidence.

Deep hard-rock and geothermal drilling expose polycrystalline diamond compact (PDC) cutter chamfers to coupled thermal shock, abrasive wear, and intermittent impact, which accelerates edge spalling and degrades the quality of on-bit monitoring signals. This bench-scale proof-of-concept study evaluates a surface-gradient architecture that combines shallow cobalt leaching in the chamfer region with a thin silicon carbide (SiC) interlayer and a nanocrystalline diamond topcoat. Commercial 13 mm PDC cutters were treated within a surface-gradient design window of $t_{\mathrm{SiC}}$ = 0–1.0 μm and $L_{\mathrm{deCo}}$ = 0–200 μm, and were examined by cross-sectional microscopy, XPS/ToF-SIMS, Raman stress mapping, scratch adhesion, apparent fracture toughness, laser-flash thermal transport, thermal-shock cycling, 400 °C pin-on-disc wear, instrumented impact loading, bench granite-drilling signal acquisition, and finite-element correlation. The optimized configuration ($t_{\mathrm{SiC}}\approx 0.7\mathsf{\mu }\mathrm{m}$, $t_{\mathrm{D}}\approx 5\mathsf{\mu }\mathrm{m}$, and $L_{\mathrm{deCo}}\approx 100\mathsf{\mu }\mathrm{m}$) reduced the 95th-percentile tensile residual stress at the chamfer from about 0.48 to 0.26 GPa, reached a scratch critical load of about 28 N, compared with about 16 N for the topcoat-only condition and about 25 N for the SiC-plus-topcoat condition, cut high-temperature wear volume by about 40%, and shifted the characteristic spalling energy from about 0.8 to 1.3 J. In bench-scale granite drilling, the same design stabilized frictional response and improved simple pre-spall discrimination metrics, raising ROC-AUC from about 0.65 to 0.87. These bench-scale results provide proof-of-concept evidence that surface-gradient design can improve PDC chamfer durability and signal discriminability, while the proposed signal metrics have yet to be validated under field-scale downhole conditions.

During deep shale gas development, complex in situ stress conditions, abnormal formation pressure, and well-developed bedding planes result in complicated wellbore instability mechanisms. Conventional homogeneous rock models are unable to accurately characterize the different failure behaviors between matrix failure and bedding-plane slip. To address this issue, a Mogi–Coulomb and Jaeger-based multi-mechanism failure assessment model was developed for the Longmaxi shale gas reservoir in the Luzhou Block by integrating logging data, core experiments, and field drilling information. The results show that bedding structures significantly control rock strength and wellbore failure modes. The proposed model effectively identifies different failure mechanisms, including matrix shear failure, bedding slip, and tensile failure. Furthermore, the effects of well inclination, azimuth, and bedding angle on collapse pressure were analyzed, and well azimuth was found to have the greatest influence on the safe drilling window. This study reveals the controlling role of matrix–weak-plane strength contrast in shale wellbore instability and provides theoretical support for well trajectory optimization and drilling parameter design in deep shale gas development.